© 2021 JETIR May 2021, Volume 8, Issue www.jetir.org (ISSN-2349-5162) On Detecting Plant Diseases with Image Processing: A Survey Epsita Medhi Nabamita Deb Department of Information Technology Gauhati University Assam, India Department of Information Technology Gauhati University Assam, India epsitamedhi12@gmail.com deb.nabamita@gmail.com ABSTRACT With the modernization of the agricultural industry, applications of image processing have a considerable impact on this industry Image processing applications have been used in the early and fast detection of crop and plant diseases The early detection of diseases enables the people concern to treat the infected crops or plants which results in ensuring good quality and yield of the crops and plants being raised by the farmers This paper describes the recent studies, techniques, and contributions made by different researchers, to detect plant diseases affecting various plant parts Current image processing trends and techniques like K-Means, Histogram Equalization, Otsu method, Adam Optimizer, etc have been put forward in this survey paper to serve the purpose of identifying and detecting diseases and their pathogens The study also discusses the various diseases and the corresponding image processing applications used by the researchers to identify the various diseases The goal of this paper is to identify the current trends of the applications of image processing methods to recognize various plant diseases Techniques like GLCM, GABOR filter, Local Binary Pattern have been used by researchers for the extraction of the features from the images KMeans, Fuzzy logic, Otsu, etc have been used for segmentation and clustering Some of the survey papers include SIFT, Euclidean, and Autoencoders as classifiers Keywords Gabor filters, Image classification, Image enhancement, Filtering techniques, Segmentation, Image processing, Feature selection INTRODUCTION India being an agricultural country, its highest economic income is based on the agricultural yields that are produced [1] With its proper irrigation facilities, fertile soil, hard labor, India produces large amounts of crops and plantations The agricultural outputs get hampered because of various reasons such as floods, drought, etc but the most important of them all is due to the attack of the pathogens and disease-causing microorganisms To detect the diseases at their onset would be much beneficial for the protection of the crops Image processing techniques help in solving various problems by applying their different algorithms It can also remove unwanted noises and signals A combination of user's data, knowledge, and processed data can help in solving various problems through image processing The automation techniques and tools can be utilized by the farmers for improving the productivity, grade, and yield to make use of smart farming From time to time investigation of the agricultural field along with the early detection of the diseased crops help in the growth of healthy crops along with the profit in the economy Various techniques have been formulated to support this desired result [2] Farmers, agriculturists, horticulturists, etc usually detect the diseases by looking at them And at many times agricultural experts are called upon to validate the same By doing JETIR2105789 this, there comes the factor of heavy costs, and also the constraint of time Through this particular survey paper and its findings, we have tried to show various types of image processing techniques such as feature extraction, segmentation, classification techniques, to name a few to detect plant diseases and show their classifications BASIC STEPS IN IMAGE PROCESSING FOR DETECTING DISEASES CAUSED IN PLANTS Image processing helps in the improvement of accuracy and the consistency of some agricultural practices, thereby helping the farmers to produce good quality yields The automated techniques help in taking quality measures which is sometimes more beneficial than visual decision making [3] The steps involved are discussed as follows At first, images are acquired with the help of a digital camera which can also be a mobile camera Acquired images are divided into two datasets that are healthy and diseased types Healthy plant images are kept so that we can easily compare them with the diseased ones Preprocessing can be performed on the diseased images by applying some techniques like geometric transformation, filtering, brightness correction, etc Region of interest can be calculated with the help of segmentation techniques like thresholding, histogram method, etc Extraction of the features from the selected region of the images can be achieved using independent component analysis, PCA, LBP techniques Symptoms of the diseases, categories of the diseases can be measured or categorized with the help of PCA, filter method, wrapper, and so on Classification of the categorized diseases can be completed with some techniques such as linear regression, KNN, SVM, etc And in the final process images can be identified as well as the diseases can be detected with the use of some techniques such as Fast R- CNN, RCNN, Single Shot Detector, etc The Flow Chart of the abovediscussed processes is given below Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f915 © 2021 JETIR May 2021, Volume 8, Issue MOBILE CAMERA/ DIGITAL CAMERA IMAGE ACQUISITION HEALTHY PLANTS (LEAF/ STEM) DISEASED PLANTS (LEAF/ STEM) GEOMETRIC TRANSFORMATION, FILTERING, BRIGHTNESS CORRECTION, etc THRESHOLDING, EDGE BASED METHOD, HISTOGRAM METHOD, etc PREPROCESSING SEGMENTATION OF ROI INDEPENDENT COMPONENT ANALYSIS, PCA, LBP, GLCM, etc FEATURE EXTRACTION PCA, FILTER METHOD, WRAPPER, EMBEDDED, etc SYMPTOMS/ DISEASE CATEGORY LINEAR REGRESSION, KNN, DECISION TREE, SVM, etc FAST R-CNN, HOG, RCNN, SINGLE SHOT DETECTOR, etc CLASSIFICATION DETECTION OF DISEASES Figure 1: The Steps involved in Detecting and Classifying plant diseases LITERATURE REVIEW There has been extensive work by various researchers which has been included in this survey paper to show the use of the different techniques for classifying and detecting diseases in various parts of the plant Different types of pre-processing techniques, feature extractions, segmentation techniques, classification techniques, clustering techniques, etc have been applied by different Researchers Gavhale et al.[4]in their proposed method used SF-CES for enhancing the image and conversion has been done using the color space method Image segmentation by applying K-Means technique and the feature extraction by using GLCM method have been performed SVM technique has also been implemented It was found that the SF-CES had better color enhancement For disease part extraction, Lab and YCbCr support K-Means clustering Prakash et al.[5]in their proposed method have analyzed and classified different techniques for detecting diseases in plant leaves A color conversion has been done For clustering and segmenting, the K-Means method of clustering has been used Extraction of features has been done using GLCM They have also used an SVM classifier Kernel Hilbert Space has been applied for reducing computational complexities Through their proposed method, they could easily distinguish the healthy and diseased parts of the plant Zhang et al.[6]proposed an intelligent fruit detection system method by improving the Multi-Task Cascaded Convolutional Neural Network (MTCNN) AuRo (Automated Robot) has been working in the real-time and with higher accuracy A procedure for a fusion of the images has been used for improving the performance of the detector The proposed detector worked without any error and had a good time-cost and accuracy Combining negative patches and samples from the dataset, a Fusion Algorithm has been generated by them The technique JETIR2105789 www.jetir.org (ISSN-2349-5162) proposed works well with other convenient objects and methods They used PNet as the convolution layer Dandawate et al[7] helped farmers over internet by developing a Decision Support System (DSS) They have used a Support Vector Machine (SVM) RGB image formats are converted to HSV color space during pre-processing A multi-thresholding technique has been used to extract the ROI OTSU technique and SIFT technique were used for classification and recognition The segmentation technique was based on color and cluster Automatic recognition is done using Scale Variant Feature Waghmare et al.[8] in their proposed method have developed a technique for the detection of leaf diseases They carried out their work by texture analysis of leaf and recognizing the pattern They have performed segmentation after removing the background Different autonomous diseases will have different textures and then the pattern is classified by multiclass type SVM Textures were analyzed using Local Binary Pattern Through their proposed method, identification of diseases can be done in terms of shape, texture, and color The texture is analyzed using Opposite Color Local Binary Pattern Classification performed using SVM Rastogi et al.[9]in their paper have proposed a leaf disease identification technique using a computational scenario Twophase implementation has been done in their proposed method The first phase includes preprocessing, feature extraction Feature extraction by GLCM matrix and the Artificial Neural Network was applied in their system In the second phase, classification has been done using K-Means classifier and feature extraction done in the Region of Interest Grading of the disease has been done K-Means clustering and Euclidean technique have also been applied Their system provides immense help to the farmer for automatically detecting the diseases in the leaves of the plant Luna et al[10]developed an effective solution for automatic disease detection in plants of tomato with a motor-controlled capturing box of images GUI, DCNN were developed together Deep Learning Algorithm with an autoencoder was developed for the classification of images and in learning future tasks The actual results of the recognition of tomato plant detection were found to be successful if the diseases matched the diagnosis of the agriculturists Akhtar et al.[11]in their paper compared various machine learning performances for identifying and classifying leaf diseases OTSU algorithm has been used for selecting the threshold value According to their experiment, the decision tree is the best classifier and the best performance is given by the Discrete Wavelet Transform (DWT) They found that when they combined the DCT and Decision Tree, their system yielded much better accuracy And when DCT, Decision Tree, and SVM were combined their system gave the best performance Their proposed technique was better than the other classification techniques Jhuria et al.[12]in their paper, proposed a detection and classification process for detecting diseases in the plant leaves They considered ANN and Backpropagation method For clustering, the K-Means technique was used Fuzzy logic has been used for the automatic grading of fruit By using their software tool, farmers would be much benefitted and help in checking the agricultural yield from time to time Kulkarni et al.[13]proposed a method to detect early diseases in pomegranate plants CIELAB has been used for segmenting the uniform color scale Here features extraction has been done using GABOR filter and classification has been done using ANN classifier, which gave a better result Khampria et al [14]designed a hybrid approach by combining the Deep Convolutional Neural Networks and Autoencoders to detect diseases in crop leaves Application of Adam Optimizer for accuracy, ReLu for activation function, and Back Propagation for measuring weights can be seen in their experiment The accuracy in the number of epochs changes with different convolutional filter sizes, which resulted in better performance They had a smaller dataset and because of the lack of good GPUs, the training became very large Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f916 © 2021 JETIR May 2021, Volume 8, Issue Le et al [15]used features that were extracted by combining operators of Local Binary Patterns along with the extracted features from plant leaves to distinguish between broadleaf plants Plant contour masks were used for improving the rate of discrimination between the plants comprising of broadleaves For filtering the noise, operators of morphological characters were used Classification is done using SVM The system proposed can distinguish similar types of crops and can also detect weeds Petrellis [16]proposed a similar technique to detect human and plant diseases OCTAVE method has been used in the system Fuzzy logic has been applied for performing segmentation and classification Gaussian Low Pass filter has been employed for MRI scan Classification uses the ANN classifier Supervised clustering in the system yields a better result than unsupervised learning McDermott et al [17]has provided an overview of the methods that were employed for computational predictions of the effectors SIEVE server has been used as a web-based tool The classification has been done using SVM On comparing Sequence Order Independence (SOI) with Sequence Order Dependence (SOD), it was found that SOI is better ROC AUC classification was found to be good Amara et al.[18]in their paper, proposed a deep learning classifier that classifies the predictive performance of unseen diseases of banana leaves LeNet architecture under CNN has been used for image classification OTSU method was used to focus the ROI against the background To identify infected areas, the K-Means clustering technique has been used The model works as a decision support system to identify plant disease Sperschneider et al.[19]predicted an effector in fungi for conserving features of motifs in the sequence of N-terminal EffectorP has been introduced for learning the machine application to predict effectors of fungi The EffectorP has an accuracy of over 80% which helps in the prediction of fungal effector based on Secretomes To predict candidates of the effector with a high-priority, EffectorP gets combined within planta expression for more power Feature selecting strategies applied like Exhaustive, Hill-climbing Greedy searches which are a selection of features correlated provided by WEKA EffectorP predicts effectors of species-specific and core types The biological mechanism can be understood with its help Dheeb et al.[20] in their paper, proposed methods to study, design, implement and evaluate plant leaf disease for its automatic detection and classification For that, they have divided their system into phases Clustering has been done by the K-Means algorithm Texture features were calculated using the Color Cooccurrence Method Neural Network Classifier that was developed performed well and successfully tested the system It was also observed that misclassification mainly occurred in the four classes that they have taken into consideration Sladojevic et al.[21]developed a new method for the recognition of plant leaf disease with DCNN Different color models such as YCbCr, HIS, CIELAB have been used for the study Their model studies the trained network to distinguish features from one another Translation and rotation have been done using Affine Transformation For measuring the weights CaffeNet was applied CNN and ReLus were applied in their system for convolutional and fully connected layers When compared with other models it was found that their model yielded better results Ramesh et al.[22]in their paper, proposed method in which classification cum recognition of diseases in crop leaves has been done Their model removes background noise and segmentation for clustering the diseased portion Clustering using K-Means done to the hue part of HSV Texture features were extracted through GLCM Weights of the nodes were updated by the combination of JOA and DNN iteratively in the hidden layers DNN_JOA classifier was found better than to rest of the classifiers and it achieved the highest accuracies Li et al.[23]in their paper, proposed video detection architecture for detecting real-time crop diseases along with pests They have proposed custom CNN, Fast-RCNN, R-CNN, Faster-RCNN for JETIR2105789 www.jetir.org (ISSN-2349-5162) detecting objects They have also proposed a custom DCNN backbone for Faster-RCNN In their model, they have used the still-images detection metrics related to True Positive and Negative, etc for calculating the lesion spots For converging images and videos Stochastic Gradient Descent (SGD) becomes handy DCNN backbone develops a good result in comparison to VGG, ResNet-50, ResNet-101 Detection from videos caused some problems like Video Defocus with Part Occlusion and Motion Blur Lesion spot shape was found to be always irregular Mohanty et al.[24]developed a model based on a smartphone that helps in assisting the disease diagnosis They used Stochastic Gradient Descent as parameters for performance Alex Net, Google Net were newly designed by them and found that Google Net performed better than Alex Net and segmentation showed variation in performance It was found that the model performed better for the color version of the dataset Dey et al.[25]in their paper, proposed a technique in detecting pests on leaves of the various plants K-Means technique has been used in their model GLCM, GLRLM techniques have been applied to extract the features Their proposed method uses some pre-processing tasks such as Noise Removal, Image Contrast Improvement, etc From their experiment, it was found that the SVM classifier achieved the highest accuracy Keh [26]through his proposed model, investigated the classification of pathology problems with single leaf images They developed a new Efficient Net model Accuracy measured using Adam Optimizer The losses in models were combined to compensate for the shortcomings It was found that their new model Efficient Net performed better than the other models It was also found that if Efficient Net and Noisy Student Training were combined then the system gave a much better result Chohan et al.[27]in their model, proposed CNN architecture for detecting diseases in plants Augmentation increases the dataset size Feature extraction has been done on horizontal edges, vertical edges, RGB images From their experimental model, it was found that CNN predicted the diseases correctly on plant leaf images Minaee et al.[28]provided comprehensive literature review for segmentation, including convolutional networks They covered literature for the segmentation of images and discussed Segmentation methods based on deep learning In the case of survey papers, as mentioned by them, various techniques such as AlexNet, VGGNet, ResNet, GoogleNet, MobileNet, and DenseNet have been applied LSTM (Long Short Term Memory) has been used by them They provided different aspects for the future which would be challenging for segmentation, based on deep learning Lin et al.[29]proposed a new model for segmentation of semantic criteria concerning CNN for detecting powdery mildew disease, at the pixel points, in the leaves of the cucumber Image augmentation technique and custom loss function along with its layer of batch normalization have been added with the convolutional layers OTSU method was used for masking ADAM method for optimization and Glorot initialization method for initializing the weights were used by them Their proposed method for clustering was better than K-Means They found that their proposed method in terms of Precision was not good, but in terms of Recall, it was found to be good Their method could be easily implemented and was rather cheap They also found that along with the diseased region, some non-diseased regions have been identified as diseased regions Iqbal et al.[30]in this survey paper showed the different classification and detection of disease techniques in the leaves of a citrus plant They surveyed different terms and techniques that have been used by different researchers for carrying out their research work such as Otsu Thresholding, Edge Detection, Compression Based Methods, etc It also discussed the importance of extraction of features along with deep learningbased methods They proposed efficiency improvement in terms of color space, YCbCr, images, along with the K-Means clustering algorithm Texture calculated using GLCM For Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f917 © 2021 JETIR May 2021, Volume 8, Issue classification, the backpropagation technique was considered The papers that were surveyed by them showed that none of the researchers used saliency-based techniques which works well with the detection of diseases FS and DL methods achieved high accuracy and perfect classification time Raufa et al.[31]through their method included four main processes such as dataset enhancement, segmentation of the lesion spot, feature extraction from the region infected, and classification of the infected region Filtering of the images done through Top-hat processes and for improving the contrast, Gaussian function has been applied Images that were enhanced are then mapped to the saliency graph Geometric features, color, textures were extracted using Entropy, PCA, Skewers The tophat process was applied to the original images and Gaussian Function was applied for better contrast Finally, the classification being performed with each instance of images in correspondence to classes of each disease Identification of diseases with naked eyes becomes a negative factor because of weather and light Thomas et al [32]in their paper have checked Phenotyping as a narrow observation for cultivars development This study provides a better system of hyperspectral phenotyping, that combines measurements of canopy scale with the environment of higher spatial resolutions Symptoms of powdery mildew were detected by combining Simplex Volume Maximization and SVM Savitzky-Golay filter has been used for smoothening of the normalized images Under artificial light, hyperspectral images acquired superior quality than that of images acquired under natural light Pugoy et al.[33]proposed disease detection system in the leaves of rice crop with images having color analyzer A region including the outlier obtained with histogram intersection between healthy and test images of the leaves of rice For clustering, K-Means was applied and from that, the diseases can be determined Their system works on Bhatia's algorithm where pixels are selected and clustered using Euclidean distance Their proposed system can inspect the rice leaf diseases by comparing and matching colors Sujatha et al.[34]have classified citrus leaf disease using both the methods of deep learning and machine learning to check which methods will be most feasible They applied cross-validation with 10-fold for classification problems and each of the fold consists of the same ratio as that of each class of target Image prediction is done when input is given Methods of Random Sampling and Cross-Validation have been done to train and test the system They used Squeeze Net (SN) for the embedding of images For image classification SVM was being considered by them For www.jetir.org (ISSN-2349-5162) optimizing an objective function, they used Stochastic Gradient Descent They have considered 10 Random Forest trees that can replicate and control growth Inception V3 has been used to increase the accuracy and reducing overfitting for fully connected layers VGG-16 was also studied for padding, max pooling VG19 has been used for building small-sized convolution, for deep neural networks On comparing the works of DL with ML it was found that the techniques of Deep Learning worked better than that of Machine Learning Xiao et al [35] developed a technique for recognizing images of disease in strawberry plants They have considered CNN techniques and a ResNet50 model to detect leaf blight Average training accuracy and loss rates were calculated for the original and feature image datasets They have worked with VGG-16, GoogleNet, ResNet-50 and have compared their accuracy with each one With their method, the diseases of the strawberry plant were properly detected TABLE 1: SUMMARY OF THE LITERATURE REVIEW PAPERS: SL REFERE NCE N O YEAR 2014 Gavhale [4] Prakash [5] JETIR2105789 2017 SUMMARY DATASET METHODOLOGY RESULTS/ VISUALIZATION/ ACCURACY ADVANTAGES/ DISADVANTAGES Development of a model to detect diseases in leaf through image analysis along with classification techniques Total samples: 250 images SF-CES to enhance the image and converting color space, K-Means for segmentation, GLCM for extracting features, along with it, classification have been done SF-CESs color enhancement was found to be better The genuine Acceptance Rate was better than the others Implementati on of a Total samples: The contrast of the images is constant when the value is Energy range between and 1, homogeneity between and 1, a correlation between and -1 are set Different rates of acceptances have been calculated Features are extracted that include Classes: Canker, Anthracnos e To determine chromaticity and the Proposed method for citrus leaf detection Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f918 © 2021 JETIR May 2021, Volume 8, Issue Zhang [6] 2017 technique for analyzing images and classifying them to detect diseases in the leaves Leaves of citrus: 60 collected images Design of an intelligent fruit detection system (InFD) by improving the Multitask Cascaded Neural Network (MTCNN) to FruitMTCNN (FMTCNN) Total samples: 1800 images Classes: Diseased 35 images, Healthy 25 images Classes: Apple, Strawberry, Oranges Dandawate[7] 2015 Proposed a tool for detection of diseases in soybean plants The tool helped farmers over the internet by developing a Decision Support System (DSS) Total samples: 120 images Waghmare [8] 2016 Proposed a model for identifying plant diseases by recognizing the texture and pattern of the leaves of a plant Total samples: 450 images Proposal for the development of a computationa l method that would help in the disease identification Classes: Leaf Scorch, Leaf Spot found in Maple and Hydrangea Rastogi [9] JETIR2105789 2015 Classes: Grape diseases: Black Rot, Downy Mildew www.jetir.org (ISSN-2349-5162) luminosity layers, images in RGB format converted to L*a*b K-Means for clustering, GLCM for extracting the features, SVM for classifying the images For reducing computational complexities, Kernel Hilbert Space (RKHS) has been used Artificial image samples were generated using the Fusion Algorithm by adding negative patches from samples Feature extraction done using PNet architecture of CNN layers, false candidates are removed by RNet and ONet The loss function is calculated by dividing it into two parts: Fruit Classification and Bounding Box Regression As the images are sent to the central system, the normal and the diseased leaves have been classified on features that were extracted The images in RGB format converted to HSV, segmentation using Otsu method, recognition of plant done through Scale Invariant Feature Transform Classification and extraction of features done through SVM Background and unwanted images are removed using segmentation, textures are analyzed with the help of Local Binary Pattern with Opposite Color, classification is done using SVM Feature extraction using GLCM matrix, classification using ANN, Segmentation using K-Means method Toolbox of Fuzzy Logic used comprising of Grading and homogeneity, energy, correlation, etc Classifier’s performance compared by implementing actual cum predicted values worked well AuRo worked in realtime with higher accuracy, the augmentation method was improved, groups of negative patches were generated Using Fusion Augmentation, the true positive rate can be improved The system worked properly in terms of accuracy and time cost It can detect any fruit Leaf’s shape transformed via Scale Invariant Feature Transform SVM classifier gives better accuracy Classification accuracy also improves It is found that the sample leaf generated from the graph is the exact match of the referenced leaf The accuracy achieved is 98.42% SIFT algorithm helps in matching species of the plant with leaves Accuracy has been achieved with SVM multiclass classifier On increasing the training and testing ratios, accuracy also increases The accuracy achieved is 96.66% Automated Decision Support System proved to be advantageous Different types of the cluster are generated on loading the system such as Grayscale cluster, Cluster of the leaf that are segmented, Clustering portion of diseases, and The proposed method yields a good result Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f919 © 2021 JETIR May 2021, Volume 8, Issue in leaves 10 11 de Luna [10] 2018 Akhtar [11] 2013 Jhuria [12] 2013 Kulkarni [13] Khamparia [14] JETIR2105789 2012 2019 Development of an automatic solution for disease detection by a motorcontrolled capturing device of image Total samples: 4923 images Performance comparison of various techniques of Machine Learning to classify, identify diseases in leaves Total samples: 40 images Development of a tool to monitor the diseases in fruits Total samples: 92 Early detection of diseases in pomegranate plants Total samples140 Design of a hybrid approach for detection of diseases in leaves by Classes: Diamante Max, a breed of Tomato Classes: Black Spots, Anthracnos e Classes: Black Rot and Powdery Mildew of Grapes and Apple Scab and Rot of Apples Classes: Alterneria-8 images, BBD-26 images, Anthracnos e – 89 images Total samples 900 Classes: Potato: www.jetir.org (ISSN-2349-5162) calculation of the infection’s percentage The network is trained with different layers such as max-pooling, dropout, convolutional layers Data annotation was applied to avoid overfitting AlexNet architecture was modified Anomaly detection and disease recognition done for epochs of 50 with FRCNN Grayscale thresholding for segmentation, tiny dots removed using open and fill operators, threshold value selected using Otsu method Feature extraction using Statistical features, Discrete Cosine Transform, and Wavelet Transform Classification is done using a Decision Tree Images are classified based on color, texture, and morphology, texture feature using Daubechies 2-D wavelet packet decomposition, backpropagation method used for recurrent networks, K-Means clustering has been used Square Error Concept was used for training, Fuzzy logic for automatic grading Segmentation using CIELAB, Filtering using Gabor filter, Classification using ANN classifier background of input image High dimensional feature vectors at multiple levels Autoencoders for learning encodings Adam optimizer for an On adjusting SGD using 50 epochs, the confidence score acquired a good result Recognition accuracy was found to be better For 36 samples, the accuracy was good The accuracy achieved is 91.6% The proposed tool worked with good accuracy DCT and Decision Tree on combining gives good accuracy DCT, DWT, SVM on combination gives the best accuracy The accuracy achieved is 94.45% The proposed techniques worked with good results For a different percentage of diseases, different grading has been found out Grading has been done by considering the ratio between the area of diseases and the entire area of the leaf The proposed technique will give good results and benefits to the farmers Optimum network efficiency and optimum termination error rate vs neural network efficiency have been achieved The accuracy achieved is 91% The proposed system yields a better result Different sizes of filters and epochs have variations in frequency levels with different accuracies The proposed system yielded better results but GPU was small and so was the database Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f920 © 2021 JETIR May 2021, Volume 8, Issue combining DCNN and Autoencoder s 12 13 Le [15] 2020 Petrellis[16] 2018 Identification of similar types of leaves specially broadleaves and to improve the discriminatio n rate using Plant Contour Masks Similarity techniques between the detection of human and plant diseases have been proposed 14 McDermott [17] 2011 Prediction of secreted effectors that reflects biologically relevant features for recognition 15 Amara [18] 2017 A deep learning approach that classifies the predictive performance of unseen diseases of banana leaves 16 Sperschneider [19] 2015 Development of an Effector in Fungi for conserving features such as N-terminal sequence motifs JETIR2105789 Early blight, Late Blight, Tomato: Leaf mound, Yellow Leaf Curl, Maize: Rust disease Total samples – 15000 Classes: Canola – 7500 images and Radish – 7500 images Total training samples 2500 Classes: Images of Orange fruits and Human skin diseases Classes: Type III and Type IV gramnegative bacteria Classes: 1643 healthy images Images of Banana diseases: Black Sigatoka240, Banana speckle1817 Manually developed secreted proteins – 1922 samples Predicting Effector candidates – 144 samples www.jetir.org (ISSN-2349-5162) increasing accuracy ReLu has been used as the activation function Backpropagation algorithm for weights training Local Binary Pattern(LBP) for extraction of features, classification with (SVM) k-FLBPC method for combining LBP with Contour Mask removes noise 51 features are calculated classes are optimized to obtain classification accuracy The accuracy achieved is 98.63% Similar Types of crops and weeds are classified and realtime weed detection is capable Dark spots are considered using the Octave method Segmentation and classification using fuzzy logic MRI scan using Gaussian low pass filter, classification using ANN classifier The accuracy of supervised clustering is much higher than unsupervised clustering which is 92% Accuracy remains high on using image processing techniques Classification of proteins using SVM, accessing using SIEVE server Area of AUC having the curve under ROC, Sequence Order Independence (SOI) have been calculated Sequence Order Independence (SOI) worked better The signal of the classes of the effector has a loosely defined motif Experimental results found that color information is very much important for disease identification Precision, F1-score, Accuracy, Recall are combined and the evaluation processed good results RGB format converted as HIS format for better performance ROC AUC of Type IV effectors was extremely good SIEVE and Effector had a similar ratio to SOI and SOD Experimentally, effectors of 58 fungi were found from species of 16 fungi with a positive sequence set EffectorP in combination with the expression of planta achieves candidates of the effector with higher priority EffectorP helps in predicting speciesspecific and core effectors Mechanisms for biologically leveled effectors worked well with all species LeNet architecture under CNN, distinguishing background pixel using Otsu method, clustering using KMeans have been used Feature vectors calculated using pepstats, frequencies of Amino Acids, Molecular length and weight of the sequence, and protein net charge Feature selection strategy using Greedy HillClimbing Search and Exhaustive The model provides decision support for the identification of plant diseases by farmers Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f921 © 2021 JETIR May 2021, Volume 8, Issue 17 18 Al Bashish[20] 2011 Sladojevic[21] 2016 A proposed method to study, design, implement and evaluate plant leaf disease for its automatic detection and classification Sample images: Developed a method to recognize plant leaf disease with DCNN Total classes: 15 out of which diseased leaves – 13 Leaves collected from the area of AlGhor present in Jordan Images for training 30880 Images for validation 2589 Classes: 19 20 Ramesh[22] Li [23] JETIR2105789 2020 2020 Proposed a method for paddy leaf disease recognition and disease classification along with optimized deep neuralbased network Their main aim was to remove the background noise and clustering the diseased portion Proposed video detecting architecture for detection of diseases in crops Peach, Powdery Mildew, Apple, Grapevine, and Wilt Total samples 650 images Classes: Bacterial Blight images: 125, Normal plant images: 95, Blast images: 170, Brown Spot images: 150, Sheath rot images:110 Classes: Rice having stem border images: 1760 images, www.jetir.org (ISSN-2349-5162) Search EffectorP has been trained using SIGNAL 4.1 as the initial predictor for the whole secretomes RGB images are converted to color space, K-Means for segmentation, features of textures extracted by Color Co-occurrence Method SGDM matrices generated for H and S Extracted features passed through a neural network for recognition Detection of plant disease was achieved by extracting the shape features method Augmentation for increasing the dataset and reduce overfitting, translation using Affine transformation, convolutional and fully connected layers using CNN and ReLus For color models YCbCr, HIS and CIELAB have been used for the study On comparing the models M1, M2, M3, it was found that M3 emerged to be the best model Neural Network classifier worked well and successfully detects the diseases Accuracy of precision 93% Elimination of intensity reduces variations in the intensity Misclassification occurred for Late Scorch, Cottony Mold, Tiny Whiteness, and also for the Normal leaves 10-fold crossvalidation technique evaluated a predictive model and repeated it after every thousand training iterations The network was finely tuned to fit the plant leaves database Good accuracy achieved after 100th iterations The accuracy achieved is 96.3% Diseased and healthy leaves were distinguished correctly by the proposed system Image background removed using HUE values, RGB model converted to HSV model, clustering using K- means done on the HUE part, the normal and diseased portion is differentiated using a threshold value GLCM helps in extracting color cum texture features For iteratively updating the weights of the nodes a combination of JOA and DNN has been used Blast disease achieved higher accuracy when combined with the classifier called DNN_JOA Confusion matrices predict the true values of positive, negative, and false values of positive, negative DAE, ANN, DNN compares the results that are experimentally found It was found that the DNN_JOA classifier was better than the other classifiers Lesion spots with a heavy infestation of diseases have been considered The stillimages detection metrics related to True Positive and It is found that when the learning rate decreases, the iterations also decrease Detection speed limits to 0.1s per frame Precision The proposed system showed a better result Detection from images causes some problems that include Part Occlusion, Motion Blur, Video Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f922 © 2021 JETIR May 2021, Volume 8, Issue 21 22 23 Mohanty[24] Dey [25] Keh[26] JETIR2105789 2016 2016 2020 occurred during realtime along with pests Frame extraction modules, still-image detector, and video synthesizer are part of the system A custom DCNN backbone has been proposed for FasterRCNN Rice Brown spot images: 1760 images, Rice of Sheath Blight's images: 1800 Development of a smartphoneassisted system for disease diagnosis in plants AlexNet and GoogleNet have been modified Total samples: Design of a system to detect pests, like White Flies, present on the plant leaves Different classifiers were studied to see the differences between healthy and diseased leaves Investigated the classification of pathology problem with single leaf image A new EfficientNet related model with the training for the noisy background was introduced Total videos 15, 23, 13 54,306 images Classes: 14 crop species, 26 diseases Images collected from the Plant Village dataset Total samples: 200 images 125 affected and 75 normal images Total samples: 1820 images of apple leaves www.jetir.org (ISSN-2349-5162) Negative, etc for calculating the lesion spots For videos, it is seen how many boxed lesions are correct They are the extraction of Frame module, synthesizer for Video, detectors for Still Images Detectors with still images work with ResNet-50, VGG16, as well as ResNet101, and for backbone, the architecture of DCNN was designed For proposed DCNN, ReLus layer was put between two convolutional layers Convergence done using SGD Different sizes of convolution layers have been considered GoogleNet used layers and hyperparameters such as Stochastic Gradient Descent (SGD) Variations in color, gray-scale, and segmentation can be seen between AlexNet and GoogleNet value using VGG16 and DCNN achieved the highest accuracy Defocus Lesion spot’s shape is always irregular From the model, the types of crop species and diseases can be found out GoogleNet worked better than AlexNet Accuracy between 85.53% to 99.34% The model performed better for color measurement Segmentation is done with K-Means technique, extraction of features with the help of GLCM cum GLRLM The classification was done with the help of SVM, ANN Different classifiers such as Binary with Decision Tree, Bayesian, K-NN have been compared Accuracy with its highest value achieved by SVM, Radial Basis with Function kernel Performance testing for trained classifiers is done which was not present in the training set Accuracy of SVM 98.4% Among the classifiers, the SVM gave the best performance Embossing the image with 0.5 probability, sharpening and applying blur on the images The loss function is used in cross-entropy EfficientNet and Semi-Supervised Noisy student training has been compared Adam Optimizer has been applied EfficientNet performed better than the other models The weights from the Noisy Student Training achieved good accuracy Accuracy is higher than other classifiers EfficientNet and Noisy Student Training when combined yields better results Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f923 © 2021 JETIR May 2021, Volume 8, Issue 24 Chohan [27] 2020 Development of a CNN architecture for detection of plant diseases Total samples: 56236 images Classes: tomato, apple, raspberry, soybean, squash 25 Minaee[28] 2020 A survey has been done on the literature review structure on the works of semantic and instancelevel segmentation More than 100 deeplearning and segmentation methods that were proposed until 2019 have been discussed Dataset: PASCAL VOC 2012, MS COCO, Cityscapes, and ADE20k 26 Lin[29] 2019 Development of a new deep learning scheme for masking semantic segmentation models based on CNN at the pixel level Total samples: 50 images of Powdery Mildew 27 Iqbal [30] 2018 Review of different methods for detecting diseases and classification in citrus plants Detail taxonomy has been described Classes: Citrus diseases such as Canker, Blackspot, Citrus Scab, Melanoses, Greening, and Anthracnos e JETIR2105789 www.jetir.org (ISSN-2349-5162) Augmentation for increasing the weights of database, extraction of features using CNN architecture, reducing sizes of the image using Pooling, and scaling the data Batch Normalization has been used CNN architecture used by researchers in the survey paper is AlexNet, VGGNet, ResNet, GoogleNet, MobileNet, and DenseNet Encoder decoder models for image translation and sequence to sequence model in NLP, GANs FCN, U-Net, and V-Net for segmentation Markov, Conditional Random fields, are used as DL architectures RGB segmentation, Stacked DE convolutional Network, LinkNet, etc have been used by some Some of them even used augmentation methods for increasing the number of labeled samples Image transformed into HSV color space, extraction of S channel, masking using Otsu method For obtaining black background, RGB images were masked U-Net is used as CNN architecture Data augmentation has been done Adam method used for optimization For initializing the weights, the Glorot method was used Challenges such as noisy background, best ROI, changes in the part of a light, low-intensity values, and a huge number of correlated redundancies have been studied Different preprocessing techniques, The model classified a maximum number of images accurately Horizontal and vertical edges, RGB values extracted from CNN The model used fully connected layers for prediction The accuracy achieved is 95% CNN was used for correctly predicting the diseases of plant leaf images Through this survey paper, one can learn about the segmentation problems and algorithms proposed till 2019 Different segmentation algorithms have been reviewed including deep learning, training the data, choice of architectural network, functional loss, strategies for training 20 popular image segmentation dataset has been overviewed Based on the properties and performance of the reviewed methods, a comparative study has been made The authors have surveyed more than 100 recent segmentation algorithms and grouped them into 10 categories like Adversarial, Generative, FCN, CNN models, etc On adding U-Net with batch normalization, and convolutional kernel, the training process gets accelerated The model achieved satisfactory segmentation Edges found under the proposed model as well as clustering were smoother than K-Means On higher pixel accuracy, the healthy region predicted accurately Efficiency improvement in the terms color space, YCbCr, images, along with K-Means clustering algorithm Color, texture features using GLCM method For classification, a backpropagation neural network was Easy implementation of the proposed model Shape, as well as the required area, can be provided for the diseased region In terms of Recall, the model worked better None of the researchers in their survey used saliencybased techniques which works well for disease detection FS and DL methods achieved high accuracy and perfect classification time Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f924 © 2021 JETIR May 2021, Volume 8, Issue www.jetir.org (ISSN-2349-5162) segmentation techniques, feature extraction methods, and classification methods have been surveyed 28 Rauf [31] 2019 Development of an image processing tool for recognition of both healthy and diseased citrus fruits and leaves Total images: 759 images Classes: Black Spot, Canker, Scab, Greening, Melanoses 29 Thomas [32] 2018 Proposal of a hyperspectral phenotyping system within a controlled measurement environment Classes: Powdery mildew of Barley cultivars 30 Pugoy [33] 2011 Proposal for development of automatic detection of disease tool to detect diseases in the rice plants Classes: Brown spot and Leaf Scald of rice crop 31 Sujatha [34] 2020 Performances of Deep Learning (DL) and Machine Learning Total samples: 609 images JETIR2105789 Classes: The hyperspectral sequence of the disease can be tracked; different species of citrus plants are compared Images were resized, the processing is done using Top Hat process, Gaussian function for better contrast, enhancement using saliency map Feature extraction was done using Skewness, PCA, and Entropy methods Light in the image is measured in the area of the electromagnetic spectrum Inside the greenhouse setup, measurement based on the canopy generates higher throughput when combined inside the controlled setup of a laboratory Smoothening is done by applying the Savitzky-Golay filter Preprocessed images with Simplex Volume maximization are implemented for Hyperspectral measures SVM is applied for determining nonlinear discriminant function Hue Saturation (HS) Histogram is extracted from healthy leaf images Colors are extracted using K-Means, ROI segmented out from the background, pixels are selected using Bhatia’s algorithm, and clustered using Euclidean distance For multiclass labels, class labels were classified Testing, training is done on Random Sampling, k-fold offering cross- used The accuracies were measured between correct and incorrect samples AUC has been used to see the rank of a classifier Infected images were divided into classes Infected images highlighted using lesion segmentation Features selected visually Weather and light affect the data when images are visualized with the naked eye The proposed system achieved greater accuracy and also improved the efficiency of the system The accuracy achieved is 94.83% During the analysis of data, various conditions of light falling on the canopies proved to be somewhat challenging Artificial light proved to be better than natural light The likeliness of Brown Spot was lower than the Leaf Scald in the first test In the second test, the Brown Spot was higher than the Leaf Scald Histogram was found to be useful The system can inspect the rice leaf diseases by comparing and matching colors Confusion matrix calculated for class labels Insignificant values are Melanoses that are predicted, Black Spot with On comparing, Deep Learning techniques worked better than Machine Learning Techniques Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f925 © 2021 JETIR May 2021, Volume 8, Issue Xiao [35] 2021 ACCURACIES TECHNIQUES Black Spot images: 171, Canker images: 163, Greening images: 204, Melanoses images: 13, healthy images of plants: 58 Development of a technique for recognizing images of diseases Total samples: 792 images OF Classes: Leaf Blight, Gray Mold, Powdery Mildew DIFFERENT It is seen that; Researchers have applied various image processing techniques to identify and classify different diseases found in the body of plants On implementing the processes, it can be seen that different techniques yielded different sorts of accuracies And it has been found that their accuracies not have many differences The following graph depicts the different types of techniques used by researchers and the calculated accuracies in percentage validation methods Embedding using Squeeze Net, classification using SVM, optimizing using Stochastic Gradient Descent, calculations of regression loss, functions of Squared Hinge with various parameters, 10 Random Forest Trees were considered for replication and control growth, Inception V3 for reducing the overfitting and increasing accuracy, VGG-16 for padding, VGG-19 for Deep Neural Networks Calculating the result using Confusion Matrix using GoogleNet, features images trimmed manually, CNN based on GoogleNet, VGG-16, ResNet-50 was used Average training accuracy and Loss Rate calculated Training accuracy increases with the increment of the training period COMPARED ACCURACIES IN PERCENTAGE 32 (ML) have been compared www.jetir.org (ISSN-2349-5162) Actual class, values of Canker, Predicted Healthy values have that have little percentage of impact In Melanoses classes that are Actual, all predicted values of classes not have an impact Predicted Canker Actual classes have little significant values In Actual Greening, Melanoses and Canker had little or no impact whereas Blackspot and Greening had high impact Healthy classes had no impact The classification rate was found to be accurate, different classes were detected using different models The Gray model was found to be the best in classification rate with VGG-16 Powdery Mildew was found to be the best with GoogleNet The system worked properly with a correct prediction 98.4 100 98.42 96.3 96.3 98 95 94.5 96 92 91.6 94 91 92 90 88 86 SEGMENTATION, FEATURE EXTRACTION, CLASSIFICATION Figure 1: Graph depicting Segmentation, Feature Extraction, and Classification techniques along with their accuracies in percentage JETIR2105789 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f926 © 2021 JETIR May 2021, Volume 8, Issue www.jetir.org (ISSN-2349-5162) COLLECTED IMAGES OF DISEASES PRESENT IN BANANA Conference on Intelligent Sustainable Systems (ICISS) (Dec 2017) 103-109 IEEE DOI= 10.1109/ISS1.2017.8389326 The following images are of Banana leaves and stem that had been collected from a village present in Assam The images show different diseases which have occurred in the Banana plant [3] Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A and Menaka, R 2020 Attention embedded residual CNN for disease detection in tomato leaves Applied Soft Computing, 86, 105933 https://doi.org/10.1016/j.asoc.2019.105933 [4] Gavhale, K R., Gawande, U., and Hajari, K O 2014 Unhealthy region of citrus leaf detection using image processing techniques In International Conference for Convergence for Technology2014 (April 2014) 1-6 IEEE DOI= 10.1109/I2CT.2014.7092035 (a) (b) [5] Prakash, R M., Saraswathy, G P., Ramalakshmi, G., Mangaleswari, K H and Kaviya, T 2017 Detection of leaf diseases and classification using digital image processing In 2017 international conference on innovations in information, embedded and communication systems (ICIIECS) (Mar 2017) 1-4 IEEE DOI= 10.1109/ICIIECS.2017.8275915 [6] Zhang, L., Gui, G., Khattak, A M., Wang, M., Gao, W and Jia, J 2019 Multi-task cascaded convolutional networks based intelligent fruit detection for designing automated robot IEEE Access, 7, 56028-56038 DOI= 10.1109/ACCESS.2019.2899940 (c) (d) Figure 2: (a) Fusarium wilt, (b) Sigatoka disease, (c) Pseudo stem splitting, (d) Black spot diseases CONCLUSION Various tools and techniques have been developed or invented by Researchers worldwide Agriculture, horticulture have been improved to a large extent because plant diseases can be detected at their very onset But in a country like India, farmers still to a large extent depends upon the agricultural experts to look into the various problems of crops and plants So to develop an automated system at a cheaper cost would be very much beneficial for the farmers and towards the economy of a country For the detection of diseases, Image Processing based approaches help to fulfill the desired output of features and to work upon the desired specific techniques This survey paper shows some valuable techniques and algorithms related to Preprocessing, Segmentation, Clustering, Classification, and other techniques of Image Processing that would be beneficial in detecting diseases of plants For future work, we would try to develop some techniques using the surveyed Image Processing techniques and try to develop an automated disease detection on some indigenous plants found in the specific region of India REFERENCES [1] Kumar, S S and Raghavendra, B K 2019 Diseases detection of various plant leaf using image processing techniques: A review In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), (Mar 2019) 313-316 IEEE DOI= 10.1109/ICACCS.2019.8728325 [7] Y Dandawate and R Kokare, "An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective," 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, India, 2015, 794-799, DOI= 10.1109/ICACCI.2015.7275707 [8] H Waghmare, R Kokare and Y Dandawate, "Detection and classification of diseases of Grape plant using opposite colour Local Binary Pattern feature and machine learning for automated Decision Support System," 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2016, 513-518, DOI= 10.1109/SPIN.2016.7566749 [9] Rastogi, A., Arora, R and Sharma, S 2015 Leaf disease detection and grading using computer vision technology & fuzzy logic In 2015 2nd international conference on signal processing and integrated networks (SPIN) (Feb 2015) 500-505 IEEE DOI=10.1109/SPIN.2015.7095350 [10] De Luna, R G., Dadios, E P and Bandala, A A 2018 Automated image capturing system for deep learning-based tomato plant leaf disease detection and recognition In TENCON 2018-2018 IEEE Region 10 Conference (Oct 2018) 1414-1419 IEEE DOI= 10.1109/TENCON.2018.8650088 [11] Akhtar, A., Khanum, A., Khan, S A and Shaukat, A 2013 Automated plant disease analysis (APDA): performance comparison of machine learning techniques In 2013 11th International Conference on Frontiers of Information Technology (Dec 2013) 60-65 IEEE DOI= 10.1109/FIT.2013.19 [12] Jhuria, M., Kumar, A and Borse, R 2013 Image processing for smart farming: Detection of disease and fruit grading In 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013) (Dec 2013) 521-526 IEEE DOI= 10.1109/ICIIP.2013.6707647 [13] Kulkarni, A H and Patil, A 2012 Applying image processing technique to detect plant diseases International Journal of Modern Engineering Research, 2(5), 3661-3664 [2] Bharate, A A and Shirdhonkar, M S 2017 A review on plant disease detection using image processing In 2017 International JETIR2105789 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f927 © 2021 JETIR May 2021, Volume 8, Issue [14] Khamparia, A., Saini, G., Gupta, D., Khanna, A., Tiwari, S and de Albuquerque, V H C 2020 Seasonal crops disease prediction and classification using deep convolutional encoder network Circuits, Systems, and Signal Processing, 39(2), 818836 DOI= https://doi.org/10.1007/s00034-019-01041-0 [15] Le, V N T., Ahderom, S., Apopei, B and Alameh, K 2020 A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators GigaScience, 9(3), giaa017 https://doi.org/10.1093/gigascience/giaa017 [16] Petrellis, N 2018 A review of image processing techniques common in human and plant disease diagnosis Symmetry, 10(7), 270 https://doi.org/10.3390/sym10070270 [17] McDermott, J E., Corrigan, A., Peterson, E., Oehmen, C., Niemann, G., Cambronne, E D and Heffron, F 2011 Computational prediction of type III and IV secreted effectors in gram-negative bacteria Infection and immunity, 79(1), 23-32 DOI= 10.1128/IAI.00537-10 [18] Amara, J., Bouaziz, B and Algergawy, A 2017 A deep learning-based approach for banana leaf diseases classification Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband [19] Sperschneider, J., Gardiner, D M., Dodds, P N., Tini, F., Covarelli, L., Singh, K B and Taylor, J M 2016 EffectorP: predicting fungal effector proteins from secretomes using machine learning New Phytologist, 210(2), 743-761 https://doi.org/10.1111/nph.13794 [20] Al Bashish, D., Braik, M and Bani-Ahmad, S 2011 Detection and classification of leaf diseases using K-means-based segmentation and Information technology journal, 10(2), 267-275 [21] Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., and Stefanovic, D 2016 Deep neural networks based recognition of plant diseases by leaf image classification Computational intelligence and neuroscience, 2016 https://doi.org/10.1155/2016/3289801 [22] Ramesh, S., and Vydeki, D 2020 Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm Information processing in agriculture, 7(2),249-260 https://doi.org/10.1016/j.inpa.2019.09.002 [23] Li, D., Wang, R., Xie, C., Liu, L., Zhang, J., Li, R., and Liu, W 2020 A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network Sensors, 20(3),578 https://doi.org/10.3390/s20030578 www.jetir.org (ISSN-2349-5162) [27] Chohan, M., Khan, A., Chohan, R., Katpar, S H., and Mahar, M S 2020 Plant Disease Detection using Deep Learning (May 2020) DOI= 10.35940/ijrte.A2139.059120 [28] Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., and Terzopoulos, D 2020 Image segmentation using deep learning: A survey arXiv preprint arXiv:2001.05566 DOI= 10.1109/TPAMI.2021.3059968 [29] Lin, K., Gong, L., Huang, Y., Liu, C., and Pan, J 2019 Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network Frontiers in plant science, 10, 155 https://doi.org/10.3389/fpls.2019.00155 [30] Iqbal, Z., Khan, M A., Sharif, M., Shah, J H., ur Rehman, M H., and Javed, K 2018 An automated detection and classification of citrus plant diseases using image processing techniques: A review Computers and electronics in agriculture, 153, 12-32 https://doi.org/10.1016/j.compag.2018.07.032 [31] Rauf, H T., Saleem, B A., Lali, M I U., Khan, M A., Sharif, M., and Bukhari, S A C 2019 A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning Data in brief, 26, 104340 https://doi.org/10.1016/j.dib.2019.104340 [32] Thomas, S., Behmann, J., Steier, A., Kraska, T., Muller, O., Rascher, U., and Mahlein, A K 2018 Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform Plant Methods, 14(1), 1-12 https://doi.org/10.1186/s13007-018-0313-8 [33] Pugoy, R A D., and Mariano, V Y 2011 Automated rice leaf disease detection using color image analysis In Third international conference on digital image processing (ICDIP 2011) (Vol 8009, p 80090F) International Society for Optics and Photonics (July 2011) https://doi.org/10.1117/12.896494 [34] Sujatha, R., Chatterjee, J M., Jhanjhi, N Z., and Brohi, S N 2021 Performance of deep learning vs machine learning in plant leaf disease detection Microprocessors and Microsystems, 80, 103615 https://doi.org/10.1016/j.micpro.2020.103615 [35] Xiao, J R., Chung, P C., Wu, H Y., Phan, Q H., Yeh, J L A., and Hou, M T K 2021 Detection of Strawberry Diseases Using a Convolutional Neural Network Plants, 10(1), 31 https://doi.org/10.3390/plants10010031 [36] Khairnar, K., and Dagade, R 2014 Disease detection and diagnosis on plant using image processing–a review International Journal of Computer Applications, 108(13), 36-38 [24] Mohanty, S P., Hughes, D P., and Salathé, M 2016 Using deep learning for image-based plant disease detection Frontiers in plant science, 7, 1419 https://doi.org/10.3389/fpls.2016.01419 [25] Dey, A., Bhoumik, D., and Dey, K N 2016 Automatic detection of whitefly pest using statistical feature extraction and image classification methods Int Res J Eng Technol, 3(09), 950959 [26] Keh, S S 2020 Semi-Supervised Noisy Student Pre-training on EfficientNet Architectures for Plant Pathology Classification arXiv preprint arXiv:2012.00332 JETIR2105789 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org f928 ... classification techniques Jhuria et al.[12]in their paper, proposed a detection and classification process for detecting diseases in the plant leaves They considered ANN and Backpropagation method... automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective," 2015 International Conference on Advances in Computing,... diseases in crops Peach, Powdery Mildew, Apple, Grapevine, and Wilt Total samples 650 images Classes: Bacterial Blight images: 125, Normal plant images: 95, Blast images: 170, Brown Spot images: 150,