Estimation of rice plant height and stem number based on laser scaneed point cloud data analysis

ESTIMATION OF RICE PLANT HEIGHT AND STEM NUMBER BASED ON LASER-SCANNED POINT CLOUD DATA ANALYSIS By Phan Thi Anh Thu A Dissertation Submitted to the Graduate School of Engineering of Nagaoka University of Technology for the Degree of Doctor of Engineering Energy and Environment Science Program Nagaoka University of Technology, Japan 2017 Table of contents Table of contents i List of figures iii List of Table vi Chapter 1: INTRODUCTION 1.1 The importance of rice 1.2 Rice growth phases 1.3 The main factors effect on rice growth 1.4 Rice growth parameters measurement methods 1.5 Laser scanner in precise agriculture 1.6 The objectives 1.7 Dissertation organization Chapter 2: DATA ACQUISITION 10 2.1 Experimental site 10 2.2 Laser scanner instrument 11 2.3 Data acquisition 13 2.3.1 Field works 13 2.3.2 Laser Data 15 2.4 Data extraction 16 Chapter 3: METHOD FOR ESTIMATING RICE PLANT HEIGHT WITHOUT GROUND SURFACE DETECTION USING LASER SCANNER MEASUREMENT 20 3.1 Introduction 20 3.2 Methodology 22 3.3 Results 25 3.4 Discussion 26 3.5 Conclusion 29 Chapter 4: FUNDAMENTAL STUDY FOR ESTIMATING RICE PLANT STEM NUMBER USING LASER SCANNER MEASUREMENTS 31 4.1 Introduction 31 4.2 Methodology 32 i 4.2.1 Basic Concepts of Estimating Rice Plant Stem Number from Laser Data under Ideal Conditions (Good Laser Data) 33 4.2.2 Conceptual application using the observed laser data 38 4.2.3 Regression Analysis 40 4.3 Results 40 4.4 Discussion 50 4.5 Conclusion 54 Chapter 5: CONCLUSIONS 56 5.1 Conclusions 56 5.2 The advantage 57 5.3 Application 58 5.3 Limitations and future works 59 Acknowledgement 61 Preference 62 ii List of figures Fig 1.1: Apparent calorie intake and contribution from rice of Countries where rice represents more than 20 percent of calorie intake, 2000-2002 average Source: FAO Fig 1.2: Rice growth phases and stages Source: Arkansas rice production handbook, 2012 Fig 1.3: Research technicians is measuring rice plant height and stem number in the field Fig 2.1: Schematic of the field experiment setup for measuring the 3D laser point cloud in 2013 and 2014 (a) Laser scanning plane, and (b) planting geometry of the five plots (P1–P5) Arrows indicate row orientation Row orientation is not considered in plot 11 Fig 2.2: Field observations were collected by laser scanners (a) SICK LMS 200 and (b) UTM 30LX in 2014 and 2016, respectively Both lasers were approximately m above the paddy field surface and could move along the rail under motorized power 12 Fig 2.3: Cultivation calendar of special cultivated rice Koshihikari The studied period is from middle June to end of July every year and is identified inside the red rectangle In this period, plant height increases from 12 cm to 80 cm and stem number increases from 60 stems/m2 to 500 stems/m2 Source: http://www.ja-echigosantou.or.jp/contents/1_einou/agrinet /310undou/2012/06koshihikari.pdf 13 Fig 2.4: The measured physical parameters of rice plant in (a) average rice plant height (𝐻) and (b) rice plant stem number (𝑆) with their standard deviation in three growing seasons 14 Fig 2.5: Illustration of point cloud data (a) SICK LMS 200 point cloud of rice canopy in nadir viewing and (b) Point cloud capture with LIDAR sensor (Velodyne); Source: https://www.geospatialworld.net/news/velodyne-releases-powerful-lidar/ 14 Fig 2.6: Plot target area in 2014 (a) location of target area, (b) vertical distance relative to scan angle of scanline A–A in plot 3, (c) range image, (d) intensity image 16 Fig 2.7: Histograms of 3D point cloud data from plot 2, collected in three years The rows show the results from 2013 (a–g), 2014 (h–n), and 2016 (o–u) Red dashed lines show the ground position after harvesting the crop 17 Fig 2.8: The likelihood of SICK LMS 200 laser pulses reaching a ground surface covered by water Images a) and b) are an infrared image and an intensity image collected by SICK LMS 200 on June 21st, 2013, respectively For this, potted rice plants were put into clear water, and the tops of the pots at a water depth of around cm could be recognized from the intensity image In contrast, c) the paddy field was covered by turbid water with many bubbles on the iii water surface on June 18th, 2014, and d) the paddy field was not covered by water on June 24th, 2014 18 Fig 3.1: Determination of the top and bottom location, where 𝑝𝑡 and 𝑝𝑏 are the closest percentiles to the top and bottom of the rice plant, respectively; 𝐷𝑡 and 𝐷𝑏 are the vertical distances from the scanning point to the installation height of the laser scanner at 𝑝𝑡 and 𝑝𝑏 22 Fig 3.2: Vertical distance at various percentile ranks Panels (a–c) show results from 2013 (d– f) are from 2014, and (g) from 2016 Columns show results from (a and d) densely planted plots, (b, e and g) moderate density plots, and (c and f) sparse plots Red dashed lines show the position of the ground surface after harvesting the crop 24 Fig 3.3: Plots of 𝑟𝐷 against measured rice plant height The rows show the results from 2013 (a–c), 2014 (d–f), and 2016 (g–i) The columns show the results with the reference position computed at 70th (a, d, g), 80th (b, e, h), and 95th (c, f, i) percentile ranks Red dash-dotted lines show the regression lines 27 Fig 3.4: Plots of 𝑟𝐷 against measured rice plant height, the regression lines and achieved 𝑅𝑀𝑆𝐸 (a) show the result of a combination of the first two years and (b) shows the result of a combination of three years 28 Fig 4.1: Spatial volume (𝑉𝑠) between the ground and the rice canopy surface detected by the laser: Cross-section of voxel representation of 𝑉𝑠 34 Fig 4.2: Spatial volume (𝑉𝑠) is simply modeled as a rectangular prism from the measured rice plant height (𝐻) and the approximately area-proportional function of 𝑆 (𝑓(𝑆)) 34 Fig 4.3: Calculation of 𝑛𝑉𝑠 from laser scanning data (a) Top and bottom (close to the ground surface) positions of the rice plant used for normalizing 𝐷 The corresponding laser data are then divided into many layers for 𝑛𝑉𝑠 computaion (b) Volumes (in voxels) of scanning points corresponding to each layer, (c) the computation of 𝑛𝑉𝑘 from the scanning points, and (d) the relationship between 𝑛𝑉𝑠 and 𝑛𝑉𝑠𝑚𝑎𝑥 36 Fig 4.4: The 𝑛𝐷 histogram shape of the third observation of plot in growing season of 2014 with divided layers of (a) 𝑚 = 5, (b) 𝑚 = 10, (c) 𝑚 = 50, and (d) 𝑚 = 100 39 Fig 4.5: Distributions of original and normalized data of seven observations collected at different times of the growing season in (a) 2014 and (e) 2016 The observation date is clearly displayed in Table 2.2 The planting density was 15.1 plant hills.m-2 and the planting direction was perpendicular Rows show the results from 2014 (a–d) and 2016 (e–h) Columns show the original data (a, e) and the 𝑛𝐷 values for different bottom positions of the rice plant; 𝐷95 (b, f), 𝐷80 (c, g), and 𝐷70 (d, h) 41 iv Fig 4.6: These charts show the relationship between relative spatial volume and stem number in 2013 with m=100 The columns show the results with the various determined bottom position of 𝐷70, 𝐷80 , and 𝐷95 The rows show the results from plot (a-c)), plot (d-f), plot (g-i), plot (j-l) and plot (m-o) 42 Fig 4.7: These charts show the relationship between relative spatial volume and stem number in 2014 with m=100 The columns show the results with the various determined bottom position of 𝐷70, 𝐷80 , and 𝐷95 The rows show the results from plot (a-c)), plot (d-f), plot (g-i), plot (j-l) and plot (m-o) 43 Fig 4.8: These charts show the relationship between relative spatial volume and stem number in 2016 with m=100 The columns show the results with the various determined bottom position of 𝐷70, 𝐷80 , and 𝐷95 The rows show the results from target area (a-c)) and target area (d-f) 44 Fig 4.9: Estimated rice plant stem numbers during the growing season of 2013 with 𝑚=100 Rows show the results from plot (a–c), plot (d–f), plot (g–i), plot (j–l), and plot (m– o) Columns show the results for different bottom positions of the rice plants; 𝐷70 (a, d, g, j, m), 𝐷80 (b, e, h, k, m), and 𝐷95 (c, f, i, l, o) 48 Fig 4.10: Estimated rice plant stem numbers during the growing season of 2014 with 𝑚=100 Rows show the results from plot (a–c), plot (d–f), plot (g–i), plot (j–l), and plot (m– o) Columns show the results for different bottom positions of the rice plants; 𝐷70 (a, d, g, j, m), 𝐷80 (b, e, h, k, m), and 𝐷95 (c, f, i, l, o) 49 Fig 4.11: Estimated rice plant stem numbers during the growing season of 2016 Rows show the results for different numbers of layers; 𝑚 = 500 (a–c), 𝑚 = 100 (d–f), and 𝑚 = 10 (g–i) Columns show the results for different bottom positions of the rice plants; 𝐷70 (a, d, g), 𝐷80 (b, e, h), and 𝐷95 (c, f, i) 50 Fig 4.12: Estimated 𝑆 (with 100 divided layers) versus manually measured 𝑆 during the growing season of 2013 (red), 2014 (black) and 2016 (blue) Rows show the results in plot for different numbers of layers; 𝑚 = 500 (a–c), 𝑚 = 100 (d–f), and 𝑚 = 10 (g–i) Columns show the results for different bottom positions of the rice plants; 𝐷70 (a, d, g), 𝐷80 (b, e, h), and 𝐷95 (c, f, i) 53 v List of Table Table 2.1: Rice planting densities and geometries during the growing seasons of 2013 and 2014 11 Table 2.2: List of field observation data 15 Table 4.1: Computed allometric parameters for the scaling exponential functions that predict the rice plant stem number in moderate density plots 45 Table 4.2: Effect of layer number and bottom position of rice plant on the precision of the estimated stem number in a moderate density plot (plot 2) during the 2014 and 2016 growing seasons 46 Table 4.3: Effect of planting density and geometry on the precision of the estimated stem number in 2013 with 𝐷𝑏𝑜𝑡𝑡𝑜𝑚 = 𝐷95 47 Table 4.4: Effect of planting density and geometry on the precision of the estimated stem number in 2014 with 𝐷𝑏𝑜𝑡𝑡𝑜𝑚 = 𝐷80 47 vi Chapter 1: Introduction Chapter 1: INTRODUCTION 1.1 The importance of rice For food security, a critical issue in the world, the most important thing is to have enough food to provide the daily life of the human In Asian countries, rice is the main food crop The population needs rice for survival because they consume rice for every daily meal (Figure 1.1) The management of rice crop is necessary for national food security and political stability Moreover, rice is the main income of people living in the agricultural area Therefore, the social security is directly affected by rice production According to a report of FAO in 2000, rice production got the problem of declining rate of growth in yields, depletion of natural resources, labor shortages, institutional limitations and environmental pollution By applying new rice variety and fertilizer technology, the increase of rice yield leads to the growth in rice production Nowadays, the consumers pay more attention to rice quality They demand for safe, affordable, and high-quality rice Because of the importance of rice, rice growth must be controlled by applying technologies to reduce labor costs and achieve the optimized yield with good quality Thus, it is necessary to monitor the rice growth during the rice growing season Fig 1.1: Apparent calorie intake and contribution from rice of Countries where rice represents more than 20 percent of calorie intake, 2000-2002 average Source: FAO Chapter 1: Introduction 1.2 Rice growth phases Rice growth includes many stages from seeding to harvesting According to Arkansas rice production handbook, 2012, rice growth can be divided into three main phases of development including vegetative phase, reproduction phase and ripening phase or maturation phase (Figure 1.2) The rice growth duration depends on the variety and the environment Fig 1.2: Rice growth phases and stages Source: Arkansas rice production handbook, 2012 Chapter 1: Introduction The vegetative phase starts from seed germination stage and ends at tillering stage This phase is characterized by a gradual increase in plant height, tillering activity, and more leaves During this phase, the rice plant almost stands upright and the number of tillers (stems) increase in a sigmoidal-shaped curve After the maximum tiller number is reached the tiller number decreases, whereas rice plant height continues to increase with a slower rate From this time, no more effective tillers are produced Therefore, the yield component, potential panicles per unit area, is determined The reproduction phase starts at panicle initiation stage and ends at flowering stage During this period, the culm elongates, tiller number lightly decrease, panicle emerges from the stem The heading date, which is used to predict draining and harvest date, is identified when the 50% of panicles are exserted from the boot Flowering begins after panicles have fully emerged from the boot The ripening phase is characterized by grain growth Solar intensity and temperature also effect on this period The size and weight of rice grain will increase Moreover, the color of grain will turn to gold or straw color at maturity, whereas the rice leaves begin to senesce 1.3 The main factors effect on rice growth Rice is almost grown in many humid tropical and subtropical areas The factors that effect on rice growth can be classified to four main groups including farming practices, land and soil factors, climatic factors, fertilizer treatment and water supply According to the rice association, there are 40,000 rice varieties in the world and based on the condition of environment and the demands on rice quality, rice variety is selected The variety of rice variety partly explains why yields obtained from different countries are different During their early phases of rice growth, good, clean and healthy rice seeds are required for healthy development of rice plants Land preparation is need for controlling weeds, water and fertilizer management in wetland rice production Depending on farming practices the rice crop can be established by direct seeding or transplanting of seedlings Direct seeding requires more seeds than transplanting but transplanting have better weeding condition by hand or mechanical Rice is grown on all types of soils; however, wetland rice production suits to soil of river valleys and deltas Variations in soil conditions and unsuitable soils cause wide disparity in rice yields During the rice growing season, air temperature, solar radiation, day-length, winds and relative humidity are main climatic factors that effect on the rice growth Air temperature effect grain fill period, grain weight and grain quality Too high or too low air temperature is Chapter 4: Fundamental study for estimating rice plant stem number using laser scanner measurements manually measured 𝑆 By virtue of these promising results, the proposed approach is expected to be recommended for rice plant stem estimation from data collected by different sensors The major limitation of this approach is the dependence of the allometric parameters on the planting conditions, including the density and geometry of the plantings Moreover, the environmental effects were not completely removed from the laser data In future work, the effects of planting geometry and environment should be considered in a refined version of the method 55 Chapter 5: Conclusions Chapter 5: CONCLUSIONS 5.1 Conclusions In the entire study, the physical parameters of rice plant including rice plant height (𝐻) and rice plant stem number (𝑆) are estimated from laser scanner data They directly affect rice plant competition and amount of solar radiation received, contribute to rice yield and influence rice quality The method for estimating those parameters are proposed and validate using the field observation data in three growing seasons of 2013, 2014 and 2016 More explicitly, in two first years, the observation data was collected in five test plots with different planting geometry by the SICK LMS 200 line-laser scanner, whereas the test plot with one planting geometry was observed by the UTM 30LX line-laser scanner in 2016 Although the footprint size and footprint shape are different, the good results have been achieved with observation data collected by both mentioned laser scanner The conclusions in detail of the entire study can be displayed as following In the fundamental studies for 𝐻 estimation using laser scanner measurements, the new reference position is proposed instead or ground surface level for 𝐻 computation By this way, the problem of unobservable ground surface in dense density crop or wet land paddy is completely solved In this approach, the reference position is determined at the top of the rice plant and insignificant effect by planting density or planting geometry The bottom position of rice plant is identified from observation data by applying percentile analysis The difference between top and bottom position named relative vertical distances (𝑟𝐷) is used to estimate 𝐻 As a result, 𝑟𝐷 is always smaller than 𝐻 and has a high correlation with 𝐻 Thus, the consistent regression lines slope between 𝑟𝐷 and 𝐻 is greater than 1.0 The effect of the number of stems on the regression slopes depends on the distance between the plant bottom position and the ground surface With the upper the plant bottom position from the ground surface, the regression slope is steeper With the bottom position of the plant closed to the ground surface (𝑝𝑏 =95th), the achieved maximum 𝑅𝑀𝑆𝐸 of cm is only cm greater than standard deviation of manually measured 𝐻 Additional, without the consideration of different footprint size and shape, the achieved 𝑅𝑀𝑆𝐸 was cm and the bias value was 18 cm For the 𝑝𝑏 of 56 Chapter 5: Conclusions 80th and 70th of the RMSE was about cm and corresponds to les than 10% of mature rice plant height In conclusion, the proposed method is suitable for obtaining rice plant heights greater than 20 cm In the second study, the method for estimating rice plant stem number (𝑆) is proposed and validated The relative spatial volume (𝑟𝑉𝑠𝑙𝑎𝑠𝑒𝑟 ) is derived from the laser data and presented as an exponential function of 𝑆 The results confirmed the relationship between 𝑟𝑉𝑠𝑙𝑎𝑠𝑒𝑟 and 𝑆, and demonstrated that rice plant stem number can be estimated directly from laser scanning data In this study, the laser observed data is normalized to remove the effect of rice plant height The allometric parameters are determined in three growing seasons, although different line laser scanning devices were applied They depend on the planting geometry, planting density and 𝐷𝑏𝑜𝑡𝑡𝑜𝑚 , but were almost independent of the number of layers The layer number should be >10 (𝑚>10) The maximum 𝑆 is clearly obtained from the estimated stem number of 𝑀𝐷80 with 𝑟𝐸 values of approximately 0.10 By using the 𝑟𝑉𝑠𝑙𝑎𝑠𝑒𝑟 metric, the footprint shape or 𝑃𝑣𝑜𝑥𝑒𝑙 not need to be concerned Moreover, the estimated 𝑆 is good match to manually measured 𝑆 In general conclusion, the proposed approach is recommended for rice plant stem estimation from data collected by different sensors In general, without using survey grade laser scanner as previous, the similar best results are also achieved with non-grade laser scanner (line laser scanner) For detail, in both studies of estimating rice plant height and stem number, the achieved 𝑟𝐸s in the best cases are equal or less than 10% In practice farming, the 𝑟𝐸 of 10% is acceptable Therefore, the results in this study could be poupular used for monitoring the future crop which has the same planting conditions with the significantly reduction of equipment cost 5.2 The advantage The best results which are mention above desmontrate the advantages of this study In general, there are four main improvements, which agree to initial expectations, are achieved They can be listed as following Non-survey grade laser scanner could be used for monitoring rice growth The acceptable relative errors (𝑟𝐸) in practice farming is achieved The problem of unobservable ground surface in previous studies is solved The fertilizer application resolution for smart agriculture could be reduce 57 Chapter 5: Conclusions According to the good results achieved in this study, it was expected that the proposed approaches are suitable for monitoring rice plant height and stem number from laser data collected by different non-surveying grade sensors 5.3 Application Laser scanner has delivered a promising tool for forest inventory with the ability to determine the tree height, basal area, and volume of a forest stand In agriculture, plant growth, indicating of crop performance, can be measured by different methods on various spatial and temporal scales The laser scanner is used of in-field to monitor the crop growth by monitoring crop growth characteristics such as crop height In this study, the non-surveying grade laser scanner is chosen for monitoring rice growth By virtue of these promising results of the entire study, the proposed approaches are expected to be recommended for rice plant height estimation from laser data collected by different sensors Moreover, the achieved parameters can be applied for estimating rice plant stem number of the rice crop in future In other words, if a rice crop with same planting condition is collected by a line laser scanner, we can know the rice plant height and stem number of that crop Especially, before heading date in late July, the fertilizer application should be performed; therefore, the information of rice growth is necessary for identifying the amount and element of added fertilizer The approaches and results in this study can be applied to UAV-based laser scanner system or UAV LIDAR system in future UAV LIDAR system is one of the most developing technologies and very flexible to use for monitoring agricultural crops UAV easily cover large areas or difficult access in several minutes Therefore, UAV LIDAR can collect a data set which covers everything that we need to know These systems can be used to create agricultural land elevation maps, crop surface models or plant density map For projects require repeated surveys to track crop growth status, UAV LIDAR could give faster and more cost- effective results The UAV LIDAR system with lightweight and compact sensors is now an easy investment and affordable By using UAV LIDAR system, the rice plant information can be rapidly achieved with the high spatial resolution (around 1m2) As a result, the biological differences existed in agricultural fields could be determined in detail Thus, the farmers can control the rice growth in sub-field scale In detail, the farmers could increase the fertilizer application resolution to meter or several meters resolution to get the maximum yield and optimize the economic benefits Combining with other techniques for example photographic techniques, LiDAR UAVs can 58 Chapter 5: Conclusions result in enhanced performance in precision agriculture such as ensure crop growth, estimate yields or identify non-productive crop areas 5.3 Limitations and future works In the entire study, the major requirement of observed data requirement is that the scanning point must be dense enough to get the information of rice canopy Additional, the narrow rice leaf blades, which can be smaller in width than the laser footprint size, lead to difficulties in detecting both the rice leaf tip and the individual rice leaves with the laser scanner With the cheap line laser scanner used in this study, the footprint size depends on the observed distance Thus, the laser scanner was always installed at the low height of around m from the ground surface As a result, we have to make the strips planning and spent more time to collect information in large area Moreover, this study only focuses on the nadir data and not consider to the incident angle effects This make the target width is more and more narrow The other factors are also ignored in this study is planting conditions and environmental effects Thus, these factors lead to the limitation of this study as following For the rice plant height estimation method, the planting density and geometry insignificantly effect on the 𝐷𝑡𝑜𝑝 but 𝐷𝑏𝑜𝑡𝑡𝑜𝑚 of rice plant Therefore, the 𝑟𝐷 is also effected In detail, the different planting density and geometry leads to the different rice plant stem number per unit area According to the results in chapter 3, the rice plant stem number effects on the correlation between estimated and measured 𝐻 However, this effect has not been considered carefully in the present study For the rice plant stem number estimation method, the major limitation of the proposed approach is the dependence of the allometric parameters on the density and geometry of the plantings Moreover, the quality of laser data is compromised by environmental factors The environmental effects were not completely removed from the laser data As a result, there was many outline points Based on the value of rice plant height and installation height of laser scanner, the outline points are manually identified and removed This process is time consuming and may contain the human errors In future work, the effects of incident angle must be careful investigations in a refined version of the method The fraction of vegetation or vegetation coverage should be mentioned as the weight factor to reduce the effects of planting density and geometry In addition to reduce the effect of environment factors on laser data, the data collecting process should be 59 Chapter 5: Conclusions planed carefully and the fixed frame in which the laser scanner is mounted should be designed to minimize the laser scanner shake In this way, the refined version can be developed and accurately applied to larger target areas 60 Preference Acknowledgement Firstly, I would like to express my sincere gratitude to Professor Rikimaru Atsushi and Associate Professor Takahashi Kazuyoshi for their patience, motivation, and immense knowledge Their guidance helped me in all the time of research and writing of this thesis I could not have imagined having a better advisor and mentor for study I really appreciate the advices and continuous supports of Associate Professor Takahashi I could not finish my doctoral study without his understanding and encouragement My sincere thanks also go to Mr Higuchi Yasuhiro, a member of Niigata Agricultural Research Institute who helps me to establish the rice field and perform the field works I thank Mr Ichikawa Yusuke and Mr Odaka Naoto, my lab-mates, for helping me carry out the field observation Without their precious support, it would not be possible to conduct this research 61 Preference Preference Alvaro F., García del Moral L F., and Royo C., “Usefulness of remote sensing for the assessment of growth traits in individual cereal plants grown in the fiel”, Intl J Remote Sensing 28(11): 2497-2512 (2007) Armesto-González J., Riveiro-Rodríguez B., González-Aguilera D., Rivas-Brea M T., “Terrestrial laser scanning intensity data applied to damage detection for historical buildings”, Journal of Archaeological 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