Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms

doi:10.12133/j.smartag.2021.3.2.202104-SA003

Abstract

Abstract:

Wheat lodging is a negative factor affecting yield production. Obtaining timely and accurate wheat lodging information is critical. Using unmanned aerial systems (UASs) images for wheat lodging detection is a relatively new approach, in which researchers usually apply a manual method for dataset generation consisting of plot images. Considering the manual method being inefficient, inaccurate, and subjective, this study developed a new image processing-based approach for automatically generating individual field plot datasets. Images from wheat field trials at three flight heights (15, 46, and 91 m) were collected and analyzed using machine learning (support vector machine, random forest, and K nearest neighbors) and deep learning (ResNet101, GoogLeNet, and VGG16) algorithms to test their performances on detecting levels of wheat lodging percentages: non- (0%), light (<50%), and severe (>50%) lodging. The results indicated that the images collected at 91 m (2.5 cm/pixel) flight height could yield a similar, even slightly higher, detection accuracy over the images collected at 46 m (1.2 cm/pixel) and 15 m (0.4 cm/pixel) UAS mission heights. Comparison of random forest and ResNet101 model results showed that ResNet101 resulted in more satisfactory performance (75% accuracy) with higher accuracy over random forest (71% accuracy). Thus, ResNet101 is a suitable model for wheat lodging ratio detection. This study recommends that UASs images collected at the height of about 91 m (2.5 cm/pixel resolution) coupled with ResNet101 model is a useful and efficient approach for wheat lodging ratio detection.

Key words: wheat lodging ratio, machine learning, deep learning, mission height, UAS, ResNet101

CLC Number:

TP751

FLORES Paulo, ZHANG Zhao. Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms[J]. Smart Agriculture, 2021, 3(2): 23-34.

References

1	BALFOURIER F, BOUCHET S, ROBERT S, et al. Worldwide phylogeography and history of wheat genetic diversity[J]. Science Advances, 2019, 5(5): ID 0536.
2	SHEWRY P R, HEY S J. The contribution of wheat to human diet and health[J]. Food Energy Security, 2015, 4(3): 178-202.
3	STATISTA. Global wheat production from2011/2012 to 2020/2021[EB/OL]. (2021-03-05)[2021-03-30]. .
4	JAHAN N, FLORES P, LIU Z, et al. Detecting and distinguishing wheat diseases using image processing and machine learning algorithms[C]// 2020 ASABE Annual International Virtual Meeting. St. Joseph, MI: ASABE, U.
4	S., 2020.
5	MONDAL S, RUTKOSKI J E, VELU G, et al. Harnessing diversity in wheat to enhance grain yield, climate resilience, disease and insect pest resistance and nutrition through conventional and modern breeding approaches[J]. Frontiers in Plant Science, 2016, 7: ID 991.
6	WEBBER H, EWERT F, OLESEN J E, et al. Diverging importance of drought stress for maize and winter wheat in Europe[J]. Nature Communications, 2018, 9(1): 1-10.
7	ZHANG Z, FLORES P, IGATHINATHANE C, et al. Wheat lodging detection from UAS imagery using machine learning algorithms[J]. Remote Sensing, 2020, 12(11): ID 1838.
8	CHAUHAN S, DARVISHZADEH R, BOSCHETTI M, et al. Remote sensing-based crop lodging assessment: Current status and perspectives[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019,151: 124-140.
9	WU W, MA B L. A new method for assessing plant lodging and the impact of management options on lodging in canola crop production[J]. Scientific Reports, 2016, 6(1): 1-17.
10	SETTER T L, LAURELES E V, MAZAREDO A M. Lodging reduces yield of rice by self-shading and reductions in canopy photosynthesis[J]. Field Crops Research, 1997, 49: 95-106.
11	PINTHUS M J. Lodging in wheat, barley, and oats: the phenomenon, its causes, and preventive measures[J]. Advanced Agronomy, 1974, 25: 209-263.
12	ISLAM M S, PENG S, VISPERAS R M, et al. Lodging-related morphological traits of hybrid rice in a tropical irrigated ecosystem[J]. Field Crops Research, 2007, 101(2): 240-248.
13	ZHANG Z, HEINEMANN P H, LIU J, et al. The development of mechanical apple harvesting technology: A review[J]. Transactions of the ASABE, 2016, 59(5): 1165-1180.
14	ZHANG Z, POTHULA A, LU R. Improvements and evaluation of an infield bin filler for apple bruising and distributions[J]. Transactions of the ASABE, 2019, 62(2): 271-280.
15	ZHANG Z, IGATHINATHANE C, LI J, et al. Technology progress in mechanical harvest of fresh market apples[J]. Computers and Electronics in Agriculture, 2020, 175: ID 105606.
16	LU R, POTHULA A, MIZUSHIMA A, et al. System for sorting fruit: 9919345[P]. 2018-03-20.
17	ZHANG Z, LU Y, LU R. Development and evaluation of an apple infield grading and sorting system[J]. Postharvest Biology and Technology, 2021, 180: ID 111588.
18	YAO L, HU D, ZHAO C, et al. Wireless positioning and path tracking for a mobile platform in greenhouse[J]. International Journal of Agricultural and Biological Engineering, 2021, 14(1): 216-223.
19	FLORES P, ZHANG Z, IGATHINATHANE C. et al. Distinguishing seedling volunteer corn from soybean through greenhouse color, color-infrared, and fused images using machine and deep learning[J]. Industrial Crops and Products, 2021, 161: ID 113223.
20	FISCHER R A, STAPPER M. Lodging effects on high-yielding crops of irrigated semidwarf wheat[J]. Field Crops Research, 1987, 17: 245-258.
21	PI?ERA-CHAVEZ F J, BERRY P M, FOULKES M J, et al. Avoiding lodging in irrigated spring wheat. I. Stem and root structural requirements[J]. Field Crops Research, 2016, 196: 325-336.
22	YANG M D, HUANG K S, KUO Y H, et al. Spatial and spectral hybrid image classification for rice lodging assessment through UAV imagery[J]. Remote Sensing, 2017, 9(6): ID 583.
23	YANG H, CHEN E, LI Z, et al. Wheat lodging monitoring using polarimetric index from RADARSAT-2 data[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 34: 157-166.
24	ZHAO L, YANG J, LI P, et al. Characterizing lodging damage in wheat and canola using Radarsat-2 polarimetric SAR data[J]. Remote Sensing Letter, 2017, 8(7): 667-675.
25	VARGAS J Q, KHOT L R, PETERS R T, et al. Low orbiting satellite and small UAS-based high-resolution imagery data to quantify crop lodging: A case study in irrigated spearmint[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(5): 755-759.
26	CHAUHAN S, DARVISHZADEH R, LU Y, et al. Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel-2 data[J]. Remote Sensing of Environment, 2020, 243: ID 111804.
27	CHU T, STAREK M J, BREWER M J, et al. Assessing lodging severity over an experimental maize (Zeamays L.) field using UAS images[J]. Remote Sensing, 2017, 9(9): ID 923.
28	RAJAPAKSA S, ERAMIAN M, DUDDU H, et al. Classification of crop lodging with gray level co-occurrence matrix[C]// 2018 IEEE Winter Conference on Applications of Computer Vision. Piscataway, New York, USA: IEEE, 2018: 251-258.
29	LI X, LI X, LIU W, et al. A UAV-based framework for crop lodging assessment[J]. European Journal of Agronomy, 2021, 123: ID 126201.
30	MARDANISAMANI S, MALEKI F, HOSSEINZADEH K, et al. Crop lodging prediction from UAV-acquired images of wheat and canola using a DCNN augmented with handcrafted texture features[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway, New York, USA: IEEE, 2019.
31	ABALLA A, CEN H, WAN L, et al. Nutrient status diagnosis of infield oilseed rape via deep learning-enabled dynamic model[J]. IEEE Transactions on Industrial Informatics, 2020, 17(6): 4379-4389.
32	FLORES P, ZHANG Z, JITHIN M, et al. Distinguishing volunteer corn from soybean at seedling stage using images and machine learning[J]. Smart Agriculture, 2020, 2(3): 61-74.

[1]	MAO Kebiao, ZHANG Chenyang, SHI Jiancheng, WANG Xuming, GUO Zhonghua, LI Chunshu, DONG Lixin, WU Menxin, SUN Ruijing, WU Shengli, JI Dabin, JIANG Lingmei, ZHAO Tianjie, QIU Yubao, DU Yongming, XU Tongren. The Paradigm Theory and Judgment Conditions of Geophysical Parameter Retrieval Based on Artificial Intelligence [J]. Smart Agriculture, 2023, 5(2): 161-171.
[2]	SHI Jiefeng, HUANG Wei, FAN Xieyang, LI Xiuhua, LU Yangxu, JIANG Zhuhui, WANG Zeping, LUO Wei, ZHANG Muqing. Yield Prediction Models in Guangxi Sugarcane Planting Regions Based on Machine Learning Methods [J]. Smart Agriculture, 2023, 5(2): 82-92.
[3]	XIA Xue, CHAI Xiujuan, ZHANG Ning, ZHOU Shuo, SUN Qixin, SUN Tan. A Lightweight Fruit Load Estimation Model for Edge Computing Equipment [J]. Smart Agriculture, 2023, 5(2): 1-12.
[4]	GUO Yangyang, DU Shuzeng, QIAO Yongliang, LIANG Dong. Advances in the Applications of Deep Learning Technology for Livestock Smart Farming [J]. Smart Agriculture, 2023, 5(1): 52-65.
[5]	ZUO Min, HU Tianyu, DONG Wei, ZHANG Kexin, ZHANG Qingchuan. Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example [J]. Smart Agriculture, 2023, 5(1): 34-43.
[6]	JI Jie, JIN Zhou, WANG Rujing, LIU Haiyan, LI Zhiyuan. Progressive Convolutional Net Based Method for Agricultural Named Entity Recognition [J]. Smart Agriculture, 2023, 5(1): 122-131.
[7]	HU Songtao, ZHAI Ruifang, WANG Yinghua, LIU Zhi, ZHU Jianzhong, REN He, YANG Wanneng, SONG Peng. Extraction of Potato Plant Phenotypic Parameters Based on Multi-Source Data [J]. Smart Agriculture, 2023, 5(1): 132-145.
[8]	LIU Xiaohang, ZHANG Zhao, LIU Jiaying, ZHANG Man, LI Han, FLORES Paulo, HAN Xiongzhe. Infield Corn Kernel Detection and Counting Based on Multiple Deep Learning Networks [J]. Smart Agriculture, 2022, 4(4): 49-60.
[9]	FU Hongyu, WANG Wei, LIAO Ao, YUE Yunkai, XU Mingzhi, WANG Ziwei, CHEN Jianfu, SHE Wei, CUI Guoxian. High Quality Ramie Resource Screening Based on UAV Remote Sensing Phenotype Monitoring [J]. Smart Agriculture, 2022, 4(4): 74-83.
[10]	XU Yulin, KANG Mengzhen, WANG Xiujuan, HUA Jing, WANG Haoyu, SHEN Zhen. Corn and Soybean Futures Price Intelligent Forecasting Based on Deep Learning [J]. Smart Agriculture, 2022, 4(4): 156-163.
[11]	FAN Chengzhi, WANG Ziwen, YANG Xingchao, LUO Yongkai, XU Xuexin, GUO Bin, LI Zhenhai. Machine Learning Inversion Model of Soil Salinity in the Yellow River Delta Based on Field Hyperspectral and UAV Multispectral Data [J]. Smart Agriculture, 2022, 4(4): 61-73.
[12]	LUO Qing, RAO Yuan, JIN Xiu, JIANG Zhaohui, WANG Tan, WANG Fengyi, ZHANG Wu. Multi-Class on-Tree Peach Detection Using Improved YOLOv5s and Multi-Modal Images [J]. Smart Agriculture, 2022, 4(4): 84-104.
[13]	SHANG Fengnan, ZHOU Xuecheng, LIANG Yingkai, XIAO Mingwei, CHEN Qiao, LUO Chendi. Detection Method for Dragon Fruit in Natural Environment Based on Improved YOLOX [J]. Smart Agriculture, 2022, 4(3): 120-131.
[14]	ZHANG Zhibo, ZHAO Xining, GAO Xiaodong, ZHANG Li, YANG Menghao. Accurate Extraction of Apple Orchard on the Loess Plateau Based on Improved Linknet Network [J]. Smart Agriculture, 2022, 4(3): 95-107.
[15]	HE Ruimin, ZHENG Kefeng, WEI Qinyang, ZHANG Xiaobin, ZHANG Jun, ZHU Yihang, ZHAO Yiying, GU Qing. Identification and Counting of Silkworms in Factory Farm Using Improved Mask R-CNN Model [J]. Smart Agriculture, 2022, 4(2): 163-173.