UAV-Based Remote Sensing for Soybean FVC, LCC, and Maturity Monitoring

Jingyu Hu, Jibo Yue, Xin Xu, Shaoyu Han, Tong Sun, Yang Liu, Haikuan Feng and Hongbo Qiao ()
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Jingyu Hu: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Jibo Yue: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Xin Xu: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Shaoyu Han: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Tong Sun: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Yang Liu: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
Haikuan Feng: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
Hongbo Qiao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China

Agriculture, 2023, vol. 13, issue 3, 1-19

Abstract: Timely and accurate monitoring of fractional vegetation cover (FVC), leaf chlorophyll content (LCC), and maturity of breeding material are essential for breeding companies. This study aimed to estimate LCC and FVC on the basis of remote sensing and to monitor maturity on the basis of LCC and FVC distribution. We collected UAV-RGB images at key growth stages of soybean, namely, the podding (P1), early bulge (P2), peak bulge (P3), and maturity (P4) stages. Firstly, based on the above multi-period data, four regression techniques, namely, partial least squares regression (PLSR), multiple stepwise regression (MSR), random forest regression (RF), and Gaussian process regression (GPR), were used to estimate the LCC and FVC, respectively, and plot the images in combination with vegetation index (VI). Secondly, the LCC images of P3 (non-maturity) were used to detect LCC and FVC anomalies in soybean materials. The method was used to obtain the threshold values for soybean maturity monitoring. Additionally, the mature and immature regions of soybean were monitored at P4 (mature stage) by using the thresholds of P3-LCC. The LCC and FVC anomaly detection method for soybean material presents the image pixels as a histogram and gradually removes the anomalous values from the tails until the distribution approaches a normal distribution. Finally, the P4 mature region (obtained from the previous step) is extracted, and soybean harvest monitoring is carried out in this region using the LCC and FVC anomaly detection method for soybean material based on the P4-FVC image. Among the four regression models, GPR performed best at estimating LCC (R 2 : 0.84, RMSE: 3.99) and FVC (R 2 : 0.96, RMSE: 0.08). This process provides a reference for the FVC and LCC estimation of soybean at multiple growth stages; the P3-LCC images in combination with the LCC and FVC anomaly detection methods for soybean material were able to effectively monitor soybean maturation regions (overall accuracy of 0.988, mature accuracy of 0.951, immature accuracy of 0.987). In addition, the LCC thresholds obtained by P3 were also applied to P4 for soybean maturity monitoring (overall accuracy of 0.984, mature accuracy of 0.995, immature accuracy of 0.955); the LCC and FVC anomaly detection method for soybean material enabled accurate monitoring of soybean harvesting areas (overall accuracy of 0.981, mature accuracy of 0.987, harvested accuracy of 0.972). This study provides a new approach and technique for monitoring soybean maturity in breeding fields.

Keywords: UAV; chlorophyll; fractional vegetation cover; maturity monitoring; anomaly detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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