Unmanned aerial vehicle (UAV) imaging and machine learning applications for plant phenotyping

The availability of timely and accurate information about plant conditions is critical for making informed de-cisions for maintaining or improving agricultural productivity. However, field data collection is often laborious and time-consuming, underscoring the need for accurate and efficient methods to monitor crop growth and development. Therefore, the objective of this study was to evaluate the effectiveness of combining unmanned aerial vehicle (UAV)-based imaging and machine learning (ML) techniques for monitoring sweet corn (Zea mays var. saccharata) height, biomass, and yield. The study was conducted at the Tropical Research and Education Center (TREC) during the winter (dry) season of 2020-2021 using 16 experimental plots planted with sweet corn. The treatments were set up in a completely randomized block design (RCBD) with four irrigation treatments of 25%, 50%, 75%, and 100% full irrigation with four replications each. Field data collection included plant height, fresh and dry biomass, and yield. In addition, UAV images were collected using the DJI Matrice 210 v2 UAV (SZ DJI Technology Co., Ltd., Shenzhen, China) equipped with a MicaSense RedEdge-MX multispectral sensor (MicaSense, Seattle, WA, USA). Image processing was done with Pix4Dmapper 4.7.5 (Pix4D S.A., Prilly, Switzerland). A crop surface model, representing estimated plant height (UAVH), was calculated based on pixel -to-pixel differences between digital surface and terrain models. A simple linear regression model was used to estimate sweet corn biomass and yield from UAV images estimated plant height (UAVH). In addition, two linear algorithms known as a linear model (LM) and Lasso and elastic-net regularized generalized linear model (GLMNET) and three non-linear ML algorithms including random forest (RF), support vector machine (SVM), and k-nearest neighbor (kNN) were used to predict plant height and biomass. These algorithms were chosen due to their reliable performance and ability to learn complex non-linear relationships. Eight vegetation indices with UAVH were also used to evaluate the ML models' performance to predict plant height and biomass. Results confirmed that UAV imaging could be effectively used to estimate plant height (d = 0.99, r2 = 0.99, and MAE = 5 cm). Estimated fresh and dry biomass from UAV imaging also had good agreements with measured data with r2 values of 0.75 and 0.70, respectively. A statistically significant linear correlation between measured fresh yield at harvest and UAVH was found with d, adjusted r2, and MAE of 0.84, 0.66, and 67 g m- 2, respectively. Evaluation of ML algorithms revealed that all models performed well for plant biomass estimation with d values between 0.88 and 0.99. However, the kNN and SVM outperformed all other models for biomass estimation. GMLNET performed better than other models for plant height estimation. Overall, results revealed that UAV imaging and ML models could be effectively used for monitoring plant phenotypic characteristics such as height, yield, and biomass.