Assessing the health degree of winter wheat under field conditions for precision plant protection by using UAV imagery

Widespread infestation of pests and pathogens during winter wheat’s heading stage poses significant risks to yield loss. In this study, an assessment model of health degree (HD) of winter wheat under field conditions was established by using unmanned aerial vehicle remote sensing (UAV RS) imagery. Firstly, non-photosynthetic features were identified from the UAV RS imagery based on different machine learning methods, including Minimum Distance (MD), Maximum Likelihood Estimation (MLE), and Support Vector Machine (SVM). Classification results indicated that MD demonstrates the best performance, according to the values of Overall Accuracy (0.898), Kappa Coefficient (0.863), and Precision (0.856). Therefore, the inversion model between the proportion of pixels classified as non-photosynthetic features and the corresponding ground truth of the incidence of non-photosynthetic features was established. Coefficient of determination (R²), RMSE (root mean square error), and RRMSE (Relative RMSE) of the inversion model are 0.73, 4.86%, and 19.81%, respectively, demonstrating strong correlation and high accuracy. Subsequently, an assessment model for HD of the wheat field was generated based on the predicted incidence of the non-photosynthetic features, and the conclusion was reached that HD1 (pre-symptoms of the infestation of pests and pathogens) dominated in the wheat field, with the proportion of area as 56.16%, while HD4 and HD5 (severe infestation of pests and pathogens) were negligible, with proportions of area of 2.29% and 17.75%. Finally, the assessment model of HD was used to simulate the precision OSMP (One-Spray-Multiple-Protection), and the agricultural chemical could be reduced to 69.11% of the conventional OSMP operation, which provides theoretical and methodological support for the reduction of agricultural chemicals in the domain of precision agriculture.