Comprehensive wheat lodging detection after initial lodging using UAV RGB images

Abstract

Crop lodging in agricultural fields is one of the major factors that limit cereal crop yields. Wheat, the most popular cereal crop in most countries, is also affected by this phenomenon, which may result in a significant decrease in both yield and quality. Therefore, addressing wheat lodging is crucial for producers. This study aims to detect and identify wheat lodging through aerial images and classify its severity based on ratio, angle and location of the lodging. To achieve this goal, a multi-task approach was proposed involving three phases. First, automatic dataset generation methodology was conducted on orthomosaic imagery of three dates. Next, a comprehensive assessment of wheat lodging (ratio, angle and location) was performed, which has received little research attention. Third, applying and improving selected classification models for classifying image datasets was conducted. Combining convolutional neural networks and temporal sequences in a single model provided an opportunity to use spatiotemporal information extracted from the wheat image datasets. Time dependency and individual dates were both considered in the classification task. The limited number of data and imbalanced classes challenges, resulting from real field conditions data collection, were overcome by applying a new loss function to the classifier models. The overall accuracy of wheat lodging classification reached over 91% in these two states using the proposed approach. Based on this research, wheat lodging was detected more accurately by the proposed models despite the small and imbalanced dataset. The developed methodology paves the way for comprehensive and automatic wheat lodging detection, and the methodology can be adapted for similar crops that suffer lodging issues with suitable modifications.