Vision Transformers in Optimization of AI-Based Early Detection of <i>Botrytis cinerea</i>

Abstract

Detecting early plant diseases autonomously poses a significant challenge for self-navigating robots and automated systems utilizing Artificial Intelligence (AI) imaging. For instance, Botrytis cinerea, also known as gray mold disease, is a major threat to agriculture, particularly impacting significant crops in the Cucurbitaceae and Solanaceae families, making early and accurate detection essential for effective disease management. This study focuses on the improvement of deep learning (DL) segmentation models capable of early detecting B. cinerea on Cucurbitaceae crops utilizing Vision Transformer (ViT) encoders, which have shown promising segmentation performance, in systemic use with the Cut-and-Paste method that further improves accuracy and efficiency addressing dataset imbalance. Furthermore, to enhance the robustness of AI models for early detection in real-world settings, an advanced imagery dataset was employed. The dataset consists of healthy and artificially inoculated cucumber plants with B. cinerea and captures the disease progression through multi-spectral imaging over the course of days, depicting the full spectrum of symptoms of the infection, ranging from early, non-visible stages to advanced disease manifestations. Research findings, based on a three-class system, identify the combination of U-Net++ with MobileViTV2-125 as the best-performing model. This model achieved a mean Dice Similarity Coefficient (mDSC) of 0.792, a mean Intersection over Union (mIoU) of 0.816, and a recall rate of 0.885, with a high accuracy of 92%. Analyzing the detection capabilities during the initial days post-inoculation demonstrates the ability to identify invisible B. cinerea infections as early as day 2 and increasing up to day 6, reaching an IoU of 67.1%. This study assesses various infection stages, distinguishing them from abiotic stress responses or physiological deterioration, which is crucial for accurate disease management as it separates pathogenic from non-pathogenic stress factors. The findings of this study indicate a significant advancement in agricultural disease monitoring and control, with the potential for adoption in on-site digital systems (robots, mobile apps, etc.) operating in real settings, showcasing the effectiveness of ViT-based DL segmentation models for prompt and precise botrytis detection.

Keywords

• botrytis cinerea
• deep learning
• cucumber
• early detection
• cut-and-paste
• image segmentation