Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers

Abstract

In order for a country's economy to grow, agricultural development is essential. Plant diseases, however, severely hamper crop growth rate and quality. In the absence of domain experts and with low contrast information, accurate identification of these diseases is very challenging and time-consuming. This leads to an agricultural management system in need of a method for automatically detecting disease at an early stage. As a consequence of dimensionality reduction, CNN-based models use pooling layers, which results in the loss of vital information, including the precise location of the most prominent features. In response to these challenges, we propose a fine-tuned technique, GreenViT , for detecting plant infections and diseases based on Vision Transformers (ViTs). Similar to word embedding, we divide the input image into smaller blocks or patches and feed these to the ViT sequentially. Our approach leverages the strengths of ViTs in order to overcome the problems associated with CNN-based models. Experiments on widely used benchmark datasets were conducted to evaluate the proposed GreenViT performance. Based on the obtained experimental outcomes, the proposed technique outperforms state-of-the-art (SOTA) CNN models for detecting plant diseases.

Plant phenotyping relevance

植物画像から病害を検出するVision Transformer手法を提案し、ベンチマークデータセットで性能評価しているため、植物病害状態の表現型取得手法が中心です。

Code and data availability

The paper used two public plant leaf image datasets (PlantVillage and DRLI) for its GreenViT disease-detection experiments, with explicit availability links in the Data Availability Statement. No author analysis code or trained model checkpoint is released.