Potato plant disease detection: leveraging hybrid deep learning models.

Abstract

Agriculture, a crucial sector for global economic development and sustainable food production, faces significant challenges in detecting and managing crop diseases. These diseases can greatly impact yield and productivity, making early and accurate detection vital, especially in staple crops like potatoes. Traditional manual methods, as well as some existing machine learning and deep learning techniques, often lack accuracy and generalizability due to factors such as variability in real-world conditions. This study proposes a novel approach to improve potato plant disease detection and identification using a hybrid deep-learning model, EfficientNetV2B3+ViT. This model combines the strengths of a Convolutional Neural Network - EfficientNetV2B3 and a Vision Transformer (ViT). It has been trained on a diverse potato leaf image dataset, the "Potato Leaf Disease Dataset", which reflects real-world agricultural conditions. The proposed model achieved an accuracy of 85.06 % , representing an 11.43 % improvement over the results of the previous study. These results highlight the effectiveness of the hybrid model in complex agricultural settings and its potential to improve potato plant disease detection and identification.

Plant phenotyping relevance

ジャガイモ葉画像から病害状態を推定する深層学習モデルを開発・評価しており、植物病害表現型の取得・分類が研究の中心です。

Code and data availability

The paper explicitly states code availability with a public GitHub repository URL (which is in the allowed list) for the EfficientNetV2B3+ViT potato disease detection model. The two image datasets (Plant Village, Potato Leaf Disease Dataset) are noted as publicly available but no paper-specific URLs are provided in the