Deep learning framework for timely detection and classification of chili leaf diseases and pests.

Abstract

Food security continues to be a significant challenge the world over, with crop production becoming increasingly threatened by crop diseases and pest infestations. In the case of chili production, farmers often suffer significant yield loss and economic insecurity due to the unpredictable nature of both of these problems. Current pest control options (agrochemical and organic methods alike) have not reliably been enough for timely and effective control and demonstrate the importance of early and effective pest and disease identification processes. To solve this problem, the present work describes a deep learning implementation that leverages advanced YOLO-based architectures for the detection and classification of leaf diseases and pest on chili plant leaves. The present work trains and evaluates three models (YOLOv5, YOLOv7, and YOLOv8) on a newly created and balanced dataset of over 28,800 images combining 20 total classes of pest and leaf diseases. The dataset was supplemented by preprocessing the images and conducting an augmentation process to create a total of 32,000 images for training to generate reliable models. The results from the experiments found that YOLOv8 provided the best baseline performance of 95.1% mean Average Precision (mAP), while YOLOv5 had an mAP of 86.1%, and YOLOv7 had an mAP of 67.5%. An additional enhancement in the construction of a Modified YOLOv8 hybrid model—reflecting all advantages of YOLOv5, YOLOv7, and YOLOv8—achieved a highest mAP of 99.5% to be the most effective model in this study, the results suggest that the newly proposed Modified YOLOv8 framework, is highly accurate and reliable for the early detection of pests and diseases in chili, and is helpful to improve sustainable agricultural practices, mitigate crop losses, and increase global food security.

Plant phenotyping relevance

チリ葉の病害を画像から検出・分類する深層学習手法の開発、比較評価、データセット構築が研究の中心であり、植物の病害状態を直接推定するため対象範囲に該当する。

Code and data availability

The paper describes a custom chili leaf disease/pest image dataset (8,000 images, 20 classes, augmented to 32,000) and YOLO-based models, but only states a future intent to share the dataset ('Our plans include making the dataset publicly available') with no public URL, repository, or identifier. No author analysis代码,

No evidence-backed public reproduction asset is currently recorded.