Comparative Analysis of Potato Leaf Disease Detection Using Convolutional AEVGG19 Model Based on Deep Learning

Abstract

Identifying leaf diseases beforehand is essential for both production and early prevention in contemporary agriculture. Early diagnosis of potato leaf spot is difficult to complicated symptoms of crop diseases and interactions with climatic conditions. Conventional methodologies of plant disease detection are labour-intensive, time-consuming, and require a great deal of experience. Scholars, researchers, and administrators have recently focused a great deal of emphasis on the crucial field of autonomous plant disease identification. Various deep learning and computer vision methods have emerged to detect potato leaf diseases. They also often involve complicated efforts and are subject to inaccuracy. This study develops a convolutional AEVGG19 network and compares it with existing approaches on different performance assessment metrics, including accuracy, F1-score, specificity, and sensitivity. The suggested approach achieves an astounding 98.36% training accuracy in accurately detecting illnesses, while maintaining a validation accuracy of 94%. By offering a reliable and effective method for the early detection of leaf diseases, this research advances agricultural technology and helps to safeguard crops and increase productivity.

Plant phenotyping relevance

ジャガイモ葉の病徴を画像から深層学習で検出する手法を開発・比較しており、植物の病害状態の表現型取得が研究の中心である。

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