Comparative Analysis of CNN, EFFICIENTNET and RESNET for Grape and Potato leaves Disease Prediction: A Deep Learning Approach.

Abstract

Abstract Grapes and potato, both a crucial component of the global agricultural economy, are susceptible to various diseases that can adversely affect crop quality and yield. Recently, the use of Deep Learning (DL) techniques in agriculture has shown potential for predicting and detecting diseases early. This study explores the effectiveness of Convolutional Neural Networks (CNN), Efficient Net, and Residual Networks (ResNet) in identifying diseases in grape and potata leavess. It employs a database containing high-resolution images of healthy and diseased grape leaves, including conditions like leaf blight, and grape and potata leaves browny mildew. Data pre-processing methods are used to standardize and enhance the datasets for model training and evaluation. The study implements and fine-tunes three DL classifiers—CNN, Efficient Net, and ResNet—using transfer learning. To assess the models' performance in disease classification, the dataset is divided into training and validation subsets. Metrics such as accuracy, recall, precision, and F1-score are used to evaluate the models' predictive capabilities. The experimental results show that CNN achieved 94% accuracy, ResNet attained the highest efficiency with 96% accuracy, and Efficient Net reached 97% accuracy.

Plant phenotyping relevance

ブドウ・ジャガイモ葉の病害状態を画像から分類する深層学習手法を比較・評価しており、植物病害フェノタイピングが中心的な技術課題である。

Code and data availability

The paper uses a self-collected dataset of ~4000 grape and potato leaf images (grape healthy/downy mildew, potato early/late blight, healthy) plus CNN/EfficientNet/ResNet models, but no public deposit of the dataset, images, or code is provided. The Data Availability statement explicitly restricts access to requests to

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