Improving the Performance of Convolutional Neural Networks (CNN) in Identification of Agricultural Plant Diseases

Abstract

This study aims to improve the performance of the Convolutional Neural Network (CNN) algorithm in detecting coffee leaf diseases using the Inception V3 architecture. The main challenge of this classification is the subtle visual similarity in color, texture, and symptom patterns between diseases. To overcome this, Inception V3 is implemented because of its superiority in multi-scale feature extraction through convolution factorization which reduces parameters while increasing accuracy. The dataset used consists of 1,120 images, evenly distributed into four classes (three types of diseases and one healthy class, each with 280 images), with a training, validation, and test data split ratio of 896:112:112. As a comparison, a conventional basic CNN architecture consisting of 3 convolution layers (3 X 3, stride 1), 3 max-pooling, and 1 dense layer, trained with the same hyperparameters (Adam optimizer, learning rate 0.001, batch size 32) is used. The experimental results show a significant performance improvement; Model accuracy increased from 74.4% on a standard CNN to 97.0% after integrating Inception V3. The scientific contribution of this research lies in mapping overlapping visual characteristics of coffee diseases through multi-scale feature optimization, which demonstrates that computational efficiency can go hand in hand with accuracy improvements on complex agricultural image datasets. These findings confirm that the Inception V3 architecture provides a robust and efficient solution for automating plant disease diagnosis in the field.

Plant phenotyping relevance

コーヒー葉の病徴を画像から分類するCNN手法の改善・比較が研究の中心であり、植物病害状態の画像ベース表現型推定に該当する。

Code and data availability

The paper describes a coffee leaf disease image classification study using Inception V3 on a 1,120-image dataset, but provides no public URL, repository, or deposit for the dataset, code, or trained model. The dataset is described only as 'a public dataset of 1,120 images of coffee diseases' without any identifier or链接

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