Improved MobileNetV2 Architecture with Modified Lite Attention Model for Detection of Plant Leaf Disease

Abstract

Global agriculture is seriously threatened by plant diseases, which result in large losses in both productivity and quality. Timely and accurate disease detection is essential for effective crop management and food security. This work presents an improved MobileNetV2 architecture with Modified Lite Attention (MLA) Model for detecting plant leaf disease. Our methodology incorporates pre-processing, feature extraction through attention model, convolution layers, and classifying into diseased or healthy categories. Further, multiclassification of diseases is performed on a dataset comprising 4432 samples including whitefly, leaf spot, leaf curl, yellowish and healthy leaves. The proposed attention model is compared with existing attention models like CBAM (Convolutional Block Attention Model), SE (Squeeze and Excitation), ECA (Efficient Channel Attention) and SDMnet (Spatially Dilated Multi-Scale Network) to validate our hybrid MLA feature extraction technique. Customizing the categorization with fully connected layers and utilisation of a pre-trained MobileNetV2 model allow the system to achieve excellent results. Findings show encouraging accuracy, surpassing 97% compared to existing techniques for multiclass dataset classification. The integration of MobileNetV2 with custom dense layers enables robust detection even with limited datasets, making it ideal for use in mobile or low-resource agricultural environments. Further, the proposed method is tested on the PlantVillage dataset consisting of 10,836 samples using K-Fold cross-validation for K = 5 and K = 4 to obtain an average accuracy of 98.4% and 98.69%, respectively.

Plant phenotyping relevance

植物葉の病徴を画像から分類する深層学習手法を開発し、複数モデルおよびデータセットで精度検証しており、病害状態の表現型取得が中心である。

Code and data availability

The supplied blocks describe a MobileNetV2+MLA chili leaf disease classifier trained on a Kaggle-compiled dataset and PlantVillage, but contain no authors' public code, trained model checkpoints, or paper-specific dataset deposit. The Kaggle dataset is cited as a source rather than released by the authors, and no code-

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