XAI-Based SmartAgriGo: An Intelligent Agriculture Framework for Transparent Crop Recommendation and Plant Disease Detection

Abstract

Agriculture in India is challenged due to inappropriate crop selection, climate change, soil nutrient imbalance, and late identification of plant diseases. To overcome these problems, this paper proposes SmartAgriGo, an Explainable Artificial Intelligence (XAI)-based smart agriculture framework for transparent crop recommendation and automated plant dis-ease identification. The proposed framework combines machine learning, and explainable AI for accurate and interpretable agricultural decision support. Crop recommendation is done based on soil nutrients, pH, temperature, humidity and rainfall, where XLNet-based feature extraction and Support Vector Machine (SVM) classification identify the best-suited crop. Plant disease identification is done based on Convolutional Neural Network (CNN) and Softmax classification of leaf images. To improve interpretability, SHAP values are used for crop recommendation, and LIME values are used for disease identification.The interface designed for farmers shows the prediction results with confidence and explanation. SmartAgriGo fills the gap between state-of-the-art AI approaches and real-world agriculture by providing accurate, interpretable, and data-driven agricultural support.

Plant phenotyping relevance

葉画像からCNNで植物病害を自動識別する手法が、農業支援フレームワークの主要構成として明示されており、植物の病害状態を画像から推定する中央的な方法貢献がある。

Code and data availability

The paper describes a proposed SmartAgriGo framework (SVM/Random Forest crop recommendation, CNN disease detection, SHAP/LIME) but provides no public dataset, image, code, model, or supplement with availability language or URLs. Datasets are only vaguely referenced as 'government-approved' sources without identifiers.

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