Advancements in Crop Analysis through Deep Learning and Explainable AI

• URL: http://arxiv.org/abs/2508.19307v1
• Date: Tue, 26 Aug 2025 07:37:40 GMT
• Title: Advancements in Crop Analysis through Deep Learning and Explainable AI
• Authors: Hamza Khan,
• Abstract summary: This study proposes an automated approach to classify five rice grain varieties using Convolutional Neural Networks (CNN)<n>The framework combined explainable artificial intelligence (XAI) with deep learning models including CNN, VGG16, ResNet50, and MobileNetV2.
• Score: 1.3198689566654107
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Rice is a staple food of global importance in terms of trade, nutrition, and economic growth. Among Asian nations such as China, India, Pakistan, Thailand, Vietnam and Indonesia are leading producers of both long and short grain varieties, including basmati, jasmine, arborio, ipsala, and kainat saila. To ensure consumer satisfaction and strengthen national reputations, monitoring rice crops and grain quality is essential. Manual inspection, however, is labour intensive, time consuming and error prone, highlighting the need for automated solutions for quality control and yield improvement. This study proposes an automated approach to classify five rice grain varieties using Convolutional Neural Networks (CNN). A publicly available dataset of 75000 images was used for training and testing. Model evaluation employed accuracy, recall, precision, F1-score, ROC curves, and confusion matrices. Results demonstrated high classification accuracy with minimal misclassifications, confirming the model effectiveness in distinguishing rice varieties. In addition, an accurate diagnostic method for rice leaf diseases such as Brown Spot, Blast, Bacterial Blight, and Tungro was developed. The framework combined explainable artificial intelligence (XAI) with deep learning models including CNN, VGG16, ResNet50, and MobileNetV2. Explainability techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) revealed how specific grain and leaf features influenced predictions, enhancing model transparency and reliability. The findings demonstrate the strong potential of deep learning in agricultural applications, paving the way for robust, interpretable systems that can support automated crop quality inspection and disease diagnosis, ultimately benefiting farmers, consumers, and the agricultural economy.

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