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Abstract

Detecting and classifying diseases in agricultural crops is crucial for ensuring crop health and maximizing yields. In the case of walnut trees, timely identification of fungal diseases is essential to prevent their spread and minimize economic losses. Traditional methods of disease diagnosis rely on visual inspection by experts, which can be time-consuming and prone to human errors. In this study, we propose an intelligent system for the detection and classification of walnut fungi diseases using machine-learning techniques. The system leverages the power of image processing and pattern recognition algorithms to automate the identification process, enabling rapid and accurate diagnosis. The system is trained using a dataset comprising high-resolution images of healthy walnut leaves and leaves affected by various fungal diseases. We extract relevant features from the images and employ state-of-the-art machine learning algorithms, such as back-propagation neural network (BPNN), to learn the complex patterns associated with each disease. To evaluate the performance of our system, we conducted extensive experiments on a real-world dataset, achieving high accuracy rates for disease detection and classification. The system successfully identifies common walnut fungi diseases, including walnut blight, anthracnose, and powdery mildew, with accuracy rates exceeding 90%. Moreover, it exhibits robustness against variations in lighting conditions and leaf orientations. Our results demonstrate the potential of machine learning techniques in revolutionizing disease diagnosis in the agricultural domain. The proposed system offers a cost-effective and scalable solution for farmers and agronomists to monitor the health of walnut trees, detect diseases at an early stage, and apply targeted treatments. By providing accurate and timely diagnoses, this technology can contribute to reducing crop losses and improving overall productivity in walnut cultivation.

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How to Cite
Miracle A, A. (2023). Intelligent Detection and Classification of Walnut Fungi Diseases Using Machine Learning . International Journal of Multidisciplinary Studies and Innovative Research, 11(2), 1412–1426. https://doi.org/10.53075/Ijmsirq/876456788
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