Abstract: Citrus fruits and leaves are susceptible to a range of diseases that can significantly impact agricultural yield and quality. Traditional methods for disease detection rely heavily on manual inspection, which is both time-consuming and prone to human error. This paper presents a machine learning approach to automate the detection of diseases in citrus fruits and leaves. By leveraging computer vision and deep learning techniques, we develop a model that can classify and identify symptoms of various diseases from images. The approach involves preprocessing image data, extracting relevant features, and training a convolutional neural network (CNN) on a dataset of labelled images. Our model demonstrates high accuracy and efficiency in identifying disease symptoms, offering a scalable solution for early detection and management. The results indicate that integrating machine learning into disease monitoring systems can enhance precision, reduce labour costs, and improve overall crop health management.
Keywords: Citrus fruits, Disease detection, Machine learning, Computer vision, Deep learning, Convolutional neural network (CNN), Image preprocessing, Feature extraction, Accuracy, Crop health management.
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DOI:
10.17148/IJARCCE.2024.13840
