Automated Plant Disease Detection Using Convolutional Neural Networks: Enhancing Accuracy and Scalability for Sustainable Agriculture
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Abstract
The agricultural sector faces significant challenges owing to crop diseases, resulting in economic losses and reduced yields. Traditional manual inspection methods for disease detection are subjective, time-consuming, and inadequate for meeting the growing global food demand. This study aims to develop an automated, scalable, and accurate plant disease detection system using Convolutional Neural Networks (CNNs). Utilizing the PlantVillage dataset with over 54,000 images of various crops and diseases, the model employed convolutional layers for feature extraction, pooling layers for spatial dimension reduction, and fully connected layers for classification. Data augmentation techniques, such as rotation and flipping, enhance model robustness. The performance of the system, evaluated through accuracy, precision, recall, and F1-score, demonstrates a detection accuracy of up to 97%, outperforming traditional methods and other models, such as Artificial Neural Networks (ANNs). However, the accuracy of the model was influenced by environmental conditions, indicating the need for further refinement. The study concluded that CNNs offer an effective solution for early and real-time plant disease detection, potentially reducing crop loss by up to 30% annually and improving yields by 20-25%. Integration into cloud-based platforms can make the system accessible to farmers and promote sustainable agricultural practices. Future work will focus on improving adaptability to diverse crops and conditions, using transfer learning and incorporating additional data sources to enhance prediction accuracy.
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