Potato Leaf Disease Detection Using Machine Learning Techniques for Precision Agriculture

Agriculture is quite significant to the consideration of the economy. Potatoes are among the most important crops together with rice and wheat, but their production is affected because of numerous diseases, which in turn impact the economy. In terms of reducing such diseases, early detection has become essential. A lot of human expertise is required in potato leaf grading and detection as it involves very complex issues. The manual identification of potato diseases involves numerous problems, particularly time consumption, ineffectiveness, and uncertainty. Further, for designing a method for disease detection, certain factors such as reliability, robustness and scalability factors need to be considered. The creation of potential solutions in a variety of industries, including agriculture, has been facilitated by innovation in machine learning and computer vision. In this study, an automated diagnostic system for detecting potato illness in the agriculture sector is developed using machine learning innovation. The experimental results show that the recommended model is successful even under conditions like rotated or flipped photos, zoomed images, or clipped images. Furthermore, we compared the performance of our CNN-based model with an ANN-based model for classifying and identifying various potato diseases. According to the findings, the CNN model performed better than the ANN model in terms of accuracy and robustness. The CNN had a 95% accuracy rate, whereas the ANN had an 81% accuracy rate. The methodology highlights the superiority of CNNs in image-based classification tasks, particularly in the context of potato disease detection.