Authors - Supriya Narad Abstract - Agricultural economies are predominantly relevant in developing countries, wherein the farmers have to struggle operating under the impact of several constraints posed by crop diseases. Among food crops, the potato is a major one with vulnerable destructive diseases like Early Blight and Late Blight, capable of destroying the yield if detected late. Old methods of visual inspection are time-consuming and sometimes erroneous because of laxity, human error, and lack of expertise. With this research, an automated intelligent disease detection system is devised, making use of image processing and deep learning, Arduino, specifically Convolutional Neural Networks (CNNs). The model was trained using potato leaf images from the Plant Village dataset, which are improved using various preprocessing techniques, including color space conversion, image augmentation, and image resizing. The proposed CNN architecture achieved a high rate of classification accuracy of 97.2% in distinguishing healthy leaves vs. Early blight and Late blight infected leaves. Lightweight, reliable, and fast, it supports implementation on mobile or handheld devices in low-resource environments, thus giving farmers the ability to use them for timely diagnostics. The system has good prospects for scaling up for other crops and disease types in future versions.
Open Zoom