Exploiting Convolutional Neural Network for Cognizance of Diseases in Potato Plants

– Numerous plant diseases are the main cause of the reduction in agricultural yield. Early disease identification and crop prevention are the most difficult aspects of increasing crop output and eradicating disease-induced losses in plants throughout growth. Pest-infested plants and crops have an effect on the nation’s agricultural output. To find and recognize illnesses, farmers and other experts typically monitor the plants closely. But this process is often expensive, time-consuming, and inaccurate. Therefore, it is a wise choice that the villagers or farmers can make to prevent more losses. The study focuses on image processing methods for diagnosing different plant diseases. Here, we employ an effective convolutional neural network (CNN) approach that can identify the different types of leaf illnesses. The implementation procedures in our suggested study involve obtaining datasets, training, segmenting, extracting features, testing, and classifying using CNN to classify the leaves which are diseased or healthy based on data. This work implemented in giving the input leaf in real-time from the source of Google or dataset is trained under the system helps in disease detection and represents remedies for overcoming the deficiency. The way to identify a plant illness is to search for a spot on the leaves of the affected plant. This research aims to develop a disease recognition model backed by categorization of leaf images. We are using image processing with a convolution neural network (CNN) to identify plant illnesses. Convolutional neural networks, or CNNs, are a type of artificial neural network used in image recognition that are designed particularly to analyze pixel input. Convolutional neural networks, or CNNs, are a valuable tool in the identification of diseases affecting potato plants. By analyzing images of potato plant leaves or stems, CNNs can accurately detect and classify various diseases, giving farmers early warnings and precise diagnostic information. This technology allows farmers to implement targeted treatments and interventions, reducing the need for broad-spectrum chemical applications. Consequently, CNNs contribute to sustainable agriculture practices, protect crop yields, and promote efficient resource management in the farming industry.