This study details the methods and processes used in a system for identifying and classifying diseases in potato crops using image processing. However, due to the diversity of diseases in the potato leaf, the system has also been applied to identify the crop's three most common types of pests. Because illnesses of Downy mildew and bacterial spot are well-known to cause harm to the plant leaf, a benefit of the detection system achieved by image processing emphasizes the need for a design for the early detection of diseases in plants. Methods for disease detection in potato leaves are outlined, including the five core phases involved in identifying and labelling an object within an image, in this case, a diseased potato leaf. In the first phase, a picture of the diseased leaf is acquired; in the final stage, an ANN (Artificial Neural Network) is used to classify the image and determine whether what kind of disease it is present in the potato crop. Finally, the processed results of the disease detection system on the potato leaf are givenimages via means of the Mat lab program

Image processing, potato crop and disease detection