Early prediction of potato tuber diseases using ann classifier Kumar Sanjeev1, Paswan Suneeta2,*, Kumari Ragini3 1Assistant Professor -Cum- Junior Scientist (Computer Application), BAU, Sabour, Bihar 2Suject Matter Specialist (Home Science), KVK, Saharsa, BAU, Sabour, Bihar 3Assistant Professor -Cum- Junior Scientist (Soil Science), BAU, Sabour, Bihar *Corresponding Author Email-suneetapaswan@yahoo.com Online published on 20 December, 2022. Abstract Many plant diseases have distinct visual symptoms, which can be used to identify and classify them correctly. This article presents a potato tuber disease classification algorithm that leverages these distinct appearances and advances in computer vision made possible by machine learning. It is estimated that the major loss occurred in potato yield due to the diseases. In present study, we have collected sample of potato tuberdisease images from crop field and valid dataset. This dataset contains 210 images of potato tuber disease. It has 3 class of sample of Black Scurf, Common Scab and Green Colour. The 76 features are extracted from these images regarding color, texture and area. The extracted features are used to develop a model. The developed model is based on neural network for prediction and classification of potato image samples. The Feed Forward Neural Network (FFNN) Model is used for prediction and classification of unknown tuber disease. The average accuracy of ANN model is achieved 84.60%. Classifier is helpful in early and accurate prediction of the tuber diseases of potato crop. Top Keywords Tuber, Machine learning, Image Processing, Feature Extraction, Classification. Top |