Apple Disease Detection Using Convolutional Neural Networks


  • A. S. Lalitha, K. Nageswararao


Residual Neural Network (CNN), Deep learning, rotten fruit,Blotch fruit, Normal Fruit, Scab fruit, Epoch


Cultivation of a crop is a pertinent aspect in the agricultural sector. The infection of crops with a disease is one of the bottlenecks leading to reduction in the yield of the crop. This decreases the production rate and thereby creating problems in maintaining the crop throughout the year. Manual identification of the disease is laborious and time consuming. It is laborious in the sense that, it requires lot of expertise and monitoring and this problem is going to be much more, especially, when the crop is big enough to manage.  In order to minimize the effort needed, A deep learning model has been developed to identify   particular disease. Using this model it facilitates the farmer to detect the disease accurately in a real time. The model developed in this research is a Residual Neural Network model as it helps us in implementing the feature extraction and classification of a particular disease of a fruit. A database of 505 apples was considered and 385 apples were used for training the model and 120 apples were considered for testing. The proposed model has generated an accuracy of 78.76% with a loss value of 0.6818 in detecting the disease of an apple. This computer based model will enhance usability of the model in detecting the disease of a fruit in a feasible manner and at an early stage of the disease. This in turn enables the farmer to detect the disease at an early stage of the disease and can take the precautionary measures to cure the disease in a more effortless manner. 


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How to Cite

K. Nageswararao , A. S. L. (2024). Apple Disease Detection Using Convolutional Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 466–470. Retrieved from



Research Article