Abstract
Agriculture is essential for human life on earth, providing food and income for people. Many diseases attack various fruits and crops. Appletree leaves are also susceptible to a variety of pathological conditions that affect their production which are Mosaic, Frogeye, etc. Whenever an apple crop is damaged by infections that harm apple production and the country's wealth, image processing is recommended for detecting apple leaf diseases. This approach permits an effective distinction between infected and non-infected apple leaves. Frequently, individuals tried to notice those ailments with gazes. Sometimes, people deliver bad decisions because the leaves look identical. In this way, they get false results as well as delays in achieving. They cannot expect the results on time. Moreover, manpower is required to discover these leaf diseases with the eye. Our paper provides the CNN model and an algorithm to detect such diseases in crop leaves. The sample we created was trained to analyze and understand the diseased leaf and then detect the leaf disease. We are using the Inception v3 algorithm.
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Acknowledgements
I thank my guide, Dr. G.Krishna Mohan, for supporting and helping me with the successful completion of this project.
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Rishitha, T., Krishna Mohan, G. (2023). Apple Leaf Disease Prediction Using Deep Learning Technique. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_19
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DOI: https://doi.org/10.1007/978-981-19-6004-8_19
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