Abstract
These are various critical aspects that limiting apple quality and productivity, the leaf disease one of them. The usual examination process of leaf disease takes a lot of time to diagnose problems, a majority of farmers lose the ideal time to protect as well as cure diseases. Apple crop is one of the most essential crops on which the global economy lies. Therefore, apple leaf diseases detection is the most important topic of image processing. The most important goal is to figure out how to effectively depict damaged leaf images. Due to climate changes, different types of diseases have been developed. Marssonia left blotch, powdery mildew, fire blight, apple scab, black rot, and frogeye leaf spot are categories of apple leave diseases and different datasets. The manual way to find diseases on leaves are difficult to detect, error rate, and time consuming. The deep learning and segmentation techniques are very helpful for the detection of apple leaf diseases. In this paper, different DL techniques are reviewed and compared with other methods. In advanced techniques, different segmentation and detection techniques of plant leaf disease detection are explained and compared for the analysis.
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Change history
10 August 2023
A correction has been published.
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Bonkra, A., Noonia, A., Kaur, A. (2022). Apple Leaf Diseases Detection System: A Review of the Different Segmentation and Deep Learning Methods. In: Kumar, A., Fister Jr., I., Gupta, P.K., Debayle, J., Zhang, Z.J., Usman, M. (eds) Artificial Intelligence and Data Science. ICAIDS 2021. Communications in Computer and Information Science, vol 1673. Springer, Cham. https://doi.org/10.1007/978-3-031-21385-4_23
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