Abstract: Plant quality and productivity are significantly impacted by plant diseases.Plant disease detection can be done via digital image processing. In the field of digital image processing, deep learning has recently done significantly better than conventional methods. Experts are now particularly interested in the study area of deep learning-based plant disease diagnosis. Plant diseases and pest detection issues are defined in this study, and the effectiveness of the methods is contrasted with conventional ones. This article reviews the current literature on classification networks, detection networks, and segmentation networks for deep learning-based plant disease and pest detection. The advantages and disadvantages of each strategy are examined in light of the variations in network structure. We compare the effectiveness of earlier studies using common datasets.This study investigates potential challenges associated with deep learning-based plant disease and pest identification in practical contexts. Along with potential research directions and resolutions to the problems, a number of recommendations are also provided. Finally, this paper analyses and predicts the potential future directions for deep learning-based plant disease and pest detection.
Works Cited:
Wagh Dhiraj Machhindranath, Deshmukh Shubham Uttam, Wadgaonkar Sainath Sanjay, Yadav Pranav Ashok, Prof. Siddhesh Bandekar " Digital image processing technique for detection and classification of different diseased plant ", IJIREEICE International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 11, no. 10, pp. 4-11, 2023. Crossref https://doi.org/: 10.17148/IJIREEICE.2023.111002
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