Hybrid Deep Learning Technique for Leaf Disease Detection System
Pratham Kumar Adarsh Maurya Siddhant Singh Rawat
Plants play a vital part in providing nourishment in all inclusive. Different natural components cause plant illnesses, resulting in significant generation misfortunes. However manually identifying of plant diseases may be an expensive and time-consuming process. Embracing improved innovations in a machine learning and deep learning can help to resolve these challenges by allowing early detection of plant illness. The later advancements in the use of ML and Deep Learning procedures for the recognizable proof of plant illnesses are investigated in this paper. The study addresses the distributions from 2016 to 2023, and the tests covered in this article show that, using these techniques can be effective to increase the accuracy and skill of plant disease detection. This article also address the challenges and constraints, which are associated with using machine learning and deep learning for plant disease detection ,such as problem with information accessibility, imaging quality issues, and differentiating between both healthy and sick plants. All papers are examined in detail in terms of ML and deep learning data sets, architectures, efficiency metrics, so that we can combine and make the deep learning model which have the accurate accuracy of leaf disease. We hope that this study will be a valuable resource for researchers who work on the detection of plant diseases and insect pests.
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Quantam Computing Publication