EXPLORING THE LANDSCAPE - A COMPREHENSIVE LITERATURE REVIEW ON APPLE PLANT LEAF DISEASE DETECTION APPROACHES USING DEEP LEARNING IN HIMACHAL PRADESH

Chandan Kumar; Vinod Sharma

doi:10.29121/shodhkosh.v5.i6.2024.5533

Authors

Dr. Chandan Kumar Associate Professor, Department of CSE, CP University, Hamirpur, H.P(India)
Vinod Sharma Research Scholar, Department of CSE, CP University, Hamirpur, H.P(India)

DOI:

https://doi.org/10.29121/shodhkosh.v5.i6.2024.5533

Keywords:

CNN, MobileNetV2, ResNet50, AI, Transfer Learning, SVM, PlantVillage

Abstract [English]

Himachal Pradesh is a beautiful hilly state in Northern India. It is known for its rich agriculture, especially apple farming, which is very important to its economy. Early detection of apple leaf diseases is crucial to prevent their spread and maintain the health of apple farms. Plant diseases can harm both the quantity and quality of crops. Recently, Convolutional Neural Networks (CNNs), a type of deep learning method, have been very useful in spotting leaf diseases and have helped farmers a lot. However, most studies conducted so far are general and do not focus on the special climate and farming style of Himachal Pradesh. This study presents a new method for detecting plant leaf diseases using deep learning. It focuses on how these methods can be useful in Himachal Pradesh. These new techniques can help prevent apple leaf diseases early, improve crop production, and improve disease control in the region. This study also aims to support local farmers by providing them with easy-to-use and affordable technology. By combining modern AI tools with traditional farming knowledge, the overall efficiency and sustainability of apple farming can be significantly improved.

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