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Enhancing Smart Agriculture Applications Utilizing Deep Learning Models and Computer Vision Techniques
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Author(s): K. Srujan Raju (CMR Technical Campus, India), K. Suneetha (Jain University, India), K. Reddy Madhavi (Mohan Babu University, India), Kondra Pranitha (B.V. Raju Institute of Technology, India), J. Avanija (Mohan Babu University, India)and B. Narendra Kumar Rao (School of Computing, Mohan Babu University, India)
Copyright: 2024
Pages: 15
Source title:
Agriculture and Aquaculture Applications of Biosensors and Bioelectronics
Source Author(s)/Editor(s): Alex Khang (Global Research Institute of Technology and Engineering, USA)
DOI: 10.4018/979-8-3693-2069-3.ch012
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Abstract
The integration of deep learning models and computer vision techniques has created new agricultural possibilities, transforming traditional farming practices into smart and efficient operations. These advanced technologies have enabled farmers to optimise resource utilisation, manage crops effectively, maximise yields, and make informed decisions, resulting in increased crop productivity. One of the main applications of deep learning models is the usage of convolutional neural networks (CNNs) for detecting plant disease. By training on a large dataset containing images of healthy and diseased plants, these models can identify and prevent the spread of diseases among crops, significantly reducing losses. The transfer learning approach involves adapting pre-trained models to agricultural datasets, and improves disease identification capabilities by applying knowledge gained from general image datasets. Deep learning-based models combined with computer vision techniques play a significant role in monitoring crop growth and estimating yields.
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