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Advancements in Crop Yield Prediction Using Deep Learning Algorithms
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Author(s): A. Ajina (M.S. Ramaiah Institute of Technology, India), K. G. Jaya Christiyan (M.S. Ramaiah Institute of Technology, India), L. Sunithbabau (M.S. Ramaiah Institute of Technology, India), Rajesh Natarajan (University of Technology and Applied Sciences, Oman)and Vishal Nandyal (M.S. Ramaiah Institute of Technology, India)
Copyright: 2025
Pages: 20
Source title:
Expert Artificial Neural Network Applications for Science and Engineering
Source Author(s)/Editor(s): Lingala Syam Sundar (Prince Mohamamd Bin Fahd University, Saudia Arabia), Deepanraj Balakrishnan (Prince Mohammad Bin Fahd University, Saudi Arabia)and Antonio C.M. Sousa (University of Aveiro, Portugal)
DOI: 10.4018/979-8-3693-7250-0.ch008
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Abstract
Agriculture is at a critical crossroads due to the growing problems brought about by changing environmental conditions, particularly the effects of global warming and climate uncertainty. There has never been a greater urgent need for agricultural techniques to develop and adapt. Machine learning can also help farmers identify the most profitable crops to plant based on market demand and environmental factors. By analyzing historical market data and weather patterns, machine learning models can predict the demand for different crops and suggest optimal planting times and locations. Crop yield prediction is one of the challenging problems in precision agriculture, and many models have been proposed and validated, so crop yield prediction models can estimate the actual yield reasonably, but a better performance in yield prediction is still desirable. This study aims to investigate the potential of deep learning algorithms, such as convolutional neural networks (CNN), long short-term memory networks (LSTM), and deep neural networks (DNN), as revolutionary tools.
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