By: Abhijith D.A., Ajay V.G., Adimulam Gayathri, Sai Sushma Shree K.S., and Zeba S. Khan
Agriculture serves as the primary source of livelihood for a significant portion of India’s population, but it faces a myriad of challenges, including climate variability, soil fertility issues, and reliance on traditional farming practices. To address these challenges, researchers are increasingly turning to machine learning (ML) methodologies to predict crop yields more accurately. Studies have delved into the effectiveness of various ML algorithms such as Random Forest, Gaussian Process Regression, and Support Vector Machines. These algorithms analyze factors like weather data, soil parameters, and historical yield data to provide precise predictions. The ultimate goal is to empower farmers with the information they need to make informed decisions about crop selection and agronomic practices, thereby contributing to food security and economic stability in agricultural-dependent regions like India. Furthermore, researchers are exploring techniques like Association Rule Mining and Genetic Algorithms to further refine crop yield prediction models. As climate change continues to impact agricultural productivity, the adoption of advanced technologies and data-driven approaches becomes increasingly crucial for sustainable farming practices. These approaches not only aid in mitigating the effects of climate variability but also ensure food security for India’s growing population. By leveraging ML and other advanced technologies, agriculture in India can transition towards more sustainable practices while maximizing yields. This shift towards data-driven decision-making not only benefits farmers but also plays a vital role in ensuring the overall economic stability and food security of the nation. With continued research and implementation of these innovative techniques, India can navigate the challenges posed by climate change and sustainably meet the food demands of its populace.
Citation:
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