Crop Recommendation Using Machine Learning

One of the key economic drivers in India is agriculture. Farmers now cultivate crops using lessons learned from the previous century. One of the most crucial elements of farm planning is crop selection. Losses are reduced when farmers are well-informed on the crops that will thrive in their soil and...

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Bibliographic Details
Published in:	2023 International Conference on Data Science and Network Security (ICDSNS) pp. 1 - 5
Main Authors:	C, Ramachandra A, Ankitha, Garre Venkata, Divya, Idupulapati, Vandana, Parimi, Jagadeesh, H S
Format:	Conference Proceeding
Language:	English
Published:	IEEE 28-07-2023
Subjects:	Agriculture crop recommendation Crops Gaussian Naive Bayes Machine Learning Machine learning algorithms Prediction algorithms Random Forest Soil moisture Supply chains Support Vector Machine Support vector machines
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Summary:	One of the key economic drivers in India is agriculture. Farmers now cultivate crops using lessons learned from the previous century. One of the most crucial elements of farm planning is crop selection. Losses are reduced when farmers are well-informed on the crops that will thrive in their soil and climate. Many factors affect crop yield, including specific meteorological conditions and soil characteristics (such as soil N, P, K values, soil moisture, etc.). Various datasets including these traits were gathered and examined. The data analysis process, which evaluates each component of the data using a variety of analyses and logical reasoning, is crucial. Agricultural monitoring and the food business use a variety of models thanks to the development of machine learning algorithms. Post-season data on crop types, however, won't help with crop estimation and monitoring during the growing season. On the other hand, by mapping pre-season crops, early warnings of agricultural production and supply chains can be provided, lowering trade tensions and agricultural hazards. This work analyses and suggests pre-season crop-type maps using a variety of learning algorithms based on past crop-type data. As examples of machine learning algorithms, Support Vector Machines, Random Forest, and Naive Bayes are utilized for computation, the accuracy is calculated and compared. The crop prediction also checked based on the parameters considered for computation.
DOI:	10.1109/ICDSNS58469.2023.10245154