The week's pick
Reseach Article
Survey Paper on Agricultural Dataset for Improving Crop Yield Prediction using Machine Learning Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 46 |
| Year of Publication: 2023 |
| Authors: Atul Tripathi, Bhawani Singh Rathore, Divakar Singh |
10.5120/ijca2023922571
|
Atul Tripathi, Bhawani Singh Rathore, Divakar Singh . Survey Paper on Agricultural Dataset for Improving Crop Yield Prediction using Machine Learning Algorithms. International Journal of Computer Applications. 184, 46 ( Feb 2023), 28-34. DOI=10.5120/ijca2023922571
Abstract
By being able to anticipate crop yields more precisely than what has already been done in the field, we hope to improve precision agriculture. A system that examines a data set that includes crop, cost of cultivation, cost of production, and crop yield from the past, builds a statistical model through learning, and then tries to assist farmers in making accurate and precise decisions about which crops can be grown profitably in the near future can be created with the aid of machine learning techniques and the appropriate optimizations and fine-tuning of the classifying algorithm.As a consequence of this work, the system would have a set of guidelines (referred to as a "Knowledge base," which learns through additional training and data sets) that aid farmers in selecting the crops that are most likely to yield a profit in the current environment. The method used to categories agricultural datasets by crop, area (Quantel/hectar), and production. Here, we investigate our classification algorithms with the aid of the WEKA tool. There is currently a push to transform the vast amounts of agricultural data into various technologies and make them accessible to farmers so they can make better decisions. This survey study investigates the best machine learning algorithms, including Random Tree, J48, Bayes Net, and KStar. We research machine learning methods to uncover relevant data in the agricultural dataset so that we may more accurately forecast crop yields for important crops.
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