A Utility System for Farmers to Build Decision Support System on Agrometeorological Data Using Machine Learning Algorithms
| Autor: | S. Seema, B J Sowmya, Zaifa Khan, B. Sathvik, K. G. Srinivasa, Aniket Singh |
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| Rok vydání: | 2021 |
| Předmět: |
Decision support system
Computer science business.industry Machine learning computer.software_genre Gross domestic product Field (computer science) Domain (software engineering) Support vector machine Work (electrical) Hyperparameter optimization Data analysis Artificial intelligence business Algorithm computer |
| Zdroj: | Lecture Notes in Networks and Systems ISBN: 9789811597114 |
| DOI: | 10.1007/978-981-15-9712-1_20 |
| Popis: | Agriculture is the foundation of the Indian economy; however, the business is in need of more help than some others due to the rapidly changing environment and physical conditions. This evolution is happening both by the growth of humans and some explorations in agriculture field. The advancement in the area of data analytics and Internet of things also facilitates the growth the domain of data analytics (DA), and Internet of things (IoT) is very much facilitating this growth in the field of agriculture which is directly contributing to the gross domestic product (GDP). The continuous changes in the weather conditions and improper irrigation techniques are the main challenges in the field of agriculture. To solve the issues, farmers should use the benefits of modern tools and techniques. Here we have made an attempt to solve the problems in the field of agriculture by using the machine learning techniques. In our work, an attempt is made to analyse the influences of the different agrometeorological data using machine learning algorithm. Rainfall, humidity and temperature are some of the variables that determine the type of crop. So, the task of prediction of crop type given the factors using support vector machines (SVM) is implemented, and the accuracy of the models is computed. Some of the critical parameters of SVM are tuned and applied grid search to improve the accuracy. |
| Databáze: | OpenAIRE |
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