| Autor: |
Gadipe, Sunitha, Mucha, Swetha, Poladi, Supraja, Thota, Sruthi, Mahesh, Akarapu, Tallapally, Sampath Kumar, Reddy, I. Rajasri |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 2971 Issue 1, p1-13, 13p |
| Abstrakt: |
Rural India's economy is mostly based on agriculture and its related industries. Agriculture has a big impact on the country's GDP (GDP). A blessing in disguise, the country's agricultural sector is a boon. In compared to worldwide norms, the yield per hectare of crops is disappointing. This might explain why marginal farmers in India commit suicide at greater rates than their non-farming counterparts. An easy-to-use yield forecast method for farmers is presented in this study Farmers will be able to connect to the planned system through a smartphone application. GPS aids in pinpointing a user's exact position. The region and soil type are entered by the user. The most lucrative crop list may be picked using machine learning techniques, or the crop yield of a user-selected crop can be predicted. There are various AI techniques that might be utilized to make forecasts about crop efficiency, including support vector machines, fake brain organizations, arbitrary timberlands, multivariate direct relapses, and K-Nearest Neighbors. The Random Forest was the most accurate, coming in at 95%. In addition, the algorithm recommends when to apply fertiliser to maximise production. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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