Thursday, 18 Jul, 2024




Crops Management Analysis

International Journal of Emerging Trends in Science and Technology

Volume 8 Issue 1

Published: 2022
Author(s) Name: Vikram R. and Hishore | Author(s) Affiliation: M.Kumarasamy College of Engineering, Karur, Tamil Nadu, India.
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India is developing at a fast speed as is the utilization of innovation in the developing areas of the country. A significant mass of the populace is as yet reliant and rehearsing horticulture as its essential type of revenue. India has been in a persistent tryst with its cultivating infra, rehearses and related networks since freedom. Price Prediction, these days, has turned into a vital agrarian issue which is to be addressed just in light of the accessible inform ation. The point of this paper is to anticipate the yield cost for the following revolution. This work depends on observing appropriate information models that aides in accomplishing high exactness and over over-simplification for price prediction, Yield predic tion and fertilizer recommendation. For taking care of this issue, different Machine learning strategies were assessed on various information sets. This work presents a framework which involves information examination strategies to foresee the cost of the yield. The proposed framework will apply AI calculations and foresee the cost of the harvest in view of different factors like Area collected, Area planted and so on. This furnishes a rancher with knowledge of what the future cost of the yield that he will gather. Along these lines, the paper fosters a framework by incorporating information from different sources, information investigation, expectation examination which can assist with foreseeing the objective cost of the yield and increment the benefit edg es of rancher helping them over a more drawndrawn-out run. The total examination comes up to a resolution that XGBoost and Gradient boosting algorithm is the reasonable strategy for our task.

Keywords: Privacy Data analysis, predictions, SVM, KNN, XGBoost alg orithm, Gradient boosting algorithm

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