Implementing Machine Learning For Smart Farming To Forecast Farmer’s Interest in Hiring Equipment
Yash Sawarbande1, Anushka Shelke2, Roshan Dehankar3, Swaraj Rawate4, Gaurav Sawale5
1Student, Computer Science & Engineering Department, PRMIT&R, Badnera 2Student, Computer Science & Engineering Department, PRMIT&R, Badnera 3Student, Computer Science & Engineering Department, PRMIT&R, Badnera 4Student, Computer Science & Engineering Department, PRMIT&R, Badnera
5Assistant Professor, Computer Science & Engineering Department, PRMIT&R, Badnera.
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Abstract - agriculture is the backbone of Indian frugality. In this farming sector, there's a lot of fieldwork, similar as weeding, reaping, sowing etc. these operations preliminarily were done by traditional outfit’s.Working with that outfit’s was tedious and laborious, also traditional ways are time consuming. robotization in farming made husbandry easier and quick. There are variety of machines are available for nearly every task in farming. Beginning with preparing land to the harvesting of crop and farther process can be done by machines. The husbandry ministries that are used now days are premium and cannot be swung by utmost of planter with pastoral background. utmost of the growers in India enjoy veritably small pieces of land and retaining these premium machines may not be doable for them. piecemeal from this utmost of growers consider the traditional ways of husbandry as primary styles. Considering above mentioned factors there's need to develop such a system which will recommend and suggest essential accoutrements on rent to ameliorate husbandry.
In this forum, we proposed a two way decision support system using which growers will get needed accoutrements recommendations for hiring the accoutrements to ameliorate husbandry and on the other hand outfit proprietor will get analytics report about registered growers and their demand. In this forum we proposed decision tree algorithm to develop decision support system. In our proposed system there will be 3 druggies; admin, planter and outfit proprietor.
Key Words: Technological innovations; Smart Farming; Equipments Hiring; Equipment Recommendation; K-means Clustering; Decision Tree.
