Abstract
Smart farm is a management system that influences farmers in the highest demand for agriculture, which predicts the future of the field instead of the fruit of the field by learning the appropriate machine in your area, which is what the farmers want to produce, the year they want to know. This paper is aimed at the development of an IOT based smart agriculture system which can help and provide all information to farmers from their fields on mobile and Web server; this system is helpful for farmers to increase quality product and preserve the corps. The purpose of the article is to analyze the environmental parameters such as agricultural production zones, annual production rates, and crop location to produce, which influences crop yields and establishes relationships among these parameters. Typically, the farmers are still using traditional methods for monitoring their crop and agriculture field. To meet the end of the growing world needs, the farmers have to use new techniques of monitoring and tracking their crops which in turn help them by improvement in yield, reduction in farming cost, reduction in destruction to the environment, and increase in the quality of the produce. Existing systems and methods of monitoring crops use wireless sensor network to collect data from the different sensors deployed at various nodes and send it through the wireless protocol. In this study, regression analysis (R.A.) is used to stress environmental factors and crop yields. In this search, the random forest classification method for the deduction and precision model used is 87.04%; therefore, improve the accuracy of the prediction of the hybrid model, which is a combination of linear regression and random matching, and 93.72% of the efficiency model.
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Sharma, H., Saini, A., Kumar, A., Bhardwaj, M. (2021). An Improved Machine Learning Model for IoT-Based Crop Management System. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1334. Springer, Singapore. https://doi.org/10.1007/978-981-33-6981-8_45
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