Machine Learning based Prediction and Recommendation System for Detection of Pests and Cultivation of Crops

Authors

  • Abhishek Shah Student, Department of Computer Science and Engineering, Sipna College of Engineering and Technology, Amravati, India
  • Samreen Syeda Student, Department of Computer Science and Engineering, Sipna College of Engineering and Technology, Amravati, India

Keywords:

Image processing, Machine learning, Neural networks, Pest prediction

Abstract

Serious infliction is induced to the growth of the crop by the pests resulting in a severe decline of the stock. The fundamental approach to making suitable prevention standards is the speedy and precise prediction of pest damage. The observation process, statistical plan, and mathematical representation are the traditional pest prediction arrangements. In farm areas, the endowment of primary pest forecasting locations, but these techniques have some restrictions because of their own faults, such as an experimental prediction of individual parts is clear, the correctness of the association coefficient utilized is moderate. By applying Machine Learning technology, we can precisely distinguish the appearance of pests and disease in the fields. In current years, some researchers have established the BP artificial neural network (Back Propagation Artificial Neural Network) Compared with the conventional approach of Back Propagation Neural Network which enhances the prediction efficiency and comfort of use, but there are two severe drawbacks: gradual convergence rate and simple to fall into the minimum point. Acknowledging the results of multivariable, time-varying, and unknown factors on insect pests, it is necessary to stabilize a pest model with powerful prediction performance, accuracy, and precision. First, based on the Back Propagation neural network, the existent climate is used.

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Published

19-12-2020

How to Cite

[1]
A. Shah and S. Syeda, “Machine Learning based Prediction and Recommendation System for Detection of Pests and Cultivation of Crops”, IJRESM, vol. 3, no. 12, pp. 84–91, Dec. 2020.

Issue

Section

Articles