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Detection of pests or insects is a common problem in the field of agriculture. Therefore strong action should be taken to save the crop from the infestation as well as reducing the use of pesticides. Monitoring of pests infestation depends on resources used by human being however, an automatic monitoring system has been developing to reduce efforts and errors made by human. This project proposes the development of system in which image processing approaches are to be establishing for an automated detection and extraction system for detecting pest densities in agriculture fields. We have precisely developed a multipurpose robot, depending upon the densities of infestation robot decides spraying on infected area by sprayer or removes the part of crop by cutter infected by pest with an accuracy of 93% pest detection and 95% of decision making. So, it gives protection to crops, which can finally give better management and production of the crop.

The emerging era of image processing has spread its wings in human life to the extent that image has become an integral part of their life. There are various applications of image processing in the field of e-commerce, engineering, graphics design, journalism, architecture, and historical research. In this research work, image processing is considered for the detection of weed present in chilli farm. The proposed system is an innovative method to detect and spray the herbicides on weeds present in the farm field. Weed present in the area reduces the quantity and quality of the crops and reduces the profit of the farmers. Agriculture is the main income source in India, and nearly 70% of Indians are dependent on the agriculture and related industry. In India, 349 million acres of the area is under cultivation. India is the top seventh net exporting nation across the globe in agricultural goods; thus agriculture plays a key role in Indian Economics. Agriculture is a backbone of human life. We have implemented image processing based technique using MATLAB to detect the weed areas in an image of farm fields.