AUTOMATED WEED REMOVAL SYSTEM USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weed herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control system. The system consists of motors, Raspberry pi and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Deep learning is used to analyze the relevant features from the agricultural images. The dataset is trained for the classification of weed and crop. Therefore, by the use of technology, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.