CNN for Image Processing to Detect Weeds Using IOT

Abstract

One of the major issues in today’s agriculture fields is detecting weed plants in between the crops. Weeds consume more water, nutrients, and light compared to crop plants. Being hardy and vigorous in growth habits, they grow way to faster than crops and consume a huge amount of water and nutrients, results causing heavy losses in yields, the process of removal of weeds manually is a difficult job and it requires more manpower. To date, weed removal can’t be automated without manpower. Herbicides play a crucial role in removing the weeds but that leads to soil infertile and later the weeds dominate the field automatically. In solution to reduce the weeds is using herbicide in a higher amount than normal day by day. Usage of herbicides in that amount causes the land infertile. This paper deals with detecting the weeds in the crop using a convolutional neural network, Image processing, and IoT. The weeds in the field and between the crops are detected and removed by using the image processing technique. CNN algorithm is implemented in Matlab software to detect the weed areas in the fields. A robot model is connected to the controller through the motor driver which is also used to carry the camera through the field to detect the weed. The videos and images taken by the camera send to the Matlab and they are trained by using the CNN algorithm and that classifies whether it is a weed or a normal crop. And the necessary instructions send to the Arduino through Zigbee. If the camera detects any weed then the cutter is on 10 seconds to cut the weeds. And the robot model moves further until it finds the next weed. Users can also control the robot model whenever itneeds.