FCN Network-Based Weed and Crop Segmentation for IoT-Aided Agriculture Applications

Abstract

The main purpose of the work is to evaluate the deep machine learning algorithms used for the distinction between weeds and crop plants using the open database of images of the carrot garden. Precision farming methods are highly prevalent in the agricultural environment and can embed intelligent methods in drones and ground vehicles for real-Time operation. In this work, the accuracy of the weed and crop segment is analyzed using two different frameworks of deep learning for the semantic segment: The fully convolutional network and the ResNet. An open database with images of 40 plants and weeds was used for the case study. The results show a global accuracy of more than 90% in the verification package for both structures. In the second experiment, new FCN networks were trained to evaluate the impact of these processes on different image preprocessing and separation performance by different training/testing rates of the dataset.