Multiclass Weeds and Crops Classification and Segmentation using Deep Learning

Abstract

The world's population has increased exponentially with the increase in food demand, so it's essential to focus on factors that affect the quality and quantity of food. Some basic natural factors are climate, soil, pests, and weeds. Weeds are unwanted plants in yields that compete with the crops and consume a lot of nutrients that affect the quality and growth of crops. The separation of these toxic weeds is a challenging task because of their color and quantity. Pakistan is an agricultural country, and almost a big part of its economy depends on agriculture. The growth of this department does not match the population growth; the reason behind this is the presence of weeds. Automated technological interventions represent the optimal approach for weed removal. However, notable challenges persist within the technological landscape, including the absence of comprehensive datasets and the need for well-automated systems specifically tailored for crop weed classification and segmentation. Focusing on these issues, most of the researchers are working on different techniques, but the inadequate size of the dataset is still a problem for researchers, so this study performed to generate an efficient large dataset by merging different small datasets on the base to pick only those classes of weed and crops that belong to Pakistan; also an effective system using UNet variations (UNet, Attention UNet, and Attention Residual UNet(ARUNet)) and ResNet where ARUNet has performed very well for segmentation process and especially the Attention mechanism boosted the segmentation process, on the other hand ResNet has performed best for classification process on our dataset (24- classes and 120000 images). The accuracy of 99% achieved for classification process and 95% for segmentation.