Deep Learning for Detecting and Classifying The Growth Stages of Weeds on Fields

Abstract

Due to the current and anticipated massive increase of world population, expanding the agriculture cycle is necessary for accommodating the expected human’s demand. However, weeds invasion, which is a detrimental factor for agricultural production and quality, is a challenge for such agricultural expansion. Therefore, controlling weeds on fields by accurate, automatic, low-cost, environment-friendly, and real-time weeds detection technique is required. Additionally, automating the process of detecting, classifying, and counting of weeds per their growth stages is vital for using appropriate weeds controlling techniques. The literature review shows that there is a gap in the research efforts that handle the automation of weeds’ growth stages classification using DL models. Accordingly, in this thesis, a dataset of four weed (Consolida Regalis) growth stages was collected using unnamed aerial vehicle. In addition, we developed and trained one-stage and two-stages deep learning models: YOLOv5, RetinaNet (with Resnet-101-FPN, Resnet-50-FPN backbones), and Faster R-CNN (with Resnet-101-DC5, Resnet-101-FPN, Resnet-50-FPN backbones) respectively. Comparing the results of all trained models, we concluded that, in one hand, the Yolov5-small model succeeds in detecting weeds and classifying the weed’s growth stages in the shortest inference time in real-time with the highest recall of 0.794 and succeeds in counting the instances of weeds per the four growth stages in real-time with counting time of 0.033 millisecond per frame. On the other hand, RetinaNet with ResNet-101-FPN backbone shows accurate and precise results in the testing phase (average precision of 87.457). Even though the Yolov5-large model showed the highest precision value in classifying almost all weed’s growth stages in training phase, Yolov5-large could not detect all objects in tested images. As a whole, RetinaNet with ResNet-101-FPN backbone shows accurate and high precision, while Yolov5-small has the shortest real inference time of detection and growth stages classification. Farmers can use the resulted deep learning model to detect, classify, and count weeds per growth stages automatically and as a result decrease not only the needed time and labor cost, but also the use of chemicals to control weeds on fields.