Crop and weed detection using image processing and deep learning techniques

Artificial intelligence, specifically deep learning, is a fast-growing research field today. One of its various applications is object recognition, making use of computer vision. The combination of these two technologies leads to the purpose of this thesis. In this project, a system for the identification of different crops and weeds has been developed as an alternative to the system present on the FarmBot company’s robots. This is done by accessing the images through the FarmBot API, using computer vision for image processing, and artificial intelligence for the application of transfer learning to a RCNN that performs the plants identification autonomously. The results obtained show that the system works with an accuracy of 78.10% for the main crop and 53.12% and 44.76% for the two weeds considered. Moreover, the coordinates of the weeds are also given as results. The performance of the resulting system is compared both with similar projects found during research, and with the current version of the FarmBot weed detector. Form a technological perspective, this study presents an alternative to traditional weed detectors in agriculture and open the doors to more intelligent and advanced systems