Real-Time Machine-Learning Based Crop/Weed Detection and Classification for Variable-Rate Spraying in Precision Agriculture

Abstract

Traditional agrochemical spraying techniques often result in over or under-dosing. Over-dosing of spray chemicals is costly and pose a serious threat to the environment, whereas, under-dosing results in inefficient crop protection and thereby low crop yields. Therefore, in order to increase yields per acre and to protect crops from diseases, the exact amount of agrochemicals should be sprayed according to the field/crop requirements. This paper presents a real-time computer vision-based crop/weed detection system for variable-rate agrochemical spraying. Weed/crop detection and classification were performed through the Random Forest classifier. The classification model was first trained offline with our own created dataset and then deployed in the field for testing. Agrochemical spraying was done through application equipment consisting of a PWM-based fluid flow control system capable of spraying the desired amounts of agrochemical directed by the vision-based feedback system. The results obtained from several field tests demonstrate the effectiveness of the proposed vision-based agrochemical spraying framework in real-time.