Identification of Tobacco Crop Based on Machine Learning for a Precision Agricultural Sprayer

Abstract

Agrochemicals, which are very efficacious in protecting crops, also cause environmental pollution and pose serious threats to farmers’ health upon exposure. In order to cut down the environmental and human health risks associated with agrochemical application, there is a need to develop intelligent application equipment that could detect and recognize crops/weeds, and spray precise doses of agrochemical at the right place and right time. This paper presents a machine-learning based crop/weed detection system for a tractor-mounted boom sprayer that could perform site-specific spraying on tobacco crop in fields. An SVM classifier with a carefully chosen feature combination (texture, shape, and color) for tobacco plant has been proposed and 96% classification accuracy has been achieved. The algorithm has been trained and tested on a real dataset collected in local fields with diverse changes in scale, orientation, background clutter, outdoor lighting conditions, and variation between tobacco and weeds. Performance comparison of the proposed algorithm has been made with a deep learning based classifier (customized for real-time inference). Both algorithms have been deployed on a tractor-mounted boom sprayer in tobacco fields and it has been concluded that the SVM classifier performs well in terms of accuracy (96%) and real-time inference (6 FPS) on an embedded device (Raspberry Pi 4). In comparison, the customized deep learning-based classifier has an accuracy of 100% but performs much slower (0.22 FPS) on the Raspberry Pi 4.