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.

Highlights

  • Tobacco, considered as a cash crop in many countries (e.g., China, Brazil, India, and Pakistan), is a highly agrochemicals dependent crop

  • This paper focuses on the development of VOLUME 9, 2021 a machine learning-based tobacco crop/weed detection and classification system for site-specific spraying

  • The research work presented in this paper deals with the detection of crop and weeds by utilizing RGB cameras mounted on a boom sprayer in a real field

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Summary

Introduction

Tobacco, considered as a cash crop in many countries (e.g., China, Brazil, India, and Pakistan), is a highly agrochemicals dependent crop. Huge amounts of agrochemicals are applied at various times on tobacco over its threemonth growing season to get maximum yield. It has drastic environmental impact when compared to other agricultural cash crops and causes health and socioeconomic problems for populations specially in low-income and middle-income. Tobacco farmers in Pakistan fail to protect themselves from getting exposed to pesticides primarily for two reasons: firstly they are unaware of the risks associated with pesticide exposure due to lack of information, and secondly they still use the conventional rudimentary pest/weed control techniques due to lack of availability

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