Machine vision detection of pests, diseases, and weeds: A review

Chiranjeevi Muppala,Velmathi Guruviah

doi:10.25081/jp.2020.v12.6145

Chiranjeevi Muppala, Velmathi Guruviah

Open Access

https://doi.org/10.25081/jp.2020.v12.6145

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Abstract

Most of mankind’s living and workspace have been or going to be blended with smart technologies like the Internet of Things. The industrial domain has embraced automation technology, but agriculture automation is still in its infancy since the espousal has high investment costs and little commercialization of innovative technologies due to reliability issues. Machine vision is a potential technique for surveillance of crop health which can pinpoint the geolocation of crop stress in the field. Early statistics on crop health can hasten prevention strategies such as pesticide, fungicide applications to reduce the pollution impact on water, soil, and air ecosystems. This paper condenses the proposed machine vision relate research literature in agriculture to date to explore various pests, diseases, and weeds detection mechanisms.

Highlights

Agriculture plays an important role in procuring food security, soothe poverty and bolster development
Machine vision is a potential technique for surveillance of crop health which can pinpoint the geolocation of crop stress in the field
The machine vision systems (MVS) can automate crop inspection with the help of in-situ and ex-situ imaging techniques to improve overall crop yield. They can predict the problems in the crop more precisely by analyzing information acquired from the images

Summary

Introduction

Agriculture plays an important role in procuring food security, soothe poverty and bolster development. The world’s population is expected to reach 9.7 billion in 2050 and 11.2 billion by the end of this century [1], so food production must increase despite various crop yield affecting factors like pests, weeds, pathogens, nutrients, water, sunlight, soil degradation, environmental impacts, and sparse arable land. This review paper concentrates on various machine vision techniques proposed for identifying pests, diseases, and weeds in the agriculture field. MVS has great potential in identifying natural resources, precision farming, product quality assessment, sorting, classification and so forth. They can recognize the color, shape, size, and texture of an object and can find the point of interest from them. MVS can capture invisible lights such as ultraviolet, IR, and NIR, which render better information regarding crop health [3]

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