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

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

Early Detection of Magnaporthe oryzae-Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging

Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae-infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host–pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management