Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

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Using Deep Learning for Image-Based Plant Disease Detection

Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.

/pdf/a-review-of-imaging-techniques-for-plant-phenotyping-2gkyv3fqq7.pdf

A review of imaging techniques for plant phenotyping.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1240&context=me_pubs

Machine Learning for High-Throughput Stress Phenotyping in Plants

Lights, camera, action: high-throughput plant phenotyping is ready for a close-up

Field-based phenomics for plant genetics research

http://perso-laris.univ-angers.fr/~chapeau/papers/ceagri2012.pdf

ítienne Belin

Papers

On the use of depth camera for 3D phenotyping of entire plants

Application note: Thermography versus chlorophyll fluorescence imaging for detection and quantification of apple scab