AI Article Synopsis

  • Several diseases are impacting tomato production in Florida, particularly affecting fresh markets and causing significant losses.
  • A study used a portable spectral sensor to analyze tomato leaves at various disease stages, including healthy and early stages, to assess detection feasibility.
  • The results showed that healthy leaves could be accurately classified, while late-stage diseased leaves were easier to identify than early-stage diseased ones, suggesting potential for developing an image monitoring system for tomato plants.

Article Abstract

Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato leaves in different stages, including early or asymptomatic stages. One healthy leaf and three diseased tomato leaves (late blight, target and bacterial spots) were defined into four stages (healthy, asymptomatic, early stage and late stage) and collected from a field. Fifty-seven spectral vegetation indices (SVIs) were calculated in accordance with methods published in previous studies and established in this study. Principal component analysis was conducted to evaluate SVIs. Results revealed six principal components (PCs) whose eigenvalues were greater than 1. SVIs with weight coefficients ranking from 1 to 30 in each selected PC were applied to a K-nearest neighbour for classification. Amongst the examined leaves, the healthy ones had the highest accuracy (100%) and the lowest error rate (0) because of their uniform tissues. Late stage leaves could be distinguished more easily than the two other disease categories caused by similar symptoms on the multi-diseased leaves. Further work may incorporate the proposed technique into an image system that can be operated to monitor multi-diseased tomato plants in fields.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5809472PMC
http://dx.doi.org/10.1038/s41598-018-21191-6DOI Listing

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