ArticleScientific reports2018
Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Pre-symptomatic detection of wheat stem rust using hyperspectral imaging and deep learning.Frontiers in plant science · 2026Article
- Transcriptomics Analysis Reveals an Early Response GeneCurrent issues in molecular biology · 2025Article
- Recent advances in plant disease detection: challenges and opportunities.Plant methods · 2025Review
- Hyperspectral Imaging and Machine Learning for Diagnosing Rice Bacterial Blight Symptoms Caused byPlants (Basel, Switzerland) · 2025Article
- Classification of tomato leaf images for detection of plant disease using conformable polynomials image features.MethodsX · 2024Article
- Early detection of verticillium wilt in eggplant leaves by fusing five image channels: a deep learning approach.Plant methods · 2024Article
- Hyperspectral imaging analysis for early detection of tomato bacterial leaf spot disease.Scientific reports · 2024Article
- Article
- Digital Twins in Agriculture and Forestry: A Review.Sensors (Basel, Switzerland) · 2024Review
- Comparison of the Efficiency of Hyperspectral and Pulse Amplitude Modulation Imaging Methods in Pre-Symptomatic Virus Detection in Tobacco Plants.Plants (Basel, Switzerland) · 2023Article
- Early detection of Solanum lycopersicum diseases from temporally-aggregated hyperspectral measurements using machine learning.Scientific reports · 2023Article
- Boosting precision crop protection towards agriculture 5.0Frontiers in plant science · 2023Review
- CRISPR/Cas9-Based Knock-Out of theInternational journal of molecular sciences · 2022Article
- Surveying soil-borne disease development on wild rocket salad crop by proximal sensing based on high-resolution hyperspectral features.Scientific reports · 2022Article
- Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review.Sensors (Basel, Switzerland) · 2022Review
- Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning.Frontiers in plant science · 2022Article
- Divergent abiotic spectral pathways unravel pathogen stress signals across species.Nature communications · 2021Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.PloS one · 2021Article
- Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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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