A Novel Hybrid Technique for Detecting and Classifying Hyperspectral Images of Tomato Fungal Diseases Based on Deep Feature Extraction and Manhattan Distance.

Guifu Ma, Seyed Mohamad Javidan, Yiannis Ampatzidis, Zhao Zhang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Guifu MaResearch Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China.ORCID 0000-0002-6081-444X

Seyed Mohamad JavidanDepartment of Biosystems Engineering, Tarbiat Modares University, Tehran 14115-111, Iran.ORCID 0000-0003-0447-8645

Yiannis AmpatzidisAgricultural and Biological Engineering Department, Southwest Florida Research and Education Center, University of Florida, 2685 FL-29, Immokalee, FL 34142, USA.ORCID 0000-0002-3660-3298

Zhao ZhangKey Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, China.ORCID 0000-0001-6353-4067

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate and early detection of plant diseases is essential for effective management and the advancement of sustainable smart agriculture. However, building large annotated datasets for disease classification is often costly and time-consuming, requiring expert input. To address this challenge, this study explores the integration of few-shot learning with hyperspectral imaging to detect four major fungal diseases in tomato plants:

Indexed as

Hyperspectral ImagingPlant DiseasesSolanum lycopersicumAlternariaBotrytisDeep LearningFusariumdeep feature extractionearly disease detectionhyperspectral imagesone-shot and few-shot learningprecision agriculturetomato fungal diseases

Identifiers

PMID40732413

DOI10.3390/s25144285

PMCPMC12300726

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.