ArticleJournal of biosciences2022
Different stages of disease detection in squash plant based on machine learning.
Article in Journal of biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 3 citations in OpenAlex.
- Characteristic wavelength selection for rice blast based on hyperspectral remote sensing and deep convolutional neural networks.Pest management science · 2026Article
- Fungal infections: Classification performance and detectability with machine learning models.Scientific reports · 2026Article
- Prediction of soybean yellow mottle mosaic virus in soybean using hyperspectral imaging.Plant methods · 2025Article
- Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels.Sensors (Basel, Switzerland) · 2023Article
- Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence.Measurement : journal of the International Measurement Confederation · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To increase agriculture production, accurate and fast detection of plant disease is required. Expert advice is needed to detect disease in plants, nutrition deficiencies or any other abnormalities caused by extreme weather conditions. But this process is very tedious, costly, and takes more time. In this paper, hyperspectral imaging and machine learning were used to detect different stages (early, middle, and critical stage) of the powderly mildew disease (PMD) in squash plants. An unmanned aerial vehicle (UAV) was used to collect the data from the field and Locality Preserving Discriminative Broad Learning (LPDBL) was used to distinguish the diseased and healthy plants. In addition, the ability to detect the diseased plant by the proposed method was evaluated using 10 different spectral vegetation indices (VIs). The results show the proposed method detected the disease accurately in the early, middle, and critical stages of the squash plant. The proposed method's performance is compared with six different PMDs under indoor laboratory test and UAV-based field test conditions. The comparison's results show that the LPDBL provides better accuracy in detecting disease in the squash plant.
Indexed as
Identifiers
35092411W4226348928What OpenQuestion holds
Registered trials
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.