Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images
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DOI: 10.1371/journal.pone.0123262
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Cited by:
- Maimunah Mohd Ali & Norhashila Hashim & Samsuzana Abd Aziz & Ola Lasekan, 2022. "Characterisation of Pineapple Cultivars under Different Storage Conditions Using Infrared Thermal Imaging Coupled with Machine Learning Algorithms," Agriculture, MDPI, vol. 12(7), pages 1-17, July.
- Tiago Domingues & Tomás Brandão & João C. Ferreira, 2022. "Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey," Agriculture, MDPI, vol. 12(9), pages 1-23, September.
- Ganbayar Batchuluun & Se Hyun Nam & Chanhum Park & Kang Ryoung Park, 2022. "Super-Resolution Reconstruction-Based Plant Image Classification Using Thermal and Visible-Light Images," Mathematics, MDPI, vol. 11(1), pages 1-22, December.
- Alejandro Pena & Juan C. Tejada & Juan David Gonzalez-Ruiz & Mario Gongora, 2022. "Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach," Sustainability, MDPI, vol. 14(11), pages 1-28, May.
- Aneta Saletnik & Bogdan Saletnik & Grzegorz Zaguła & Czesław Puchalski, 2024. "Raman Spectroscopy for Plant Disease Detection in Next-Generation Agriculture," Sustainability, MDPI, vol. 16(13), pages 1-18, June.
- Ganbayar Batchuluun & Se Hyun Nam & Kang Ryoung Park, 2022. "Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images," Mathematics, MDPI, vol. 10(21), pages 1-18, November.
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