Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical Ecosystems
Agroforestry systems can influence the occurrence and abundance of pests and diseases because integrating crops with trees or other vegetation can create diverse microclimates that may either enhance or inhibit their development. This study analyzes the severity of coffee rust in two agroforestry sy...
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MDPI AG
2024-12-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/1/39 |
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| author | Candy Ocaña-Zuñiga Lenin Quiñones-Huatangari Elgar Barboza Naili Cieza Peña Sherson Herrera Zamora Jose Manuel Palomino Ojeda |
| author_facet | Candy Ocaña-Zuñiga Lenin Quiñones-Huatangari Elgar Barboza Naili Cieza Peña Sherson Herrera Zamora Jose Manuel Palomino Ojeda |
| author_sort | Candy Ocaña-Zuñiga |
| collection | DOAJ |
| description | Agroforestry systems can influence the occurrence and abundance of pests and diseases because integrating crops with trees or other vegetation can create diverse microclimates that may either enhance or inhibit their development. This study analyzes the severity of coffee rust in two agroforestry systems in the provinces of Jaén and San Ignacio in the department of Cajamarca (Peru). This research used a quantitative descriptive approach, and 319 photographs were collected with a professional camera during field trips. The photographs were segmented, classified and analyzed using the deep learning MobileNet and VGG16 transfer learning models with two methods for measuring rust severity from SENASA Peru and SENASICA Mexico. The results reported that grade 1 is the most prevalent rust severity according to the SENASA methodology (1 to 5% of the leaf affected) and SENASICA Mexico (0 to 2% of the leaf affected). Moreover, the proposed MobileNet model presented the best classification accuracy rate of 94% over 50 epochs. This research demonstrates the capacity of machine learning algorithms in disease diagnosis, which could be an alternative to help experts quantify the severity of coffee rust in coffee trees and broadens the field of research for future low-cost computational tools for disease recognition and classification |
| format | Article |
| id | doaj-art-4481b0e0fe6e4fdeb538890c62fe7cf4 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-4481b0e0fe6e4fdeb538890c62fe7cf42025-01-10T13:13:29ZengMDPI AGAgriculture2077-04722024-12-011513910.3390/agriculture15010039Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical EcosystemsCandy Ocaña-Zuñiga0Lenin Quiñones-Huatangari1Elgar Barboza2Naili Cieza Peña3Sherson Herrera Zamora4Jose Manuel Palomino Ojeda5Data Science Research Institute, Jaen National University, Jaen 06801, PeruInstituto de Investigación en Estudios Estadísticos y Control de Calidad, Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas 01001, PeruInstituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas 01001, PeruFaculty of Engineering, School of Forestry and Environmental Engineering, National University of Jaen, Jaen 06801, PeruFaculty of Engineering, School of Forestry and Environmental Engineering, National University of Jaen, Jaen 06801, PeruData Science Research Institute, Jaen National University, Jaen 06801, PeruAgroforestry systems can influence the occurrence and abundance of pests and diseases because integrating crops with trees or other vegetation can create diverse microclimates that may either enhance or inhibit their development. This study analyzes the severity of coffee rust in two agroforestry systems in the provinces of Jaén and San Ignacio in the department of Cajamarca (Peru). This research used a quantitative descriptive approach, and 319 photographs were collected with a professional camera during field trips. The photographs were segmented, classified and analyzed using the deep learning MobileNet and VGG16 transfer learning models with two methods for measuring rust severity from SENASA Peru and SENASICA Mexico. The results reported that grade 1 is the most prevalent rust severity according to the SENASA methodology (1 to 5% of the leaf affected) and SENASICA Mexico (0 to 2% of the leaf affected). Moreover, the proposed MobileNet model presented the best classification accuracy rate of 94% over 50 epochs. This research demonstrates the capacity of machine learning algorithms in disease diagnosis, which could be an alternative to help experts quantify the severity of coffee rust in coffee trees and broadens the field of research for future low-cost computational tools for disease recognition and classificationhttps://www.mdpi.com/2077-0472/15/1/39agroforestrydisease assessmentcoffee diseasesconvolutional neural networksAI in agriculturedeep learning |
| spellingShingle | Candy Ocaña-Zuñiga Lenin Quiñones-Huatangari Elgar Barboza Naili Cieza Peña Sherson Herrera Zamora Jose Manuel Palomino Ojeda Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical Ecosystems Agriculture agroforestry disease assessment coffee diseases convolutional neural networks AI in agriculture deep learning |
| title | Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical Ecosystems |
| title_full | Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical Ecosystems |
| title_fullStr | Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical Ecosystems |
| title_full_unstemmed | Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical Ecosystems |
| title_short | Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical Ecosystems |
| title_sort | coffee rust severity analysis in agroforestry systems using deep learning in peruvian tropical ecosystems |
| topic | agroforestry disease assessment coffee diseases convolutional neural networks AI in agriculture deep learning |
| url | https://www.mdpi.com/2077-0472/15/1/39 |
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