Remotely piloted aircraft system and machine learning for detection of coffee plants subjected to foliar application of chitosan
Considered a biostimulant, chitosan can affect the physiological responses of plants to water deficit, acting as an antitranspirant under agricultural stress. Currently, images obtained by Remotely Piloted Aircraft Systems (RPAS), together with machine learning techniques, aid in resolving agricultu...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2025.2476632 |
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| author | Nicole Lopes Bento Gabriel Araújo e Silva Ferraz Mirian De Lourdes Oliveira Alisson André Vicente Campos Milene Alves de Figueiredo Carvalho Dalyse Toledo Castanheira Ana Cristina de Souza Tulio de Paula Pires Giuseppe Rossi Valentina Becciolini |
| author_facet | Nicole Lopes Bento Gabriel Araújo e Silva Ferraz Mirian De Lourdes Oliveira Alisson André Vicente Campos Milene Alves de Figueiredo Carvalho Dalyse Toledo Castanheira Ana Cristina de Souza Tulio de Paula Pires Giuseppe Rossi Valentina Becciolini |
| author_sort | Nicole Lopes Bento |
| collection | DOAJ |
| description | Considered a biostimulant, chitosan can affect the physiological responses of plants to water deficit, acting as an antitranspirant under agricultural stress. Currently, images obtained by Remotely Piloted Aircraft Systems (RPAS), together with machine learning techniques, aid in resolving agricultural problems, including water issues. Therefore, the objective of this study was to differentiate between coffee plants subjected to the foliar application of chitosan and those not subjected to it, based on spectral data extracted from RPAS-acquired images and classification via machine learning. For this purpose, the random forest (RF) classifier was applied to two coffee cultivars (Catucaí Amarelo 2SL and Catuaí Vermelho IAC 99) over two years of study (2021 and 2022). The images were obtained by a 3DR SOLO aircraft with a Parrot Sequoia sensor, processed in PIX4D Mapper software and analysed in QGIS and RStudio software. The results showed good performance metrics for differentiating between coffee plants subjected and not subjected to the foliar application of chitosan, indicating that this method is a valid approach for modelling the presence of the biostimulant in coffee plants, thus confirming that the model can efficiently support the practices of precision agriculture. |
| format | Article |
| id | doaj-art-631bdea710a24db2b0bb655cf6e848fe |
| institution | DOAJ |
| issn | 2279-7254 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-631bdea710a24db2b0bb655cf6e848fe2025-08-20T03:05:45ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542025-12-0158110.1080/22797254.2025.2476632Remotely piloted aircraft system and machine learning for detection of coffee plants subjected to foliar application of chitosanNicole Lopes Bento0Gabriel Araújo e Silva Ferraz1Mirian De Lourdes Oliveira2Alisson André Vicente Campos3Milene Alves de Figueiredo Carvalho4Dalyse Toledo Castanheira5Ana Cristina de Souza6Tulio de Paula Pires7Giuseppe Rossi8Valentina Becciolini9Department of Agricultural Engineering (DEA), School of Engineering (EENG), Federal University of Lavras - UFLA, Lavras, MG, BrazilDepartment of Agricultural Engineering (DEA), School of Engineering (EENG), Federal University of Lavras - UFLA, Lavras, MG, BrazilDepartment of Agricultural Engineering (DEA), School of Engineering (EENG), Federal University of Lavras - UFLA, Lavras, MG, BrazilDepartment of Agriculture (DEA), Federal University of Lavras - UFLA, Lavras, MG, BrazilDepartment of Agricultural Engineering (DEA), Embrapa Café, Distrito Federal, Lavras, MG, BrazilDepartment of Agriculture (DEA), Federal University of Lavras - UFLA, Lavras, MG, BrazilDepartment of Agriculture (DEA), Federal University of Lavras - UFLA, Lavras, MG, BrazilDepartment of Agriculture (DEA), Federal University of Lavras - UFLA, Lavras, MG, BrazilDepartment of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, ItalyDepartment of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, ItalyConsidered a biostimulant, chitosan can affect the physiological responses of plants to water deficit, acting as an antitranspirant under agricultural stress. Currently, images obtained by Remotely Piloted Aircraft Systems (RPAS), together with machine learning techniques, aid in resolving agricultural problems, including water issues. Therefore, the objective of this study was to differentiate between coffee plants subjected to the foliar application of chitosan and those not subjected to it, based on spectral data extracted from RPAS-acquired images and classification via machine learning. For this purpose, the random forest (RF) classifier was applied to two coffee cultivars (Catucaí Amarelo 2SL and Catuaí Vermelho IAC 99) over two years of study (2021 and 2022). The images were obtained by a 3DR SOLO aircraft with a Parrot Sequoia sensor, processed in PIX4D Mapper software and analysed in QGIS and RStudio software. The results showed good performance metrics for differentiating between coffee plants subjected and not subjected to the foliar application of chitosan, indicating that this method is a valid approach for modelling the presence of the biostimulant in coffee plants, thus confirming that the model can efficiently support the practices of precision agriculture.https://www.tandfonline.com/doi/10.1080/22797254.2025.2476632Digital agriculturemultispectral imagesprecision coffee growingremote sensing |
| spellingShingle | Nicole Lopes Bento Gabriel Araújo e Silva Ferraz Mirian De Lourdes Oliveira Alisson André Vicente Campos Milene Alves de Figueiredo Carvalho Dalyse Toledo Castanheira Ana Cristina de Souza Tulio de Paula Pires Giuseppe Rossi Valentina Becciolini Remotely piloted aircraft system and machine learning for detection of coffee plants subjected to foliar application of chitosan European Journal of Remote Sensing Digital agriculture multispectral images precision coffee growing remote sensing |
| title | Remotely piloted aircraft system and machine learning for detection of coffee plants subjected to foliar application of chitosan |
| title_full | Remotely piloted aircraft system and machine learning for detection of coffee plants subjected to foliar application of chitosan |
| title_fullStr | Remotely piloted aircraft system and machine learning for detection of coffee plants subjected to foliar application of chitosan |
| title_full_unstemmed | Remotely piloted aircraft system and machine learning for detection of coffee plants subjected to foliar application of chitosan |
| title_short | Remotely piloted aircraft system and machine learning for detection of coffee plants subjected to foliar application of chitosan |
| title_sort | remotely piloted aircraft system and machine learning for detection of coffee plants subjected to foliar application of chitosan |
| topic | Digital agriculture multispectral images precision coffee growing remote sensing |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2025.2476632 |
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