Remotely Piloted Aircraft System and Machine Learning for Detection of Coffee Plants Subjected to Foliar Application of Chitosan - Research

Aug 5, 2026

Chitosan, described as a biostimulant, can influence plant physiological responses under water deficit and function as an antitranspirant in agricultural stress. The study uses images collected by Remotely Piloted Aircraft Systems (RPAS) combined with machine learning to distinguish coffee plants treated with foliar chitosan from untreated plants. Spectral data from RPAS-acquired imagery were classified with a random forest model across two coffee cultivars over 2021–2022, using a 3DR SOLO platform and subsequent GIS and statistical analysis.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Introduction

Chitosan properties and agricultural relevance

Materials and Methods

RPAS image acquisition and processing

Machine learning classification approach

Results and Discussion

Model performance for detection of treated plants

Article Information

Publication and article metadata

European Journal of Remote Sensing ISSN: 2279-7254 (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/tejr20)[www.tandfonline.com/journals/tejr20](homepage: www.tandfonline.com/journals/tejr20) Remotely piloted aircraft system and machine learning for detection of coﬀee plants subjected to foliar application of chitosan Nicole Lopes Bento, Gabriel Araújo e Silva Ferraz, Mir ian 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 Beccio lini To cite this article: Nicole Lopes Bento, Gabriel Araújo e Silva Ferraz, Mir ian De Lourdes Oliveira, Alisson André Vicente Campos, Milene Alves de Figueiredo Carvalho, Dalyse Toledo Castanheira, Ana Cristina de Souza, Tulio de Paula Pi res, Giuseppe Rossi & Valentina Beccio lini (2025) Remotely piloted aircraft system and machine learning for detection of coﬀee plants subjected to fo liar application of c hitosan, European Journal of Remote Sensing, 58: 1, 2476632, DOI: 10. 1080/22797254 .2025.2476632 To link to this article: [https://doi.org/10.1080/22797254.2025.2476632](https://doi.org/10.1080/22797254.2025.2476632) © 2025 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group. | Published online: 11 Mar 2025. | | | --- | --- | | | Submit your article to this journal | | | Article views: 317 | | | View related articles | | View Crossmark data | | Full Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=tejr20](https://www.tandfonline.com/action/journalInformation?journalCode=tejr20) EUROPEAN JOURNAL OF REMOTE SENSING 2025, VOL. 58, NO. 1, 2476632 [https://doi.org/10.1080/22797254.2025.2476632](https://doi.org/10.1080/22797254.2025.2476632) Remotely piloted aircraft system and machine learning for detection of coffee plants subjected to foliar application of chitosan Nicole Lopes Bentoa, Gabriel Araújo e Silva Ferraz a, Mirian De Lourdes Oliveiraa, Alisson André Vicente Camposb, Milene Alves de Figueiredo Carvalhoc, Dalyse Toledo Castanheirab, Ana Cristina de Souzab, Tulio de Paula Piresb, Giuseppe Rossi d and Valentina Becciolinid a Department of Agricultural Engineering (DEA), School of Engineering (EENG), Federal University of Lavras-UFLA, Lavras, MG, Brazil; bDepartment of Agriculture (DEA), Federal University of Lavras-UFLA, Lavras, MG, Brazil; cDepartment of Agricultural Engineering (DEA), Embrapa Café, Distrito Federal, Lavras, MG, Brazil; dDepartment of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy ABSTRACT 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

FAQs

What is the main goal of the study on coffee plants?

To differentiate between coffee plants subjected to foliar application of chitosan and those not subjected to it, using RPAS spectral data and machine learning classification.

Which machine learning method is used for classification?

A random forest (RF) classifier is applied to classify coffee plants based on spectral information extracted from RPAS-acquired images.

How were the RPAS images collected and processed?

Images were acquired with a 3DR SOLO aircraft equipped with a Parrot Sequoia sensor, processed in PIX4D Mapper, and analyzed using QGIS and RStudio.

Aug 5, 2026

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