MAPIR Research Publications

MAPIR offers solutions that are commonly used by customers in the research community. Below you can find a selection of papers using our products (click to view).

Over time we continue to improve our solution so the newer publications may contain better results. Notably our software processing was greatly improved in Q4 2022, and our spectrometer light sensor was introduced in Q4 2023. The use of our calibration targets and ambient light sensor are required for the most accurate results.

(2026) Detection of Foliar Phosphorus in Potato Crops Using Multispectral Imagery

"The findings of this study demonstrate that variations in foliar phosphorus content in potato plants can be detected using multispectral images acquired by the MAPIR Survey3 (RGN) sensor, especially when spectral data are integrated with machine learning algorithms capable of exploring nonlinear relationships among variables."

"Therefore, multispectral images obtained by the MAPIR Survey3 (RGN) sensor represent a viable and promising approach for non-destructive phosphorus monitoring, establishing a foundation for the future development of more robust nutritional diagnostic systems and applications in precision agriculture."

(2026) Shutter Speed Influences the Capability of a Low-Cost Multispectral Sensor to Estimate Turfgrass Vegetation Vigor Under Different Solar Radiation Conditions

"This study revealed that a proper calibration of the low-cost camera allowed it to reach reflectance values comparable to those achievable by the ones provided by more sophisticated sensors (e.g., hyperspectral cameras)."

"The comparison between the reflectance values obtained from the MAPIR camera after reaching the near-constant behavior with the spectral signature derived from the leaf spectrometer revealed a close relationship between them (R2 of 0.89 and RMSE = 0.06)."

(2025) A Comparative Analysis of RG-NIR and Multispectral Camera Imagery Acquired via Unmanned Aerial Vehicles for Sugarcane Crop Detection

"Based on data analysis in reflection and vegetation index research, the MAPIR RGN camera is proven to be superior to the DJI Mavic 3M multispectral camera for detecting sugarcane vegetation. This is based on the higher correlation values between reflectant values, vegetation index, and chlorophyll in the MAPIR RGN camera."

"Based on these findings, the MAPIR RGN camera is recommended for precision agriculture applications in sugarcane plantations, as it provides more accurate spectral data reflecting vegetation health."

(2025) Corn water productivity assessments with aerial camera onboard drone by modelling surface and aerodynamic resistances

"The similarities between our results and those from literature bring confidence in the suitability of estimating ETa by modelling rs and ra in the Penman-Monteith equation from the reflectance values of the Mapir camera onboard a drone together with agrometeorological data."

(2025) Innovative methodology for determining basic wood density using multispectral images and MAPIR RGNIR camera

"Data acquisition was performed using a low-cost multispectral camera (MAPIR Survey 3) equipped with an RGNIR filter (Red-Green-Near Infrared). The selection of this camera was based on its affordability and ease of operation, enabling the collection of multispectral images without the need for complex laboratory infrastructure. Its spectral configuration allows for the capture of wavelengths in the near-infrared region, which are strongly associated with both structural and chemical properties of wood. This setup significantly reduces operational costs and enhances the feasibility of large-scale implementation."

"The proposed methodology, which utilizes a low-cost multispectral imaging system such as the MAPIR Survey3 camera, demonstrates significant potential for large-scale application in the forestry sector. Its affordability and ease of operation eliminate the need for complex laboratory infrastructure, which is a critical factor for field or in situ viability."

(2025) Multispectral images in the monitoring of coffee trees phytotechnical parameters after pruning

"The objective of this work was to monitor coffee plants (Coffea arabica L.) after pruning through multispectral images obtained with an unmanned aerial vehicle (UAV) containing a Mapir Survey 3 camera and estimate agronomic parameters based on simple regression parametric models. [...] Based on the models generated, data analysis permitted estimating coffee plants' agronomic parameters after decote-type pruning with high accuracy. Height was measured in April's flight with the near-infrared band (Precision = 91.87%), crown diameter and plagiotropic branches length in April's flight with the infrared band (Precision = 89.36% and 82.22%, respectively)"

(2025) Planococcus citri in coffee trees by supervised classification using multispectral images

"The images were collected using a Phantom 4 Pro drone equipped with a Mapir Survey 3W camera, flown at a height of 100 m, resulting in a Ground Sample Distance (GSD) of 8 cm, which allowed for the identification of plant-level features essential for infestation detection."

(2025) Viabilidade do Uso de Drones no Monitoramento de Cultivos de Soja: Estudo Comparativo em Jatai-GO

"The performance index D indicates excellent performance of the MAPIR camera for calculating NDVI and LAI in plot 001, with values above 0.88 for all dates... This good performance is corroborated by RMSE values between 0.04 and 0.12, reflecting the precise adjustment of the model."

"The D and RMSE indices demonstrated excellent performance of the MAPIR camera for the calculation of NDVI and LAI in plot 002, with the D index always above 0.88 and the RMSE between 0.05 and 0.10, confirming the good fit of the model."

*(Original language: Portuguese; quotes above are translated from the original)*

(2024) Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV

"The vegetation indices obtained from drone imagery were used to create spatial maps of plant growth. These maps can be used for accurately identifying stressed or infected plant areas, targeting fertilizer, pesticide, and irrigation applications precisely where needed, reducing waste,and increasing efficiency."

(2024) Rice Leaf Nitrogen Content Estimation Through A Methodological Framework Using Single-Sensor Multispectral Images

(2024) Modelling Chlorophyll and Nutrient Contents of Peperomia obtusifolia ‘Green Gold’ Using MAPIR RGN and RGB Sensors

(2024) A Cutting-Edge Precision Agriculture Technology to Support the Sustainable Oil Palm Industry

"The UAV and Mapir camera have potentially been utilized to estimate the N, P, K, and Mg content of the leaves and to monitor the nutritional status of the leaves after fertilizing. By generating data from the field quickly and precisely, crop production inputs can be managed in an environmentally friendly way, which is fundamental to sustainability in oil palm plantations."

(2023) Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia

"The manual data collection process is very expensive, both in terms of time and money.The multispectral image approach to facilitate manual collection of crop yield data is one of the contributions of this research."

(2023) USO DE IMAGENS DE ALTA RESOLUÇÃO ESPACIAL PARA A ANÁLISE QUÍMICA DA UVA

(2023) USE OF HIGH SPATIAL RESOLUTION IMAGES FOR THE CHEMICAL ANALYSIS OF GRAPES

"Os resultados obtidos no presente estudo são promissorese apresentam uma nova forma de avaliação da viticultura nosemiárido, com uso de imagens centimétricas e de técnicascomputacionais no processamento e obtenção de informaçõesuteis para a gestão da produção de frutíferas."

"The results obtained in this study are promising and present a new way of evaluating viticulture in the semi-arid region, using centimetric images and computational techniques in the processing and obtaining of useful information for the management of fruit production."

(2023) Multispectral images for discrimination of sources and doses of fertilizer in coffee plants

"With a higher accuracy to analyze the agronomic parameters, the use of multispectral images for classification and monitoring of the crop constitutes a low-cost option for large-scale, reliable, and constant monitoring of the crop, reaching up to 86.66% of global accuracy in the RGN data classification and vegetation indices using the Randon Forest algorithm."

(2023) Analysis of Vegetation Index of Oil Palm Plants infected with Ganoderma Disease

"All the results of the analysis of NDVI, GNDVI, and SAVI on healthy oil palm plants, consistently have high values and decrease in line with the level of infection..."

(2023) Proportions of Green Area and Tree Health on University Campus: The Impact of Pavement Presence

(2022) Early Detection of Coffee Leaf Rust Caused by Hemileia vastatrix Using Multispectral Images

"This study demonstrates the potential of using multispectral images to monitor early-stage infection of coffee plants by H. vastatrix, the most important pathogen of coffee plants worldwide, using an unmanned aerial vehicle (UAV). The early detection of this pathogen in the field with low-cost technology can be an important tool for the monitoring of coffee leaf rust and, consequently, a more sustainable management of this pathogen, making applications of chemical fungicides only when necessary."