Coffee ripeness monitoring using a UAV-mounted low-cost multispectral camera

Rosas, Jorge Tadeu Fim; Pinto, Francisco de Assis de Carvalho; Queiroz, Daniel Marçal de; Villar, Flora Maria de Melo; Valente, Domingos Sárvio Magalhães; Martins, Rodrigo Nogueira

doi:10.1007/s11119-021-09838-3

Cited by 15 publications

(6 citation statements)

References 47 publications

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“…Figure 17 shows the behavior of the spectral bands as presented by Rosas et al [21] on the spectral reflectance in plants. This result validates the radiometric correction and shows that the camera reflectance agrees with what was expected, since for the plants, the NIR band has the lowest absorption and the greatest reflectivity [21].…”

Section: Performance Of Prediction Modelsmentioning

confidence: 97%

“…Cloudy sky conditions were chosen to avoid saturating the camera's NGR channels. The NIR reflectance affects the red channel as established in [21]. Images were taken under cloudy and clear sky conditions to determine the best NDVI results, as shown in Figures 6 and 7.…”

Section: Platform To Acquire Imagesmentioning

confidence: 99%

“…Table 1 presents the summary of the vegetation indices involved in this work. This research was based on the vegetation indices of the results obtained by Rosas et al [21], who carried out spectrometric analysis with the Survey3W camera. All the vegetation indices of the Table 1 are available using the RGN bands of the Survey3 camera.…”

Section: Spectral Descriptorsmentioning

confidence: 99%

“…Regarding the vegetation indices, this research found different behavior. The NDVIMean had a correlation of 49%, the NDVimin and the NDVimax were −39% and 55%, which shows the importance of the NDVI that, according to [21], represents the greenness and the vigor of the plant [34]. The RVI, SAVI, NRVI, and TVI vegetation indices have a correlation of about 45%, so it is essential to take them into account in analyzing crop yield.…”

Section: Predictors Selectionmentioning

confidence: 99%

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

2023

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Crop yield is an important factor for evaluating production processes and determining the profitability of growing coffee. Frequently, the total number of coffee beans per area unit is estimated manually by physically counting the coffee cherries, the branches, or the flowers. However, estimating yield requires an investment in time and work, so it is not usual for small producers. This paper studies a non-intrusive and attainable alternative to predicting coffee crop yield through multispectral aerial images. The proposal is designed for small low-tech producers monitored by capturing aerial photos with a MapIR camera on an unmanned aerial vehicle. This research shows how to predict yields in the early stages of the coffee tree productive cycle, such as at flowering by using aerial imagery. Physical and spectral descriptors were evaluated as predictors for yield prediction models. The results showed correlations between the selected predictors and 370 yield samples of a Colombian Arabica coffee crop. The coffee tree volume, the Normalized Difference Vegetation Index (NDVI), and the Coffee Ripeness Index (CRI) showed the highest values with 71%, 55%, and 63%, respectively. Further, these predictors were used as the inputs for regression models to analyze their precision in predicting coffee crop yield. The validation stage concluded that Linear Regression and Stochastic Descending Gradient Regression were better models with determination coefficient values of 56% and 55%, respectively, which are promising for predicting yield.

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Section: Performance Of Prediction Modelsmentioning

confidence: 97%

Section: Platform To Acquire Imagesmentioning

confidence: 99%

Section: Spectral Descriptorsmentioning

confidence: 99%

Section: Predictors Selectionmentioning

confidence: 99%

See 2 more Smart Citations

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

2023

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“…Various studies have employed remote sensing to assess coffee plants, monitor diseases and water stress, and establish correlations among other biotic and abiotic factors using vegetation indices (VIs) measured by orbital sensors [12][13][14]. Others have utilized multispectral cameras coupled to UAVs to evaluate fruit ripening, seedling mortality, and correlations between field measurements and aerial images [15][16][17][18][19][20][21][22][23]. However, no studies have utilized remote sensing and UAVs to monitor farming practices, specifically the response of coffee seedlings transplanted in different containers.…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Vegetation Indices to Evaluate Coffee Crop Response after Transplanting Seedlings Grown in Different Containers

Barata,

Ferraz,

Bento

et al. 2024

Agriculture

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Brazil stands out among coffee-growing countries worldwide. The use of precision agriculture to monitor coffee plants after transplantation has become an important step in the coffee production chain. The objective of this study was to assess how coffee plants respond after transplanting seedlings grown in different containers, based on multispectral images acquired by Unmanned Aerial Vehicles (UAV). The study was conducted in Santo Antônio do Amparo, Minas Gerais, Brazil. The coffee plants were imaged by UAV, and their height, crown diameter, and chlorophyll content were measured in the field. The vegetation indices were compared to the field measurements through graphical and correlation analysis. According to the results, no significant differences were found between the studied variables. However, the area transplanted with seedlings grown in perforated bags showed a lower percentage of mortality than the treatment with root trainers (6.4% vs. 11.7%). Additionally, the vegetation indices, including normalized difference red-edge, normalized difference vegetation index, and canopy planar area calculated by vectorization (cm2), were strongly correlated with biophysical parameters. Linear models were successfully developed to predict biophysical parameters, such as the leaf area index. Moreover, UAV proved to be an effective tool for monitoring coffee using this approach.

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Robot Operating System Powered Data Acquisition for Unmanned Aircraft Systems in Digital Agriculture

2022

Unmanned Aerial Systems in Precision Agriculture

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Coffee ripeness monitoring using a UAV-mounted low-cost multispectral camera

Cited by 15 publications

References 47 publications

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

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

UAV-Based Vegetation Indices to Evaluate Coffee Crop Response after Transplanting Seedlings Grown in Different Containers

Robot Operating System Powered Data Acquisition for Unmanned Aircraft Systems in Digital Agriculture

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