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Spectral characterization of coffee cultivars after pruning may help predict the crop's diverse phytotechnical behaviors. Therefore, this study aimed to differentiate coffee genotypes based on leaf reflectance following pruning and to develop models capable of estimating the agronomic parameters of vegetative growth. This study included eight coffee cultivars. The biochemical and leaf structure parameters were quantified for the spectral characterization of each genotype and adopted as the basis for the application of vegetation indices and to generate models that can estimate agronomic parameters after pruning. The cultivar Topázio MG-1190 proved to be the most efficient at absorbing electromagnetic radiation in the blue (220–510 nm), green (520–590 nm), and red (690–730 nm) spectral ranges among all other cultivars after pruning. The regression models could estimate the following vegetative growth parameters of the coffee cultivars: height using the normalized difference water index (root mean square error (RMSE) = 5.78%), crown diameter and plagiotropic branch length using the enhanced vegetation index (RMSE = 10.21% and 12.69%, respectively), the number of nodes using the chlorophyll b index (RMSE = 14.47%), chlorophyll a content using the photochemical reflectance index (RMSE = 2.39%), and leaf water potential using the leaf water vegetation index-1 (40.72%). The hyperspectral characterization of coffee cultivars with respect to the phytotechnical development of the genotypes after pruning was successfully accomplished. This tool can assist coffee growers to enhance their competitiveness and productivity through the development of management strategies such as the early detection of plant growth issues. [ABSTRACT FROM AUTHOR]

Image-based spectral models assist in estimating the yield of maize. During the vegetative and reproductive phenological phases, the corn crop undergoes changes caused by biotic and abiotic stresses. These variations can be quantified using spectral models, which are tools that help producers to manage crops. However, defining the correct time to obtain these images remains a challenge. In this study, the possibility to estimate corn yield using multispectral images is hypothesized, while considering the optimal timing for detecting the differences caused by various phenological stages. Thus, the main objective of this work was to define the ideal phenological stage for taking multispectral images to estimate corn yield. Multispectral bands and vegetation indices derived from the Planet satellite were considered as predictor variables for the input data of the models. We used root mean square error percentage and mean absolute percentage error to evaluate the accuracy and trend of the yield estimates. The reproductive phenological phase R2 was found to be optimal for determining the spectral models based on the images, which obtained the best root mean square error percentage of 9.17% and the second-best mean absolute percentage error of 7.07%. Here, we demonstrate that it is possible to estimate yield in a corn plantation in a stage before the harvest through Planet multispectral satellite images. [ABSTRACT FROM AUTHOR]

The application of biological products in agricultural crops has become increasingly prominent. The growth-promoting bacterium Azospirillum brasilense has been used as an alternative to promote greater yield in maize crops. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring the effect of the application of products that increase the yield of corn crops. The objective of this work was to evaluate the potential of Kriging techniques and spectral models through images in estimating the gain in yield of maize crop after applying A. brasilense. Analyses were carried out in two commercial areas treated with A. brasilense. The results revealed that models of yield prediction by Kriging with a high volume of training data estimated the yield gain with a root-mean-square error deviation (RMSE%), mean absolute percentage error (MAPE%), and R2 to be 6.67, 5.42, and 0.88, respectively. For spectral models with a low volume of training data, yield gain was estimated with RMSE%, MAPE%, and R2 to be 9.3, 7.71, and 0.80, respectively. The results demonstrate the potential to map the spatial distribution of productivity gains in corn crops following the application of A. brasilense. [ABSTRACT FROM AUTHOR]

The coffee crops are exposed to different pathogens that directly affect yield. These include nematodes, which attack the roots of plants and compromise their physiological development. Given the losses caused by this pathogen and the lack of information on spatial distribution in infested areas, it is important to adopt technologies that enable crops under different management systems to be monitored during their growth cycle. The remote sensing associated with machine learning algorithms is presented as a potential tool for monitoring agricultural crops. The present study assesses different machine learning algorithms, using radiometric values of multispectral images as input datasets, and identifies the best algorithms, to estimate the physiological agronomic parameters in coffee crops submitted to 11 treatments for nematode management. Based on the association between the images taken by a low-cost camera (bands: (R) red, (G) green and (B) blue) mounted on a remotely piloted aircraft (RPA), machine learning algorithms (Random Forest (RF) and support-vector machines (SVM)), the results made it possible to estimate with satisfactory accuracy (root mean square error (RMSE) less than 26.5% the main physical parameters of coffee plants: chlorophyll, plant height, branch length, number of branches and number of nodes per branch. With Planet satellite-derived multispectral bands, the SVM algorithm estimated plant canopy diameters with an RMSE of 7.74%. Based on the spatial distribution maps of the physical parameters, the application machine learning methods offered an opportunity to better use remote sensing data for monitoring coffee crop growth conditions and accurately guiding several management techniques. [ABSTRACT FROM AUTHOR]

The coffee plant is one of the main crops grown in Brazil. However, strategies to estimate its yield are questionable given the characteristics of this crop; in this context, robust techniques, such as those based on machine learning, may be an alternative. Thus, the aim of the present study was to estimate the yield of a coffee crop using multispectral images and machine learning algorithms. Yield data from a same study area in 2017, 2018 and 2019, Sentinel 2 images, Random Forest (RF) algorithms, Support Vector Machine (SVM), Neural Network (NN) and Linear Regression (LR) were used. Statistical analysis was performed to assess the absolute Pearson correlation and coefficient of determination values. The Sentinel 2 satellite images proved to be favorable in estimating coffee yield. Despite the low spatial resolution in estimating agricultural variables below the canopy, the presence of specific bands such as the red edge, mid infrared and the derived vegetation indices, act as a countermeasure. The results show that the blue band and green normalized difference vegetation index (GNDVI) exhibit greater correlation with yield. The NN algorithm performed best and was capable of estimating yield with 23% RMSE, 20% MAPE and R² 0.82 using 85% of the training and 15% of the validation data of the algorithm. The NN algorithm was also more accurate (27% RMSE) in predicting yield. [ABSTRACT FROM AUTHOR]

Coffee farming has high land use value, which can result in economic losses without proper land use planning. Intercropping has improved coffee production by providing an alternative income source to producers, especially in the crop formation phase. The objective of this study was to evaluate productivity, growth, and geospatial data in different soybean plant populations intercropped with coffee. The experiment was conducted at the Federal University of Uberlândia, in Monte Carmelo, Minas Gerais, Brazil. It had an experimental randomized block design with five treatments: the control (no soybeans); 80 thousand plants ha−1; 160 thousand plants ha−1; 240 thousand plants ha−1; and 320 thousand plants ha−1. Productive and vegetative characteristics of coffee and soybeans were evaluated, as well as the NDVI and GNDI vegetation indices. The mass of 1000 grains of soybean reached its peak (178.96 g) with a population of 222 thousand plants of soybean ha−1. The maximum soybean productivity of 102.78 bags ha−1 was obtained in the population of 185 thousand plants soybean ha−1. An increasing population of 240–320 thousand plants ha−1 soybean between coffee rows reduced soybean yield due to reduced light, causing plant lodging. The biometric parameters of the coffee trees did not change, which was evidenced by high normalized and green normalized difference vegetation indices (NDVI and GNDVI, respectively). Therefore, it is concluded that the use of soybeans between the coffee trees does not affect the vegetative and productive parameters of the coffee tree, making the use of the intercrop viable. [ABSTRACT FROM AUTHOR]

Currently, estimations of strawberry productivity are conducted manually, which is a laborious and subjective process. The use of more efficient and precise estimation methods would result in better crop management. The objective of this study was to assess the performance of two regression algorithms-Linear Regression and Support Vector Machine—in estimating the average weight and number of fruits and the number of leaves on strawberry plants, using multispectral images obtained by a remotely piloted aircraft (RPA). The experiment, which was conducted in the experimental area of the Botany Laboratory at the Federal University of Uberlândia-Monte Carmelo Campus (Universidade Federal de Uberlândia, Campus Monte Carmelo), was carried out using a randomized block design with six treatments and four replications. The treatments comprised six commercial strawberry varieties: San Andreas, Albion, PR, Festival, Oso Grande, and Guarani. Images were acquired on a weekly basis and then preprocessed to extract radiometric values for each plant in the experimental area. These values were then used to train the production prediction algorithms. During the same period, data on the average fruit weight, number of fruits per plant, and number of leaves were collected. The total fruit weight in the field was 48.08 kg, while the linear regression (LR) and Support Vector Machine (SVM) estimates were 48.04 and 43.09 kg, respectively. The number of fruits obtained in the field was 4585, and the number estimated by LR and SVM algorithms was 4564 and 3863, respectively. The number of leaves obtained in the field was 10,366, and LR and SVM estimated 10,360 and 10,171, respectively. It was concluded that LR and SVM can estimate strawberry production and the number of fruits and leaves using multispectral unmanned aerial vehicle (UAV) images. The LR algorithm was the most efficient in estimating production, with 99.91% accuracy for average fruit weight, 99.55% for the number of fruits and 99.94% for the number of leaves. SVM exhibited 89.62% accuracy for average fruit weight, 84.26% for the number of fruits, and 98.12% for the number of leaves. [ABSTRACT FROM AUTHOR]

This study aimed to detect soft rot caused by Pectobacterium carotovorum subsp. carotovorum in lettuce using images obtained by multispectral sensors mounted on an unmanned aerial vehicle (UAV). A secondary objective was to identify the best sensor and determine the optimal stage after inoculation to detect infected plants. In the field, soft rot lesions and the agronomic traits of lettuce plants inoculated or not with the bacteria were assessed on different days after inoculation (DAI). Classifications were made using the Support Vector Machine (SVM) and Naive Bayes (NB) algorithms to analyze data groups consisting of spectral bands, vegetation indices and a combination of bands and indices obtained from a conventional visible camera and Mapir Survey3W multispectral camera, as well as agronomic parameters. The results confirmed the possibility of pre-symptomatic detection of P. carotovorum subsp. carotovorum in lettuce at the canopy level. With respect to identifying healthy and infected lettuce plants by supervised classification, the best results were obtained at 4 and 8 DAI, especially when using the subsets derived from the Mapir Survey3W camera (RGN sensor), for both classifiers. The subsets obtained with the conventional visible sensor (RGB sensor) produced the best results at 20 and 24 DAI. [ABSTRACT FROM AUTHOR]

Conventional methodology in the field for the sampling of coffee leaf rust, caused by Hemileia vastatrix, has proven to be impractical. This paper proposes a method for the early detection of this disease, which is the most significant pathogen of coffee plants worldwide, using multispectral images acquired using a Mapir Survey3W camera and an unmanned aerial vehicle (UAV). For this purpose, 160 coffee seedlings of the coffee cultivar 'Mundo Novo' were inoculated with urediniospores of H. vastatrix and compared with 160 control (non-inoculated) seedlings to determine the most favorable interval for distinguishing healthy and infected plants. The 320 seedlings were placed on a dark surface to perform the imaging flights. In vitro analyses of the physiological parameters of 20 specimens were then performed for each condition (inoculated/non-inoculated) to obtain the hyperspectral curves, and this process was repeated three times at 15, 30, and 45 days after inoculation (DAI). Based on the simulated hyperspectral curves, a discrepancy between the red and near-infrared (NIR) bands was identified at 15 DAI, with the inoculated plants showing greater absorption in the red band and a greater spectral response in the NIR band. Thus, multispectral images were able to distinguish H. vastatrix infection in coffee seedlings at an asymptomatic stage (15 DAI) using a support vector machines (SVM) algorithm. Detection accuracy was 80% and the Kappa index of agreement was moderate (0.6). The early detection of this pathogen in the field using low-cost technology can be an important tool for the monitoring of coffee leaf rust and, consequently, a more sustainable management of the pathogen, causing farmers to make applications of chemical fungicides only when necessary. [ABSTRACT FROM AUTHOR]