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.

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.

A Spodoptera frugiperda (Smith) (lagarta-do-cartucho) é a principal praga do milho, com a intensificação da agricultura, os cultivos sucessivos possibilitam maior infestação pela praga. Isso levou ao surgimento de populações resistentes a inseticidas e culturas transgênicas. Pesquisas de campo para iniciar tratamentos com inseticidas são demoradas e exaustivas. Pensando em agilidade e qualidade, neste trabalho foi utilizado uma aeronave remotamente pilotada (ARP) equipado com uma câmera RGB e uma câmera MAPPIR 3 para capturar imagens de uma lavoura de milho, com o objetivo de estimar o índice de área foliar (IAF) de um talhão infestado por S. frugiperda. Durante o ciclo da cultura do milho foram realizadas várias avaliações: determinação do índice de área foliar (IAF), severidade do ataque da praga e, voos para aquisição das imagens. Modelos radiométricos para estimativa do IAF foram obtidos a partir de modelos de regressão linear compostos pelas bandas que melhor correlacionaram com os parâmetros medidos. Os resultados obtidos demonstraram eficiência e maior precisão na estimativa do IAF para o modelo radiométrico composto pela a banda do infravermelho próximo na câmera MAPPIR 3. Nesta ocasião, o RMSE calculado foi de 885,0714 cm². Use of images for attack detection of Spodoptera frugiperda in corn under function of loss of foliar area A B S T R A C TSpodoptera frugiperda (Smith) (cartridge-caterpillar) is the main pest of maize, with the intensification of agriculture, the successive crops allow greater infestation by the pest. This has led to the emergence of populations resistant to insecticides and transgenic crops. Field research to begin treatments with insecticides is time consuming and exhaustive. Thus, thinking of agility and quality, in this work was used a remotely piloted aircraft (ARP) equipped with an RGB camera and a MAPPIR 3 camera to capture images of a maize crop with the objective of estimating the leaf area index (LAI) of a field infested by S. frugiperda. The radiometric models for the estimation of LAI were obtained from linear regression models composed by the bands that best correlated with the measured parameters. The obtained results demonstrated efficiency and greater accuracy in the estimation of the LAI for the radiometric model composed by the near infrared band (IVP) in the MAPPIR 3 camera. On this occasion, the RMSE calculated was 885.0714 cm².Keywords: Corn; Cartridge Caterpillar; Remotely Piloted Aircraft.

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.

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.

Este trabalho objetivou analisar inconsistências topológicas nos dados do Cadastro Ambiental Rural (CAR) para o município de Barão de Melgaço – MT, apresentando sugestões que visem minimizar os erros. Foram utilizados dados vetoriais da plataforma SICAR e o complemento verificador de topologia do software QGIS para análise das classes: área do imóvel e vegetação nativa. As regras topológicas utilizadas foram: informações de uma mesma camada não deveriam se sobrepor umas às outras; inexistência de geometrias inválidas e; informações não duplicadas. Também foi realizada uma comparação entre a área do município e a área total dos imóveis cadastrados. Como resultado foram detectados 400 erros topológicos na classe área do imóvel e 196 na classe vegetação nativa. Obteve-se uma diferença de 64.153,69 hectares entre a área do município e a área total dos imóveis cadastrados. As sugestões propostas foram: disponibilização das delimitações realizadas pelos confrontantes; análise ágil dos cadastros; integração de dados externos como plataformas gratuitas de imagens com resolução espacial superior as fornecidas no CAR; cadastro realizado por técnico-profissional com Anotação de Responsabilidade Técnica; integração das informações com dados de maior acurácia, como os do Sistema de Gestão Fundiária (SIGEF) e dados oficiais, como os da Infraestrutura Nacional de Dados Espaciais (INDE).