Pilotless aircraft systems will reshape our critical thinking about agriculture. Furthermore, because they can drive a transformative precision and digital farming, we authoritatively review the contemporary academic literature on UAVs from every angle imaginable for remote sensing and on-field management, particularly for sugarcane. We focus our search on the period of 2016–2021 to refer to the broadest bibliometric collection, from the emergence of the term “UAV” in the typical literature on sugarcane to the latest year of complete publication. UAVs are capable of navigating throughout the field both autonomously and semi-autonomously at the control of an assistant operator. They prove useful to remotely capture the spatial-temporal variability with pinpoint accuracy. Thereby, they can enable the stakeholder to make early-stage decisions at the right time and place, whether for mapping, re-planting, or fertilizing areas producing feedstock for food and bioenergy. Most excitingly, they are flexible. Hence, we can strategically explore them to spray active ingredients and spread entomopathogenic bioagents (e.g., Cotesia flavipes and Thricrogramma spp.) onto the field wherever they need to be in order to suppress economically relevant pests (e.g., Diatraea saccharalis, Mahanarva fimbriolata, sugarcane mosaic virus, and weeds) more precisely and environmentally responsibly than what is possible with traditional approaches (without the need to heavily traffic and touch the object). Plainly, this means that insights into ramifications of our integrative review are timely. They will provide knowledge to progress the field’s prominence in operating flying machines to level up the cost-effectiveness of producing sugarcane towards solving the sector’s greatest challenges ahead, such as achieving food and energy security in order to thrive in an ever-challenging world.
The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R2 = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision.
The biophysical characteristics of vegetative canopies, such as biomass, height, and canopy diameter, are of paramount importance for the study of the development and productive behavior of crops. Faced with a scarcity of studies aimed at estimating these parameters, the objective of this study was to evaluate the performance of artificial neural networks (ANNs) applied to Proximal Remote Sensing (PRS) to estimate biophysical characteristics of soybean culture. The data used to train and validate the ANNs came from an experiment composed of 65 plots with 30 x 30 m mesh, its development was carried out in the 2016/2017 crop in the Brazilian agricultural area. The evaluations were carried out at 30, 45, 60, and 75 days after sowing (DAS), monitoring the spatial and temporal variability of the biophysical characteristics of the soybean crop. Vegetation indexes were collected using canopy sensors. The accuracy and precision were determined by the coefficient of determination (R2) and the error of the forecasts by MAPE (Mean Absolute Percentage Error). PRS and ANNs showed high potential for application in agriculture, since they obtained good performance in the estimation of height (R2 = 0.89) and canopy diameter (R2 = 0.96), being fresh biomass (R2 =0.98) and dry biomass (R2 = 0.97) were the best-estimated variables.
Remote sensing tools are helpful in monitoring and managing crop production. However, each remote sensing technology responds to crop variability differently. In this way, the objective of this work was to compare sensors on airborne and orbital platforms and to observe which one has the best quality to determine the behavior of the peanut (Arachis hypogaea L.) crop variability. The experimental design followed the premises of the statistical quality control (SQC), with samples collected over time. The experimental area was composed of 30 sampling points spaced every 50 m. The multispectral images were acquired with an unmanned aerial system (UAS) consisting of a DJI Matrice quad-copter and a Micasense RedEdge multispectral camera and with the PlanetScope multispectral imaging satellites. It was verified that in all periods evaluated for spectral bands and vegetation indices (VI), satellite images presented better process quality. The enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) generated from satellite images were able to detect the peanut maturation variation better. The behavior of the bands and the VIs generated from the Planet images show quality for peanut crop monitoring. While UAS showed sensitivity to detect the saturation of the bands, making it difficult to visualize the temporal variability.
Com o aumento do crescimento populacional e para atender a demanda, é de suma importância procurar a obtenção de maiores produtividades, e um dos fatores que afeta bastante a produtividade são perdas oriundas da colheita, sendo que, as mesmas precisam ser reduzidas quando detectadas. Desta maneira objetivou-se, com este trabalho, quantificar as perdas em função do tipo de armação, durante o recolhimento do amendoim, e comparar qual armação foi a melhor para determinação das perdas. O experimento foi conduzido na safra 2019/2020, no município de Ribeirão Preto, estado de São Paulo. A área experimental foi instalada em sistema de Meiosi. O delineamento experimental foi em faixas contendo 2 tratamentos (armação circular e retangular) e 20 repetições, perfazendo 40 parcelas. Cada parcela possuiu o distanciamento de 80 m de comprimento entre si. A variável analisada foi perda total, que foi obtida pelo o somatório das perdas visíveis e invisíveis, que foi coletada durante o recolhimento. Após a coleta dos dados foi efetuado teste F e quando significativo foi usado o teste de Tukey. Através da análise estatística verificou-se alta variabilidade nos dados de perdas, um fato comum quando se trabalha com perdas e colheita mecanizada, encontrando-se diferença estatística na comparação das perdas das diferentes armações, no qual a armação circular apresentou valores mais confiáveis devido ao fato de possuir uma representatividade maior da área.
Dentre as operações agrícolas, a colheita mecanizada é a etapa final que merece muita atenção por afetar diretamente na produtividade, ou seja, quanto maior for a quantidade de perdas haverá reduções de produtividade e o aumento de custos. Assim análises estatísticas como o Controle Estatístico de Qualidade (CEQ) está sendo aplicado na agricultura e tem demonstrado grande potencial para a melhoria da gestão dos sistemas agrícola bem como nas tomadas de decisão. Com este trabalho, objetivou-se monitorar a qualidade operacional da colheita mecanizada do amendoim, durante o recolhimento, por meio do CEQ, e quantificar as perdas totais com a utilização da armação retangular. O experimento foi realizado, em área comercial, na safra 2019/2020, no município de Ribeirão Preto, estado de São Paulo, localizado nas coordenadas geográficas 21°20'17.55"S e 47°54'7.31"O. O amendoim foi semeado em sistema de Meiosi (Método Inter Ocupacional Simultâneo). O delineamento experimental seguiu as premissas do CEQ, monitorando, ao longo do tempo, 20 pontos amostrais que foram distanciados entre si com 80 m de comprimento. O indicador de qualidade avaliado, durante o recolhimento, foram as perdas totais que foram quantificadas por meio da armação retangular, possuindo as seguintes dimensões 5,4 m de largura por 0,37 m de comprimento. A análise estatística foi executada por meio das ferramentas do CEQ, que foram: cartas de controle de valores individuais, gráficos sequenciais ou run charts e análise descritiva. Concluiu-se que por meio da aplicação das ferramentas de qualidade permitiu o maior acompanhamento e monitoramento da operação, em que não houve presença de causas especiais e nem de padrões de não aleatoriedade.
The objective of this work was to analyze the effects of different organic substrates, fertigated with effluents from biodigesters in the production of coriander. The experiment was carried out in a greenhouse, greenhouse type. It was used a completely randomized design, with three replications, in a 2x2x2 factorial arrangement, being: cultivars (king and tabocas); substrates (natural, fermented) and source of irrigation (water, biofertilizer). The size of the plants were evaluated; number of stems and yield of green mass. No significant effect was observed between the organic substrates and the coriander cultivars for any of the analyzed variables, however, regarding the irrigation source, statistical significance was observed for the variables plant size and green mass yield, and the best means were obtained when the plan ts were irrigated with water. It is concluded that organic substrates and coriander cultivars studied are recommended for cultivation. However, the use of concentrated biofertilizers did not increase coriander production, and more research is needed to recommend the best doses of this biofertilizer.
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