Este artigo apresenta e discorre sobre os atributos da agricultura de precisão (ap) e da agricultura digital (ad), expondo as particularidades e sinergias de cada uma delas. Explica como a ap vem sendo empregada nos sistemas de produção vegetal e animal em vários países desde a década de 1990, com uma intensidade e abrangência em relação à área e aos tipos de sistemas de produção que a adotam e que evoluem gradualmente. A ap compreende o uso de procedimentos e de equipamentos, implementos e/ou sensores que avaliam a variabilidade espacial e temporal de atributos do solo, planta, animal ou clima, com o intuito de fornecer informações que subsidiam a tomada de decisão pelo produtor ou profissional quanto à realização de uma prática ou manejo agrícola de modo diferenciado ou variável. Em muitas das atividades realizadas dentro do contexto da ap, a coleta, o armazenamento, a análise e a transmissão de dados ou informações sobre solo, planta, animal ou clima de um específico sistema de produção agrícola, são realizadas por hardwares e softwares, os quais se enquadram dentro do contexto de ad. Muitos desses procedimentos também podem ser realizados com diferentes graus de automação, parcial ou total. Termos usualmente utilizados na produção vegetal e animal, como “tecnologias de informação e comunicação”, “conectividade”, “internet das coisas”, “nuvem”, “algoritmo”, “aplicativo”, “base de dados”, entre outros, podem estar relacionados entre si e com a ap e/ou com a ad. No Brasil, como em muitos outros países, a ap e a ad encontram-se em um processo dinâmico de discussão crítica, desenvolvimento, adaptação, validação e aplicação.Palavras-chave: Tecnologias de Informação e Comunicação. Conectividade. Internet das Coisas. Nuvem. Algoritmo. Aplicativo. Base de Dados.
Currently, Brazil is the leading producer of sugarcane in the world, with self-sufficiency in the use of ethanol as a biofuel, as well as being one of the largest suppliers of sugar to the world. This study aimed to develop a predictive model for sugarcane production based on data extracted from aerial imagery obtained from drones or satellites, allowing the precise tracking of plant development in the field. A model based on a semiparametric approach associated with the inverse Gaussian distribution applied to vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI), was developed with data from drone images obtained from two field experiments with randomized replications and four sugarcane varieties. These experiments were performed under conditions identical to those applied by sugarcane farmers. Further, the model validation was carried out by scaling up the analyses with data extracted from Sentinel-2 images of several commercial sugarcane fields. Very often, in countries such as Brazil, sugarcane crops occupy extensive areas. Consequently, the development of tools capable of being operated remotely automatically benefits the management of this crop in the field by avoiding laborious and time-consuming sampling and by promoting the reduction of operation costs. The results of the model application in both sources of data, i.e., data from field experiments as well as the data from commercial fields, showed a suitable level of overlap between the data of predicted yield using VIs generated from drone and satellite images with the data of verified yield obtained by measuring the production of experiments and commercial fields, indicating that the model is reliable for forecasting productivity months before the harvest time.
In this paper we propose a cluster-based approach for the delineation of management zones in precision agriculture. The proposed approach was built following the steps of data mining for the clustering task, resulting in a computer application that generates maps of management zones and yield areas, allowing to compare them using known statistical indexes. The basis for this implementation was a model previously published in the literature that uses only historical productivity, soil electrical conductivity and relief data to generate the maps. The main difference of our work with respect to the previous model is the clustering algorithms used in the step of extracting patterns. While the original model uses only the fuzzy c-means algorithm, the model developed in this study uses the GKCluster extension to this algorithm, able to detect clusters with different geometrical shapes. From the tests performed with the new proposed model, we achieved about 76% of correlation between maps of yield and management zones from kappa index, and about 85% of correlation from overall accuracy. The original model reached, according to the authors, a maximum correlation of 49% from kappa index, and 70% from overall accuracy.
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