Colheita de prescrição para o café, é possível?
Carregando...
Arquivos
Data
2019-07-17
Autores
Kazama, Elizabeth Haruna [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Estadual Paulista (Unesp)
Resumo
O café é uma commodity cujo preço é ajustado conforme parâmetros de qualidade, sendo a colheita uma operação que está intimamente ligada à qualidade final do produto. Sabemos que frutos no estádio cereja apresentam melhor qualidade de bebida em comparação aos frutos verdes ou secos. Além disso, a planta de café em suas condições naturais, apresenta maturação em todos os estádios na mesma planta. Sendo assim, o café seria uma cultura com potencial que justifique mais pesquisas na colheita. Surge então a hipótese: e se fosse possível colher somente frutos maduros – cereja e seco? Permanecendo os frutos verdes ainda na planta, para completar seu ciclo de maturação fisiológica, sendo colhidos em um segundo momento? E se com um celular em mãos o produtor pudesse tirar fotografias dos grãos, e um estimador digital reconhecesse os frutos e estimasse a produtividade e classificasse os frutos em seus respectivos estádios de maturação. Seria possível ter uma quantidade de informação suficiente de pontos em uma área, para gerar um mapa de produtividade e maturação? De posse do mapa, poderíamos gerar um “projeto de colheita” a ser realizado pela colhedora, buscando colher apenas os frutos de interesse. Para comprovar tal situação dividimos o trabalho em duas etapas: 1) Por meio de imagens identificar os frutos de café digitalmente. Esperamos que por meio de imagens tiradas por celulares ou câmeras possam estimar a produtividade e maturação, utilizando as técnicas de processamento de imagens por Deep Learning; 2) Por meio de mapas de produtividade e maturação de uma área, definir zonas de manejo, buscando-se determinar qual a melhor regulagem para colher em diferentes graus de maturação e produtividade, buscando-se colher somente os frutos maduros (cereja e seco) e análise econômica da operação. Como resultados, observou-se que o modelo identifica os frutos de café de maneira satisfatória independente da condição de captura de foto. O reconhecimento dos frutos apresentou R2 = 0,92, no modelo proposto, reconhecendo frutos inclusive de um terço inteiro da planta de café. Também observamos que não foi possível colher somente os frutos maduros para cada regulagem testada em cada zona de manejo definida pela produtividade e maturação dos frutos de café. Recomendando-se, portanto, a regulagem com maior velocidade operacional.
ABSTRACT: Coffee is a commodity which price is adjusted according to quality parameters, the harvest is an operation that is closely linked to the final quality of the product. We know that fruits in the cherry stage have better quality drink compared to green or dry fruits. In addition, the coffee plant in its natural conditions, shows maturation in all stages in the same plant. Therefore, coffee would be a crop with potential to justify more research at harvesting. The hypothesis then arises: what if it were possible to harvest only ripe fruits - cherry and dry? Remaining the green fruits still in the plant, to complete their physiological maturation cycle, being harvested in a second moment? And if with a cell phone in hand farmers could take pictures of the grains, and a digital estimator would recognize the fruits and estimate the productivity and classify the fruits in their respective stages of maturation. It would be possible to have enough information in the area to generate maps of productivity and maturation. In the possession of the map, we could generate a "harvest project" to be carried out by the harvester, seeking to reap only the fruits of interest. To prove this situation, we divide the work into two stages: 1) With images, digitally identifies coffee fruits. We hope that by means of images taken by cell phones or cameras can estimate the productivity and maturation, using the techniques of image processing by Deep learning; 2) Through maps of productivity and maturation of an area, to define management zones, seeking to determine the best regulation to harvest in different degrees of maturity and productivity, seeking to harvest only mature fruits (cherry and dry) and economic analysis of the operation. As results, it was observed that the model identifies coffee fruits satisfactorily independent of the photo capture condition. The fruit recognition obtained R2 = 0.92, in the proposed model, recognizing fruits even of the whole third of the coffee tree. We also observed that it was not possible to harvest only the ripe fruits for mechanized harvesting adjustments within the management zones defined by the productivity and ripening of the coffee fruits. Therefore, it is recommended to regulate with higher operating speed.
ABSTRACT: Coffee is a commodity which price is adjusted according to quality parameters, the harvest is an operation that is closely linked to the final quality of the product. We know that fruits in the cherry stage have better quality drink compared to green or dry fruits. In addition, the coffee plant in its natural conditions, shows maturation in all stages in the same plant. Therefore, coffee would be a crop with potential to justify more research at harvesting. The hypothesis then arises: what if it were possible to harvest only ripe fruits - cherry and dry? Remaining the green fruits still in the plant, to complete their physiological maturation cycle, being harvested in a second moment? And if with a cell phone in hand farmers could take pictures of the grains, and a digital estimator would recognize the fruits and estimate the productivity and classify the fruits in their respective stages of maturation. It would be possible to have enough information in the area to generate maps of productivity and maturation. In the possession of the map, we could generate a "harvest project" to be carried out by the harvester, seeking to reap only the fruits of interest. To prove this situation, we divide the work into two stages: 1) With images, digitally identifies coffee fruits. We hope that by means of images taken by cell phones or cameras can estimate the productivity and maturation, using the techniques of image processing by Deep learning; 2) Through maps of productivity and maturation of an area, to define management zones, seeking to determine the best regulation to harvest in different degrees of maturity and productivity, seeking to harvest only mature fruits (cherry and dry) and economic analysis of the operation. As results, it was observed that the model identifies coffee fruits satisfactorily independent of the photo capture condition. The fruit recognition obtained R2 = 0.92, in the proposed model, recognizing fruits even of the whole third of the coffee tree. We also observed that it was not possible to harvest only the ripe fruits for mechanized harvesting adjustments within the management zones defined by the productivity and ripening of the coffee fruits. Therefore, it is recommended to regulate with higher operating speed.
Descrição
Palavras-chave
Coffea arabica, processamento de imagens, machine learning, neural networks, Agricultura de precisão, Velocidade de colheita, Image processing, Precision agriculture, Harvest speed