Automatic green fruit counting in orange trees using digital images
Yield estimation is an important factor in a production process planning. In the case of citrus crops, can be useful in industrial management and as guidance for farmers, showing a decisive role in the product market strategies and cultivation practices. Several techniques are being studied for esti...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/173296 |
| Acceso en línea: | http://dx.doi.org/10.1016/j.compag.2016.07.023 http://hdl.handle.net/11449/173296 |
| Access Level: | acceso abierto |
| Palabra clave: | Citrus Computer vision Fruit detection Precision agriculture Yield estimation |
| Sumario: | Yield estimation is an important factor in a production process planning. In the case of citrus crops, can be useful in industrial management and as guidance for farmers, showing a decisive role in the product market strategies and cultivation practices. Several techniques are being studied for estimating citrus crop yield. On the basis of the known correlation between the number of visible fruits in a digital image and the total of fruits present in an orange tree, we developed a method for green fruit feature extraction with a combination of the techniques of color model conversion, thresholding, histogram equalization, spatial filtering with Laplace and Sobel operators and Gaussian blur. In addition, we built and tested an algorithm to recognize and count them, with detection rates of false-positives of 3% in images acquired in good conditions. It is possible to estimate the mean number of visible fruits in the trees within a tolerated error of 5% with up to 46 images and taking approximately 8 min without any human interaction. |
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