Name
Abstract | ||
|---|---|---|
A system to count the number of fruits on a coffee branch in field conditions.A technique that detects occluded and unoccluded fruits in field images.A technique that classifies fruits as either harvestable or not harvestable.A model that relates 1-by-1 the number of observed fruits and measurement fruits.A system in field to measure the number of fruits in different moments of the harvest.A technique for small coffee growers, which estimates coffee production in a farm. In this article, a non-destructive method is proposed to count the number of fruits on a coffee branch by using information from digital images of a single side of the branch and its growing fruits. In order to do this, 1018 coffee branches at different ripening stages. They had different numbers of fruits, harvest dates, were of different varieties, and were at different stages of coffee trees life. A Machine Vision System (MVS) was constructed, which was capable of counting and identifying harvestable and not harvestable fruits in a set of images corresponding to a specific coffee branch was constructed. This MVS consists of an image acquisition system, based on mobile devices (it does not require to control of the environmental conditions), and an image processing algorithm to classify and detect each one of the fruits in the acquired images. After obtaining information regarding the number of fruits identified by the MVS, linear estimation models were constructed between the detected fruits automatically and the ones observed on the coffee branch. These models were calculated for fruits in three categories: harvestable, not harvestable, and fruits whose maturation stage were disregarded. These models link the fruits that are counted automatically to the ones actually observed with an R2 higher than 0.93 one-to-one. Not only is the MVS used to estimate the number of fruits on the branch but also to estimate their maturation percentage and weight. The MVS was validated in four Variedad Castillo coffee plots, in different stages of development and with different densities. We found that MVS neither overestimates nor underestimates the number of fruits and that it shows a correlation higher than 0.90 at early stages of crop development, when tree fruits are still not harvestable. The information obtained in this research will spawn a new generation of tools for coffee growers to use. It is an efficient, non-destructive, and low-cost method which offers useful information for them to plan agricultural work and obtain economic benefits from the correct administration of resources. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.compag.2017.03.010 | Computers and Electronics in Agriculture |
Keywords | Field | DocType |
Coffee,Linear model,Fruits on branches,Harvest | Machine vision system,Computer vision,Linear estimation,Artificial intelligence,Engineering,Ripening,Economic benefits | Journal |
Volume | Issue | ISSN |
137 | C | 0168-1699 |
Citations | PageRank | References |
9 | 0.56 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paula Jimena Ramos Giraldo | 1 | 10 | 1.26 |
| Flavio Prieto | 2 | 24 | 9.63 |
| E. C. Montoya | 3 | 9 | 0.56 |
| Carlos Eugenio Oliveros | 4 | 9 | 0.56 |