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... stage, Learning, the initial Data Base (DB) based on a fuzzy grid in order to obtain zero-order TSK candidate rules, is learned using an effective Multi-Objective Evolutionary Algorithm (MOEA) [7], [8]. The second stage, Tuning, applies an advanced post-processing for fine scatterbased evolutionary tuning of MFs combined with a rule selection. Fig. 6 shows the process described previously. Once the TSK-FRBS has been optimized, a new algorithm of the METSK-HD type is obtained, which we will call ...
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... As we can observe, artificial intelligence is beginning to take hold in agriculture and more recently in crops such as plums. Papers [10][11][12] present results demonstrating the effectiveness of artificial intelligence applied to agriculture, most notably in Japanese plum. Works such as those presented in [1][2][3][13][14][15][16][17][18] present results in the field of computer vision and DL; however, works that are precise and that research ripeness analysis of fruit using images collected in real environment and without capture constrains are insufficient. ...
Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness ,three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety.
Monitoring some of the parameters that affect the quality of table olives for green processing is fundamental in a farmer’s decision making. This work develops an affordable system for in-the-field evaluation of fruit calibre, ripeness and bruise index. The system consists of an illuminated cube that acquires images of fruit samples and generates an instantaneous report, using computer vision techniques implemented in software. To do this, it was necessary to determine models of fruit weight and size and also the colour regions (RGB colour space) involved in olive maturity indexes. Moreover, supervised training models were created to perform image segmentation (background and bruising areas). Error in the estimation of fruit weight was very low (R2 = 0.9), and prediction of the maturity index (MI) was quite good, with an accuracy of 0.66 and 0.91 for manually sorted olives in MI0 and MI1 respectively (green processing). Prediction of MI2 had lower precision (0.48) when the fruit was changing to black-purple and the bruising spots were confused with fruit area because of determined similarities in colour. The error in the estimated bruise index was lower for MI0 (RMSE = 2.42) than for MI1 (RMSE = 3.78), both of which are suitable for an estimation of quality in the field. Overall, the system’s performance reveals promising results for a quick, easy and accurate evaluation of the external parameters that define the quality of olives. The models obtained could be useful for other purposes.
