Abstract 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.

中文翻译：

---

一种用于田间绿橄榄视觉质量自动评估的智能系统

摘要 监测影响绿色加工食用橄榄质量的一些参数是农民决策的基础。这项工作开发了一种经济实惠的系统，用于水果口径、成熟度和瘀伤指数的现场评估。该系统由一个发光的立方体组成，该立方体使用软件中实现的计算机视觉技术获取水果样本的图像并生成即时报告。为此，有必要确定果实重量和大小的模型以及橄榄成熟度指数中涉及的颜色区域（RGB 颜色空间）。此外，还创建了监督训练模型来执行图像分割（背景和瘀伤区域）。单果重估计误差很小（R2=0.9），成熟度指数（MI）预测较好，精度为0。MI0 和 MI1 中手动分选的橄榄分别为 66 和 0.91（绿色加工）。当水果变成黑紫色并且由于颜色的确定相似性而将瘀斑与水果面积混淆时，MI2 的预测精度较低 (0.48)。MI0 (RMSE = 2.42) 的估计瘀伤指数误差低于 MI1 (RMSE = 3.78)，两者都适用于现场质量估计。总体而言，该系统的性能显示了对定义橄榄质量的外部参数进行快速、简单和准确评估的有希望的结果。获得的模型可用于其他目的。48) 当果实变成黑紫色并且由于颜色的确定相似性而将瘀斑与果实区域混淆时。MI0 (RMSE = 2.42) 的估计瘀伤指数误差低于 MI1 (RMSE = 3.78)，两者都适用于现场质量估计。总体而言，该系统的性能显示了对定义橄榄质量的外部参数进行快速、简单和准确评估的有希望的结果。获得的模型可用于其他目的。48) 当果实变成黑紫色并且由于颜色的确定相似性而将瘀斑与果实区域混淆时。MI0 (RMSE = 2.42) 的估计瘀伤指数误差低于 MI1 (RMSE = 3.78)，两者都适用于现场质量估计。总体而言，该系统的性能显示了对定义橄榄质量的外部参数进行快速、简单和准确评估的有希望的结果。获得的模型可用于其他目的。s 性能揭示了对定义橄榄质量的外部参数进行快速、简单和准确评估的有希望的结果。获得的模型可用于其他目的。s 性能揭示了对定义橄榄质量的外部参数进行快速、简单和准确评估的有希望的结果。获得的模型可用于其他目的。