ABSTRACT

The fundamental characteristics of agricultural products are appearance, size, and weight, which affect their market value, consumer preference, and choice. Thus, food and agricultural industries seek rapid, simple, and nondestructive approaches to assess real-time measurements at the post-harvest stage before packaging for the consumer market. While sorting and grading may be performed by humans, it is unreliable, time-consuming, complicated, subjective, onerous, expensive, and easily influenced by surroundings. Therefore, an astute sorting and grading method for tomato fruit is required. We evaluated two tomato configurations on a conveyor belt: single tomatoes (no occlusion) and multi-tomatoes (partially occluded). We used polygon approximation for concave and convex point extraction algorithms to segment the occluded tomatoes. We developed seven models for regression using single-tomato image features. The Bayesian regularization artificial neural network outranked all the trained models in weight estimation with a root-mean-square error (RMSE) of 1.468 g and R² of 0.971. For volume estimation, the RBF SVM had the best performance with R² of 0.982 and RMSE of 1.2683 cm³. It is feasible to implement a proposed system as a noninvasive in-line sorting technique for tomatoes.

摘要

农产品的基本特征是外观、大小和重量，影响其市场价值、消费者偏好和选择。因此，食品和农业行业寻求快速、简单和无损的方法来评估收获后阶段的实时测量，然后再为消费者市场包装。分拣分级虽然可以由人工完成，但不可靠、耗时、复杂、主观、繁重、昂贵，且容易受环境影响。因此，需要一种精明的番茄果实分选分级方法。我们评估了传送带上的两种番茄配置：单个番茄（无遮挡）和多番茄（部分遮挡）。我们使用多边形近似用于凹凸点提取算法来分割被遮挡的西红柿。我们开发了七个使用单番茄图像特征的回归模型。贝叶斯正则化人工神经网络在权重估计方面优于所有训练模型，均方根误差 (RMSE) 为 1.468 g 和R ²为 0.971。对于体积估计，RBF SVM 具有最佳性能，R ²为 0.982，RMSE 为 1.2683 cm ³。将所提议的系统实施为西红柿的无创在线分选技术是可行的。