ABSTRACT

Nondestructive estimation of physicochemical properties, post-harvest physiology, and level of ripeness of fruits is essential to their automated harvesting, sorting, and handling. Recent research efforts have identified machine vision systems as a promising noninvasive nondestructive tool for exploring the relationship between physicochemical and appearance characteristics of fruits at various ripening levels. In this regard, the purpose of the current study is to provide an intelligent algorithm for estimating two physical properties including firmness, and soluble solid content (SSC), three chemical properties viz. starch, acidity, and titratable acidity (TA), as well as detection of the ripening level of apples (cultivar Red Delicious) using video processing and artificial intelligence. To this end, videos of apples in orchards at four levels of ripeness were recorded and 444 color and texture features were extracted from these samples. Five physicochemical properties including firmness, SSC, starch, acidity, and TA were measured. Using the hybrid artificial neural network-difference evolution (ANN-DE), six most effective features (one texture and five color features) were selected to estimate the physicochemical properties of apples. The physicochemical estimation was then further optimized using a hybrid multilayer perceptron artificial neural network-cultural algorithm (ANN-CA). The results showed that the coefficient of determinations (R²) related to the prediction models for the physicochemical properties were in excess of 0.92. Additionally, the ripeness level of apples was estimated based on physicochemical properties using a hybrid multilayer perceptron artificial neural network-harmonic search algorithm (ANN-HS) classifier. The developed machine vision system examined ripeness levels of 1356 apples in natural orchard environments and achieved a correct classification rate (CCR) of 97.86%.

摘要

水果的物理化学性质、收获后生理学和成熟度的无损评估对于其自动收获、分类和处理至关重要。最近的研究工作已将机器视觉系统确定为一种有前途的非侵入性无损工具，用于探索各种成熟水平下水果的物理化学和外观特征之间的关系。在这方面，当前研究的目的是提供一种智能算法来估计两种物理特性，包括硬度和可溶性固体含量 (SSC)，即三种化学特性。淀粉、酸度和可滴定酸度 (TA)，以及苹果（品种Red Delicious ）成熟度的检测) 使用视频处理和人工智能。为此，记录了四个成熟度的果园苹果视频，并从这些样本中提取了 444 个颜色和纹理特征。测量了包括硬度、SSC、淀粉、酸度和TA在内的五种理化性质。使用混合人工神经网络差分进化（ANN-DE），选择六个最有效的特征（一个纹理和五个颜色特征）来估计苹果的理化性质。然后使用混合多层感知器人工神经网络-文化算法 (ANN-CA) 进一步优化物理化学估计。结果表明，决定系数（R ²) 与物理化学性质预测模型相关的值超过 0.92。此外，使用混合多层感知器人工神经网络-谐波搜索算法 (ANN-HS) 分类器基于物理化学性质估计苹果的成熟度。开发的机器视觉系统在自然果园环境中检查了 1356 个苹果的成熟度，并实现了 97.86% 的正确分类率 (CCR)。