Localization of spherical fruits for robotic harvesting

The orange picking robot (OPR) is a project for developing a robot that is able to harvest oranges automatically. One of the key tasks in this robotic application is to identify the fruit and to measure its location in three dimensions. This should be performed using image processing techniques which must be sufficiently robust to cope with variations in lighting conditions and a changing environment. This paper describes the image processing system developed so far to guide automatic harvesting of oranges, which here has been integrated in the first complete full-scale prototype OPR. Received: 16 April 2000 / Accepted: 19 December 2000     

基于分形特征的水果缺陷快速识别方法

计算机视觉和图象处理技术在水果自动分选和分级中起着重要的作用。因为缺陷检测的复杂性,水果表面缺陷的快速检测和识别一直是水果自动化分选和分级的障碍。在实数域分形盒维数计算方法的基础上,提出了双金字塔数据形式的盒维数快速计算方法。对于待识别水果图象的可疑缺陷区,提出用５个分形维数作为描述该区域粗糙度和纹理方向性的特征参数,并用所提出的快速计算方法进行计算,然后利用人工神经网络（ＢＰ）作为模式识别器,区     

基于IMAQ Vision和PLC的水果大小自动分级系统设计

水果大小是水果分级的一个重要依据,随着计算机技术和图像处理技术的飞速发展,计算机视觉技术被广泛用于水果品质的检测。所设计的水果大小自动分级系统由机器视觉检测系统和基于PLC的水果传送分拣机构组成。使用LabbVIEW软件编写水果大小自动分级系统的监控界面,并实现对CCD摄像机的控制及获取图片;使用IMAQ Vision工具包对所获取的水果图片进行处理,并进行大小分析,根据大小的等级由LabVIEW通过串口通信发送命令给PLC,由PLC控制水果的传送并进入对应的分级口,从而完成水果的自动分级。实际运行证明该系统能实时进行动态采集图片,有效地进行水果大小分析及自动分级。     

A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier

Advances in computer vision have led to the development of promising solutions for challenging problems in agriculture. Fruit grading and sorting are complex problems which require a great deal of human expertise. In this paper, we propose a non-destructive system for sorting and grading tomatoes, which is confounding even for expert human sorters. The proposed system performs classifications of tomatoes in three stages with digital images of samples captured in an experimental setup deployed using microcontroller. In the first stage, a binary classification is performed to discriminate tomatoes from other species using a species vector constructed from these images. In the second stage, the tomatoes are classified into ripe and unripe categories based on the color attribute. Then, the defects in the fruits are identified using Gabor wavelet transform to segment the infected regions of these images. The third stage identifies three types of defects namely the black spots, cankers and Melanose, based on a defect vector constructed from additional color and geometric features. Due to the complexity involved in solving such a non-linear problem, the proposed system is implemented as a cascade of two support vector machine classifiers. The performance of this system is assessed with the accuracy, specificity, sensitivity and precision metrics. The experimental results and comparative analyses with similar methods testify the efficacy of the proposed system over existing systems on the sorting and grading of tomatoes.The results obtained from each of the three classification stages i.e. Tomato/Non-Tomato, Good/Defective and the type of defect in the case of defective are communicated to the microcontroller to enable the respective motor, so that the given fruit is classified and collected in the respective bin.     

A multiple criteria decision for trading capacity between two semiconductor fabs

This paper presents a multiple criteria decision approach for trading weekly tool capacity between two semiconductor fabs. Due to the high-cost characteristics of tools, a semiconductor company with multiple fabs (factories) may weekly trade their tool capacities. That is, a lowly utilized workstation in one fab may sell capacity to its highly utilized counterpart in the other fab. Wu and Chang [Wu, M. C., & Chang, W. J. (2007). A short-term capacity trading method for semiconductor fabs with partnership. Expert Systems with Application, 33(2), 476–483] have proposed a method for making weekly trading decisions between two wafer fabs. Compared with no trading, their method could effectively increase the two fabs’ throughput for a longer period such as 8 weeks. However, their trading decision-making is based on a single criterion—number of weekly produced operations, which may still leave a space for improving. We therefore proposed a multiple criteria trading decision approach in order to further increase the two fabs’ throughput. The three decision criteria are: number of operations, number of layers, and number of wafers. This research developed a method to find an optimal weighting vector for the three criteria. The method firstly used NN + GA (neural network + genetic algorithm) to find an optimal trading decision in each week, and then used DOE + RSM (design of experiment + response surface method) to find an optimal weighting vector for a longer period, say 10 weeks. Experiments indicated that the multiple criteria approach indeed outperformed the previous method in terms the fabs’ long-term throughput.     

AI-enabled farm-friendly automatic machine for washing,image-based sorting,and weight grading of citrus fruits: Design optimization,performance evaluation,and ergonomic assessment

The modernization of postharvest operations and penetration of emerging technologies in horticultural processing have provided intelligent solutions for reducing postharvest losses. Work environmental and occupational health issues require immediate attention as the awkward posture and continuous drudgery-prone on-farm sorting and grading activities may lead to musculoskeletal disorders. The main objective of this study was to develop an automatic farm-friendly machine for real-time citrus fruit washing, image-based sorting, and weight grading; designed optimally and equipped with an embedded system comprising a lightweight convolutional neural network (CNN) model. Also included in this study was a thorough ergonomic assessment of the developed machine in a real work environment. The parametric choice of the fruit washing and singulation system was performed by employing computational fluid dynamics modeling and response surface methodology designed optimization. It was observed that under steady-state conditions, the water jet would arrive at a velocity of 11.36 m/s which would eventually suit a singulation conveyor with a slope of 25°. A noninvasive grading and sorting approach for citrus fruits is presented in this paper that leverages deep learning to classify the fruits into “accept” and “reject” classes. The custom lightweight CNN model “SortNet” has shown excellent classification results with an overall accuracy of 97.6%. The ergonomic evaluation shows that the average body part discomfort score in case of operating an automatic fruit grading machine was much lower (12.3 ± 2.0) than the traditional method (30.9 ± 3.3). Further, in the case of machine operation, the percentage load on the muscles ranged from 28.67 to 34.31 reflecting that subjects can work for longer duration on the machine without fatigue as compared with the traditional manual operation.     

Assessing creative problem-solving with automated text grading

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational–statistical machine learning methods to grade students’ natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students’ constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students’ natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen’s Kappa = .65–.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson’s r = .92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning.     

基于BP神经网络的水果识别系统

近些年,计算机视觉发展迅速,在水果识别方向进行了广泛的应用和研究。本文设计基于BP神经网络的水果识别系统,选取生活中常见的三种水果:苹果、橘子、香蕉作为对象。首先,通过网络资源等搜集水果图像建立样本库;然后通过MATLAB对图像进行预处理,为后续的特征提取做好准备。水果特征的提取选择纹理、形状、颜色三种特征进行提取;同时在每种特征中选用不同的特征值作为特征向量。通过提取三种特征后输入到BP神经网络中进行训练、识别。经测试,识别的成功率可以达到93.18%,证明了可行性以及未来的可实用性。     

A linear-complexity reparameterisation strategy for the hierarchical bootstrapping of capabilities within perception–action architectures

Perception–action (PA) architectures are capable of solving a number of problems associated with artificial cognition, in particular, difficulties concerned with framing and symbol grounding. Existing PA algorithms tend to be ‘horizontal’ in the sense that learners maintain their prior percept–motor competences unchanged throughout learning. We here present a methodology for simultaneous ‘horizontal’ and ‘vertical’ perception–action learning in which there additionally exists the capability for incremental accumulation of novel percept–motor competences in a hierarchical fashion.The proposed learning mechanism commences with a set of primitive ‘innate’ capabilities and progressively modifies itself via recursive generalising of parametric spaces within the linked perceptual and motor domains so as to represent environmental affordances in maximally-compact manner. Efficient reparameterising of the percept domain is here accomplished by the exploratory elimination of dimensional redundancy and environmental context.Experimental results demonstrate that this approach exhibits an approximately linear increase in computational requirements when learning in a typical unconstrained environment, as compared with at least polynomially-increasing requirements for a classical perception–action system.     

Measuring and Evaluating Parallel State-Space Exploration Algorithms

We argue in this paper that benchmarking should be complemented by direct measurement of parallelisation overheads when evaluating parallel state-space exploration algorithms. This poses several challenges that so far have not been addressed in the literature: what exactly are those overheads, how can and cannot they be measured, and how should system models be selected in order to expose the causes of parallelisation (in)efficiencies? We discuss and answer these questions based on our experience with parallelising Saturation – a symbolic algorithm for generating state-spaces of asynchronous system models – on a shared-memory architecture. Doing so will hopefully spare newcomers to the growing PDMC community from having to learn these lessons the hard way, as we did over a painful period of almost three years.