Design features and bruise evaluation of an apple harvest and in-filed presorting machine

In-field presorting of apples, in combination with the harvest aid function, would have advantages of cost savings in postharvest handling and storage, reduced postharvest pest and disease problems, and better inventory management, while also enhancing harvest productivity. A new apple harvest and in-field presorting prototype was developed to help apple growers achieve these potential benefits. The prototype sorts and grades fruit based on color and size, using a machine vision-based sorting system with an innovative fruit singulating and rotating design (SRD), and it handles the graded fruit in the bins using newly designed automatic bin fillers. Bruise damage by impact is a critical factor in the development of the apple harvest and in-field presorting prototype. This article reports on the major design features of the prototype and experimental evaluation of the prototype for potential bruise damage. Experiments were conducted on ‘Gala’ and ‘Fuji’ apples to evaluate bruise damage potential under both empty and partially filled bin conditions. An impact recording device (IRD) was used to measure the impact magnitude in terms of peak acceleration (G) at all critical points of the machine, including harvest conveyors, main conveyor, flat conveyor, SRD, cup conveyor, bin filler, and bins. It was found that bruise damage mainly occurred during bin filling. The number of impacts recorded for the partially filled bin was reduced by 60%, compared to that for the empty bin, indicating that the impact between apples and the wooden bin’s floor was a major cause of bruising. The maximum G value for the partially filled bin was measured at 34.5, while the measured G values were less than 20 from start to the point just before the bin filler, indicating no bruise damage. Bruise evaluation showed that no more than 9% of the test apples would be downgraded from ‘Extra Fancy’ grade for the partially filled bin condition. Higher G values for the empty bin condition suggested the need for further improvement to the discharge of apples from the bin filler to the bin to further reduce bruise damage

High-Precision Fruit Localization Using Active Laser-Camera Scanning: Robust Laser Line Extraction for 2D-3D Transformation

Recent advancements in deep learning-based approaches have led to remarkable progress in fruit detection, enabling robust fruit identification in complex environments. However, much less progress has been made on fruit 3D localization, which is equally crucial for robotic harvesting. Complex fruit shape/orientation, fruit clustering, varying lighting conditions, and occlusions by leaves and branches have greatly restricted existing sensors from achieving accurate fruit localization in the natural orchard environment. In this paper, we report on the design of a novel localization technique, called Active Laser-Camera Scanning (ALACS), to achieve accurate and robust fruit 3D localization. The ALACS hardware setup comprises a red line laser, an RGB color camera, a linear motion slide, and an external RGB-D camera. Leveraging the principles of dynamic-targeting laser-triangulation, ALACS enables precise transformation of the projected 2D laser line from the surface of apples to the 3D positions. To facilitate laser pattern acquisitions, a Laser Line Extraction (LLE) method is proposed for robust and high-precision feature extraction on apples. Comprehensive evaluations of LLE demonstrated its ability to extract precise patterns under variable lighting and occlusion conditions. The ALACS system achieved average apple localization accuracies of 6.9 11.2 mm at distances ranging from 1.0 m to 1.6 m, compared to 21.5 mm by a commercial RealSense RGB-D camera, in an indoor experiment. Orchard evaluations demonstrated that ALACS has achieved a 95% fruit detachment rate versus a 71% rate by the RealSense camera. By overcoming the challenges of apple 3D localization, this research contributes to the advancement of robotic fruit harvesting technology

Sensors for product characterization and quality of specialty crops—A review

This review covers developments in non-invasive techniques for quality analysis and inspection of specialty crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and discussed in this review are advanced sensing technologies including computer vision, spectroscopy, X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency identification sensors. The current status of different sensing systems is described in the context of commercial application. The review also discusses future research needs and potentials of these sensing technologies. Emphases are placed on those technologies that have been proven effective or have shown great potential for agro-food applications. Despite significant progress in the development of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these technologies by the specialty crop industry has been slow

Detection of Chilling Injury in Pickling Cucumbers Using Dual-Band Chlorophyll Fluorescence Imaging

Pickling cucumbers are susceptible to chilling injury (CI) during postharvest refrigerated storage, which would result in quality degradation and economic loss. It is, thus, desirable to remove the defective fruit before they are marketed as fresh products or processed into pickled products. Chlorophyll fluorescence is sensitive to CI in green fruits, because exposure to chilling temperatures can induce detectable alterations in chlorophylls of tissues. This study evaluated the feasibility of using a dual-band chlorophyll fluorescence imaging (CFI) technique for detecting CI-affected pickling cucumbers. Chlorophyll fluorescence images at 675 nm and 750 nm were acquired from pickling cucumbers under the excitation of ultraviolet-blue light. The raw images were processed for vignetting corrections through bi-dimensional empirical mode decomposition and subsequent image reconstruction. The fluorescence images were effective for ascertaining CI-affected tissues, which appeared as dark areas in the images. Support vector machine models were developed for classifying pickling cucumbers into two or three classes using the features extracted from the fluorescence images. Fusing the features of fluorescence images at 675 nm and 750 nm resulted in overall accuracies of 96.9% and 91.2% for two-class (normal and injured) and three-class (normal, mildly and severely injured) classification, respectively, which are statistically significantly better than those obtained using the features at a single wavelength, especially for the three-class classification. Furthermore, a subset of features, selected based on the neighborhood component feature selection technique, achieved the highest accuracies of 97.4% and 91.3% for the two-class and three-class classification, respectively. This study demonstrated that dual-band CFI is an effective modality for CI detection in pickling cucumbers

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques

Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress

status: publishe

Special issue: Recent advances in the use of visible and vibrational spectroscopy/imaging for measurement of postharvest quality

Advances in both science and industry applications are driven by technological improvements. For example, consider that the human ability to interpret nature was limited by the resolution of human eyesight until the microscope was invented

System for sorting fruit

As fruit (preferably apples) are harvested, the fruit is placed on a conveyor that conveys the fruit to a fruit singulating section. In the singulating section, the fruit is directed into slots in a lane formed by two cooperating helical drives. The helical drives rotate the fruit and convey the fruit into an imaging chamber where a camera acquires an image of the fruit. The fruit image is evaluated by a processor in communication with the camera. The fruit is then directed into a rotary sorter which sorts the fruit based on the image evaluation by the processor