Apple Surface Research Articles

Fabrication of high performance 2D flexible SERS substrate based on cellulose nanofibrils and its application for pesticide residue detection

Cellulose nanofibrils (CNFs) can serve as an efficient surface enhanced Raman scattering (SERS) platform for in situ detection of trace targets. In this study, a highly reproducible SERS platform based on TEMPO-oxidized CNFs (T-CNFs) was fabricated by the ion-exchange. Self-assembly of silver nanoparticles (AgNPs) was accomplished in only 120 s. The abundant carboxylate groups and good hydrophilicity of T-CNFs facilitated uniform and dense loading of AgNPs over the surface area. The obtained SERS substrate greatly enhanced the Raman signal of different pesticides, and the detection limits of thiram and thiabendazole were 5.81 × 10−8 M and 9.63 × 10−8 M, respectively. SERS substrate could produce homogeneous Raman-enhanced signals (relative standard deviation (RSD) = 6.59 %). In addition, due to the good flexibility, SERS substrate could collect and detect pesticide residues from the surface of apples. The intensities of Raman characteristic peak at 1384 cm−1 showed a good linear relationship with the analyte concentrations (0.96 ng/cm2–9600 ng/cm2). The constructed SERS substrate provided a theoretical basis for the preliminary rapid screening of hazardous chemical residues in food, which was of great value for the SERS technique to become a routine on-site analysis method for pesticide residues.

Ursolic acid, a natural endogenous compound, inhibits browning in fresh-cut apples

Enzymatic browning occurs rapidly in fresh-cut apples and can lead to economic losses. Although various methods have been developed to prevent this browning, physical methods are costly, and chemical methods are controversial, suggesting the potential usefulness of an endogenous natural browning inhibitor. In this study, the effects of ursolic acid (UA), a natural endogenous compound, on apple browning were investigated. It was found that UA inhibited the activity of polyphenol oxidase through Cu2+ chelation. Molecular docking indicated that the inhibition was reversible and mixed-type. Moreover, UA treatment promoted the scavenging of reactive oxygen species in fresh-cut apple and reduced the proliferation of microorganisms on the apple surface. Similar effects were observed on the browning of chestnut, eggplant, potatoes, and pears. In conclusion, the findings demonstrate the efficacy of UA on inhibiting browning in fresh-cut apple, together with an analysis of the mechanism involved.

Efficacy of Postharvest Application of Aureobasidium pullulans to Control White Haze on Apples and Effect on the Fruit Mycobiome

White haze, an emerging disorder caused by extensive fungal growth on the apple surface, results in a compromised fruit quality and decreased marketability. The use of biological control agents could be an interesting tool to reduce its development. This work aimed to test the efficacy of two Aureobasidium pullulans strains (AP2 and PL5) in controlling white haze on stored apples. An in vivo trial was conducted by inoculating fruits with white haze causal agents (Entyloma belangeri, Golubevia pallescens, Tilletiopsis washingtonensis) and treating them with the antagonistic yeasts. Three commercial biofungicides were also included in the trial. Both A. pullulans strains, along with the Metschnikowia fructicola-based product, reduced white haze incidence after 110 days of storage at 1 ± 1 °C and after 7 days of shelf life. Furthermore, the effect of A. pullulans application on the fruit fungal microbiome was assessed. A significant impact of apple matrix and treatment on the mycobiome composition was observed. Analyses showed a good colonization of A. pullulans on the treated apples, both epiphytically and endophytically. A decrease in white haze-related fungi abundance was observed in the treated fruits. Additionally, a reduction of Ramularia spp. and modifications in the abundance of other fungal genera were detected after storage and shelf life.

Impact of overhead evaporative cooling, canopy location, sunlight exposure, inoculation level, region, and growing season on the survival of generic Escherichia coli on in-field Fuji apples.

The survival of inoculated Escherichia coli on Fuji apples in Washington State orchards was studied, considering evaporative cooling, canopy location, year, and region, with the examination of sunlight exposure and inoculation levels in year 2. Rifampicin-resistant E. coli was applied to Fuji apples. Initial concentrations for the high-inoculation study were 7.4±0.3 log10 CFU per apple and 3.4±0.3 log10 CFU per apple for the low-inoculation study. Enumeration of E. coli was conducted at 0, 2, 10, 18, 34, 42, 58, 82, 106, and 154 h after inoculation. Results were analyzed using Tukey's honest significance difference test and a log-linear model. Log-linear, Weibull, and biphasic models characterized E. coli die-off patterns for high and low inoculations. The application of evaporative overhead cooling water did not significantly influence E. coli survival on Fuji apples; inoculation level and sunlight exposure were significant factors in a log-linear model. Escherichia coli decreased by 5.5±1.3 and 3.3±0.4 log10 CFU per apple for high and low-inoculated apples, respectively, by 154 h. The biphasic model best explained the die-off pattern for high and low-inoculated Fuji apples. Overhead evaporative cooling, a useful fruit quality practice, did not impact the survival of generic E. coli on Fuji apple surfaces. The significant impact of sunlight exposure and inoculation levels on die-off highlights the importance of ultraviolet radiation in risk reduction and the need for various inoculum concentrations in preharvest field studies.

Hyperspectral imaging coupled with deep learning model for visualization and detection of early bruises on apples

Early bruise on apples caused by external impacts during the transportation process is commonly difficult to be detected on the apple surface, limiting the application of traditional machine vision methods in determining fruit quality. In recent years, hyperspectral imaging (HSI) has emerged as a promising technology for identifying early bruise of fruits due to its efficient and nondestructive detection. In this study, HSI data in the shortwave infrared range were collected at 2-hour and 6-hour intervals after mechanical damage. The combination of the successive projections algorithm (SPA) and principal component analysis (PCA) was used to select three key feature bands, namely, 1074 nm, 1269 nm and 1441 nm. Pseudo color transformation and band ratio algorithm were then employed to improve the contrast between damaged and healthy apple tissues for image enhancement. The fast and precise YOLOv5 (FP-YOLOv5) model achieved effective identification of apple bruises, with a high recognition rate of 95 % and a fast detection speed at 130 fps. Overall, the proposed framework based on band selection and image enhancement exhibits better performance in the detection of early apple bruises, providing useful insights for HSI combined with a deep learning model in the grading evaluation of fruit quality.

Application of lactobacillus casei and lactobacillus plantarum to develop dried functional apple and banana.

The goal of this research was to create dried fruits loaded with probiotic microorganisms (Lactobacillus casei and Lactobacillus plantarum). In separate bottles for each probiotic microbe, apple and banana pieces have been submerged into the impermeability solution with gentle shaking. The vacuum pressure was applied. By the conclusion of the incubation time, L. casei and L. plantarum colonies were enumerated (CFU/g). The scanning electron microscope method was applied to confirm the penetration of impregnation solutions into the intercellular spaces of fruit tissue. On day 28, the population of L. plantarum was 5 log CFU/g for apples and 5.5 log CFU/g for bananas. After storage, the number of L. casei in apples was 5 log CFU/g and 5.5 log CFU/g, respectively. L. casei was found on the surface of apple and banana tissue. After one-week, whole phenolic content of probiotic-enriched bananas and apples augmented. After storage, the antioxidant activity of all samples decreased greatly. The sensory qualities of the samples were excellent throughout storage in terms of color, quality, scent, sensitivity, chewiness, and general adequacy. As a result, dried apples and bananas infused with L. plantarum and L. casei might be a novel probiotic meal. RESEARCH HIGHLIGHTS: Dried apples and bananas infused with L. plantarum and L. casei are novel probiotic meal. After one-week, whole phenolic content of probiotic-enriched bananas and apples augmented. The sensory qualities of the samples were excellent throughout storage in terms of color, quality, scent, sensitivity, chewiness, and general adequacy.

Plasmonic AgNPs reinforced flexible hydrogel Surface-Enhanced Raman scattering (SERS) sensor for in-situ detection of curved samples

In-situ detection of analytes on curved surfaces is a persistent and great challenge in the application of SERS technique. A hydrogel-based flexible SERS substrate demonstrates a unique charm for providing close contact to the real-sample surface but limited by its weak enhancement capability in the quantitative analysis. Herein, we report a reinforced hydrogel by reduction of plasmonic silver nanoparticles (AgNPs) on a PNIPAM membrane with the aid of polyvinylpyrrolidone (PVP) surfactant. The modification of AgNPs on PNIPAM endows the AgNPs/PNIPAM composite hydrogel with good mechanical properties and reinforced swelling property. Moreover, the AgNPs/PNIPAM hydrogel-based SERS sensor exhibits an enhanced SERS sensitivity as well as improved signal uniformity (RSD ＜10 %), and the detection concentration for pesticide molecules can be down to 10-9 M. Due to the fingerprint feature of SERS, multicomponent detection of pesticide mixtures was readily realized. In addition, the flexible AgNPs/PNIPAM hydrogel was successfully applied to the curved apple surface for SERS detection. Notably, it is capable of achieving qualitative and quantitative analyses of different pesticides, which has lower analytical concentrations than European Union (EU) standards, indicating the potential applications in rapid in-situ pesticide analysis.

Simple Synthesis of Cellulose-Based Nanocomposites as SERS Substrates for In Situ Detection of Thiram.

There is a growing interest in the use of flexible substrates for label-free and in situ Surface-enhanced Raman Spectroscopy (SERS) applications. In this study, a flexible SERS substrate was prepared using self-assembled Au/Ti3C2 nanocomposites deposited on a cellulose (CS) paper. The Au/Ti3C2 nanocomposites uniformly wrapped around the cellulose fibers to provide a three-dimensional plasma SERS platform. The limit of detection (LOD) of CS/Au/Ti3C2 was as low as 10-9 M for 4-mercaptobenzoic acid(4-MBA) and crystal violet (CV), demonstrating good SERS sensitivity. CS/Au/Ti3C2 was used for in situ SERS detection of thiram on apple surfaces by simple swabbing, and a limit of detection of 0.05 ppm of thiram was achieved. The results showed that CS/Au/Ti3C2 is a flexible SERS substrate that can be used for the detection of thiram on apple surfaces. These results demonstrate that CS/Au/Ti3C2 can be used for the non-destructive, rapid and sensitive detection of pesticides on fruit surfaces.

A SERS-based point-of-care testing approach for efficient determination of diquat and paraquat residues using a flexible silver flower-coated melamine sponge

Diquat (DQ) and paraquat (PQ) residues in food are potential hazards to consumers' health. Point-of-care testing (POCT) of them remains challenging. Based on surface-enhanced Raman spectroscopy (SERS) technology, we developed a POCT strategy for DQ and PQ on apple surface and in apple juice. A point-of-use composite was fabricated using a piece of porous melamine sponge (MS) modified with silver nanoflowers (AgNFs), combining the specificity of the SERS fingerprint and the excellent adsorption capacity of MS. Using this dual-functional AgNFs@MS, the on-site determination of the DQ and PQ residues was completed within 3 min without pretreatment. Clear trends were observed between SERS intensity and logarithmic concentrations, with r values from 0.962 to 0.984. The limit of detection of DQ and PQ were 0.14–0.70 ppb in apple juice and on apple surface. This study provides a new point-of-use alternative for rapidly detecting DQ and PQ residues in nonlaboratory settings.

The stress response of Listeria monocytogenes inoculated on fresh apples exposed to gaseous chlorine dioxide

AbstractFresh apples are vulnerable to Listeria monocytogenes (L. monocytogenes) contamination during handling and storage causing subsequent foodborne outbreaks. Gaseous chlorine dioxide (ClO2) is an effective antimicrobial agent against L. monocytogenes and could augment food safety. This study investigates the stress and virulence response of L. monocytogenes on apples exposed to a sublethal concentration of ClO2. L. monocytogenes was inoculated on the surface of apples and exposed to gaseous ClO2 for 1 h. Gene expression analysis via Real‐Time quantitative polymerase chain reaction (RT‐qPCR) revealed minimal changes in gene expression compared to control samples. However, a surface‐dependent response was observed, with slight upregulation of relevant virulence (hly) and stress (clpC) genes on more hydrophilic surfaces. These findings indicate that gaseous ClO2 treatment can provide a biostatic effect against L. monocytogenes on fresh produce, shedding light on its antimicrobial mechanism and potential for enhancing apple safety.

The effect of cold plasma treatment on quality attributes and shelf life of apples

The effectiveness of atmospheric air dielectric barrier discharge (DBD) cold plasma was compared with existing decontamination methods for improving the quality and shelf life of apples. The samples were stored in two different storage conditions (ambient, 27 ± 3 °C; and cold, 10 ± 1 °C) to check the effect of storage temperature on their shelf life. The microbial load on the apples was reduced after all the treatments, and samples stored at 10 ℃ showed a higher reduction in microbial load with increased shelf life. The polyphenol oxidase (PPO) and phenylalanine ammonia-lyase (PAL) enzyme activities showed a significant reduction after cold plasma treatment; however, peroxidase (POD) activity showed a lower reduction than PPO and PAL enzymes. Color changes evaluated using L*, a*, and b* values showed higher retention for the red color in the treated samples compared to control samples, and the firmness of all treated samples was maintained throughout the storage period. The total phenolic content (TPC) increased after cold plasma treatment and carotenoid content was retained in the treated samples. In addition, the present study demonstrated that the novel cold plasma decontamination method was more effective in reducing the pesticides (Malathion, Fenthion, and Chlorpyrifos) from the apple surfaces than the conventional methods. Cold plasma can be a better alternative for decontaminating apples; however, other hurdles may be required to increase their shelf life further.

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

The sugar apple (Annona squamosa) is valued for its taste, nutritional richness, and versatility, making it suitable for fresh consumption and medicinal use with significant commercial potential. Widely found in the tropical Americas and Asia’s tropical or subtropical regions, it faces challenges in post-harvest ripeness assessment, predominantly reliant on manual inspection, leading to inefficiency and high labor costs. This paper explores the application of computer vision techniques in detecting ripeness levels of harvested sugar apples and proposes an improved deep learning model (ECD-DeepLabv3+) specifically designed for ripeness detection tasks. Firstly, the proposed model adopts a lightweight backbone (MobileNetV2), reducing complexity while maintaining performance through MobileNetV2′s unique design. Secondly, it incorporates the efficient channel attention (ECA) module to enhance focus on the input image and capture crucial feature information. Additionally, a Dense ASPP module is introduced, which enhances the model’s perceptual ability and expands the receptive field by stacking feature maps processed with different dilation rates. Lastly, the proposed model emphasizes the spatial information of sugar apples at different ripeness levels by the coordinate attention (CA) module. Model performance is validated using a self-made dataset of harvested optical images categorized into three ripeness levels. The proposed model (ECD-DeepLabv3+) achieves values of 89.95% for MIoU, 94.58% for MPA, 96.60% for PA, and 94.61% for MF1, respectively. Compared to the original DeepLabv3+, it greatly reduces the number of model parameters (Params) and floating-point operations (Flops) by 89.20% and 69.09%, respectively. Moreover, the proposed method could be directly applied to optical images obtained from the surface of the sugar apple, which provides a potential solution for the detection of post-harvest fruit ripeness.

Optimization of cotton SERS substrates based on different weave structures for surface trace detection

Cotton fabrics have been used to fabricate Surface-enhanced Raman spectroscopy (SERS) substrates due to their excellent properties such as flexibility, hygroscopicity, and large-scale production. The morphology of the SERS substrate has a great influence on the deposition of noble metal nanoparticles and affects the Raman-enhanced signal. Although numerous research reports have been conducted on SERS substrates made from cotton, there has been limited exploration on the impact of the weave structure of cotton fabric on SERS performance. Herein, the cotton SERS substrates were prepared by depositing silver nanoparticles (Ag NPs) on cotton fabrics with different weave structures by a simple vacuum-thermal evaporation method. The influence of substrate morphology on the deposition arrangement of Ag NPs and the enhancement of SERS performance was explored. The analysis findings demonstrate that the cotton SERS substrate displays exceptional Raman signal enhancement performance. Thereinto, the satin SERS substrate exhibited a stronger Raman signal due to its smoother surface favoring the deposition of Ag NPs. The limit of detection for p-aminothiophenol (PATP) was established at a low of 10−8 M, with an RSD value of 9.96%. Moreover, the satin SERS substrate demonstrated an enhancement factor of 5.18 × 105. In addition, it has excellent mechanical flexibility and good hygroscopicity, and thiram on the apple surface can be detected by simple wiping. Cotton SERS substrates show great potential in food safety analysis and monitoring. More importantly, this study provides assistance for the selection of SERS substrates for traditional cotton fabrics, and further promotes the broadening of the application fields of smart cotton fabrics.

A new strategy for constructing 3D hybrid graphene/Au/rectangular pyramids PMMA on a flexible SERS substrate for trace molecule detection

Surface enhanced Raman spectroscopy (SERS) has attracted interest in the fields of hazardous substance detection and food safety. However, its limited sensitivity and SERS signal consistency inhibit the widespread use of SERS technology. Herein, a novel approach for the fabrication of a graphene/Au NP/rectangular pyramid PMMA hybrid SERS substrate (Gr/Au/RP PMMA) with high uniformity and sensitivity is presented. The Gr/Au/RP PMMA substrate is machined by indentation firstly, then following wet etching of the copper substrate. The Raman enhancement of Gr/Au/RP PMMA is studied in terms of both electromagnetic enhancement and chemical enhancement. The field strength enhancement of the obtained Gr/Au/RP PMMA substrate is more than 4.9 times greater than that of the Gr/Au/flat PMMA substrate, established using a COMSOL simulation. By actively modulating the machining parameters on a Cu-Gr surface, the number of Gr layers, as well as the Gr/Au/RP PMMA topography, can be controlled, which can effectively influence the performance of the SERS substrate. Moreover, compared with other SERS substrates, including Gr/Au/RP PMMA, Gr/Au/flat PMMA, Au/Gr/RP Cu, Gr/RP PMMA, and Au/RP PMMA, the Raman intensities of R6G were strongest on the Gr/Au/RP PMMA. In addition, the minimum detectable concentrations of paraquat (PQ) and 2,4-d used in situ detection were 10−8 M and 10−6 M, respectively, after being covered with the Gr/Au/RP PMMA flexible substrate. To test its practical applicability with actual fruit surfaces, the Gr/Au/RP PMMA flexible substrate was used for the in situ SERS detection of PQ on apples. Clear Raman peaks of 10−7 M PQ were detected on the apple surface.

A stable and flexible Au@Ag NPs/PVA SERS platform for thiram residue detection on rough surface

Flexible and transparent surface-enhanced Raman scattering (SERS) substrates have gained great attention in analysis field as they offer a fast, non-destructive, and highly sensitive platform for in-situ detection. In this work, we present a facile one-pot strategy for synthesizing gold-cored silver shell nanoparticles (Au@Ag NPs) in the polyvinyl alcohol (PVA) colloid. With no other reducing agents, PVA can serve as both reducing and stabilizing agents for forming Au@Ag NPs. Besides, PVA acts as a scaffold to maintain SERS “hot-spots” by preventing nanoparticle aggregation. By using this flexible Au@Ag NPs/PVA colloid, the analytes can be extracted from rough surfaces for SERS measurements with excellent sensitivity, repeatability and stability. The SERS activity of the Au@Ag NPs/PVA remained at 89.8% even after 120 days of storage at room temperature in sealed air atmosphere. The selective detection of thiram residues on the surface of fruits and vegetables was successfully achieved. The limits of detection for thiram residues on apple and tomato surfaces were measured to be 0.58 and 0.56 ng cm−2, respectively, with recovery rate ranging from 91% to 107%. This work demonstrates the immense application potential of SERS colloid platform in the fields of food safety and environmental analysis.

Metabolomic insights into the browning inhibition of fresh-cut apple by hydrogen sulfide

Hydrogen sulfide (H2S) is known to effectively inhibit the browning of fresh-cut apples, but the mechanism at a metabolic level remains unclear. Herein, non-targeted metabolomics was used to analyze metabolic changes in surface and internal tissues of fresh-cut apple after H2S treatment. The results showed that prenol lipids were the most up-accumulated differential metabolites in both surface and inner tissue of fresh-cut apple during browning process, which significantly down-accumulated by H2S treatment. H2S treatment reduced the consumption of amino acid in surface tissue. Regarding inner tissue, H2S activated defense response through accumulation of lysophospholipid signaling and induced the biosynthesis of phenolic compounds. We therefore propose that H2S inhibited the surface browning of fresh-cut apple by reducing the accumulation of prenol lipids, directly delaying amino acid consumption in surface tissue and indirectly regulating defense response in inner tissue, which provides fundamental insights into browning inhibition mechanisms by H2S.

Lotus leaf-inspired biomimetic SERS substrate for detection of thiram on apple

Lotus leaf is a super-hydrophobic material found in nature. Drawing inspiration from the micro-nano structure of lotus leaf surface, a hybrid surface-enhanced Raman spectroscopy (SERS) substrate owning both hydrophobic and hydrophilic properties was developed. The biomimetic part, which was fabricated by replicating the surface structure of lotus leaf using polydimethylsiloxane, exhibited a hydrophobic surface with a contact angle of 150.3°. Meanwhile, silver nanoparticles-coated filter paper was assembled on the top of the biomimetic part to render hydrophilic regions for SERS detection. Silver nanoparticles on the surface of filter paper owned a size of approximately 70–80 nm with an interval of about 5–10 nm. The hydrophobic/hydrophobic hybrid regions on the substrate significantly enhanced Raman signals of rhodamine 6g with mitigated coffee ring effects, showing a limit of detection low to 10−10 M. Notably, the limit of detection of this technique for thiram on apple surface reached 0.5 ng/cm2, which was lower than the reported methods. The prepared substrate displayed exceptional uniformity and stability, with an RSD value of 7.21% calculated based upon 30 randomly selected Raman signals, and experienced a decrease in signal by only 29.7% after 30-d storage. This biomimetic substrate can be utilized for batch detection of pesticide residues on fresh produce with high sensitivity, thereby owning great potential for application as a rapid detection tool for food safety.

Flexible Surface-Enhanced Raman Scattering Tape Based on Ag Nanostructured Substrate for On-Site Analyte Detection.

Surface-enhanced Raman scattering (SERS) has emerged as a powerful surface analytical technique that amplifies Raman scattering signals of molecules adsorbed onto metal nanostructured surfaces. The droplet reaction method has recently been employed to fabricate large-scale microring patterns of silver (Ag) nanostructures on rigid substrates, which enables sensitive detection within the ring area. However, these rigid substrates present limitations for direct on-site detection of analyte residues on irregular sample surfaces. There is a need to develop soft and flexible SERS substrates that can intimately conform to arbitrary surfaces. In this study, we presented a SERS substrate using flexible and adhesive tape as the supporting material. This SERS tape was fabricated by repeatedly transferring presynthesized Ag nanostructures from a rigid substrate to the tape. For a model compound adenine, our SERS tape exhibited a good linear response from 5 × 10-4 M to 5 × 10-5 M with a low limit of detection (LOD) of 5 × 10-7 M and displayed a SERS enhancement factor (EF) of 3.2 × 105. The relative standard deviation (RSD) of SERS intensity achieved was as low as 1.93%, indicating its outstanding uniformity. The as-prepared SERS tape was used for in situ detection of pesticide residue on an apple surface and dye residue on human hair. Leveraging the large surface area of Ag nanostructure patterns from the droplet reaction, the developed SERS tape demonstrates excellent performance in terms of sensitivity and uniformity. The successful detection of analyte residues on arbitrary surfaces of apple and human hair highlights the potential of this flexible SERS tape for real-world applications across various industries for enhanced diagnostic accuracy.

Three-dimensional visualization and detection of early bruise in apple based on near-infrared hyperspectral imaging coupled with geometrical influence correction

Visible-near-infrared (Vis-NIR) spectral imaging holds great promise for the automatic detection of fruit defects. However, uneven brightness resulting from fruit geometry and the limitations of one-directional imaging significantly restrict the current detection to a limited area. This study presents a rotation measurement system that combines a line-scan NIR-hyperspectral imaging (HSI) camera with a laser profile. A total of 72 apple samples with bruises in the central and edge regions were prepared. A 360° scanning approach was employed to collect HSI and shape data from the entire surface of the samples over a 6-h post-bruising period. Height and angle corrections were applied to eliminate the surface geometric influences on the HSI data, resulting in improved reflectance spectrum uniformity. A two-step principal component analysis method was employed for image enhancement, followed by a straightforward bruise detection technique using global segmentation and connected-domain selection. The results demonstrated an overall improvement in bruise detection over time. Moreover, the correction significantly enhanced the detection accuracy. After 6 h of bruising, the corrected data achieved a classification accuracy of 90.3% and an identification rate of 83.3% for central bruises and 61.1% for edge bruises, whereas the uncorrected data yielded 70.8%, 58.3%, and 31.9%, respectively. Thus, this study successfully detected early bruising across the entire surface of apples and improved the detection in low-intensity edge areas. The proposed method has the potential to contribute to the comprehensive evaluation of agricultural products with irregular geometries.

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.