Apple Surface Research Articles

Fast Screening Test of Apple Juice and Surface Water for Patulin Recognition and Quantification

Since a high concentration of patulin causes cytotoxicity, a new approach to the determination of patulin plays an important role in the research and development of new control technologies. The main objective of the paper is to provide a fast screening method for obtaining clean water and high-quality apple juice. Square-wave voltammetry method using a modified Mn (OAP) Cl screen-printed carbon electrode was used for the fast screening test of apple juice and surface water for patulin recognition and quantification. A number of experimental parameters, such as scan rate, type of buffer solution, and pH, were studied and optimized. Thus, a linear concentration dependence was determined in the range 1.00 × 10–12 to 1.00 × 10–8 mol L–1, while the quantification limit was 1.00 × 10–12 mol L–1 and the detection limit was 3.33 × 10–13 mol L–1. High reliability was achieved at the assay of patulin in water and apple juice samples, with a recovery rate of 96.27–99.88%, relative standard deviation (RSD), and a bias value of less than 5.00%. Based on the remarkable outcomes, it can be inferred that the sensor holds promise as a robust instrument for point-of-care detection in the times ahead.

A multiscale Monte Carlo simulation of irradiating a typical-size apple by low-energy X-rays and electron beams

Irradiation of foods and fruits by ionizing radiation effectively eliminates pathogenic microorganisms primarily present on the surface. Recently, much attention has been paid to the use of low-energy X-rays and electron beams for food irradiation. Dosimetric investigation of foods with complex shapes is experimentally challenging. In this study, a multiscale Monte Carlo simulation was performed to compare the dose distributions of low-energy X-rays and electrons in a typical-size apple as well as to calculate the DNA damages induced by secondary electrons in a bacterial cell model. Several energies up to 500 keV and tube voltages up to 200 kV were considered for incident electrons and X-rays, respectively. The results indicate that on a microscopic scale, the yields of single- and double-stranded DNA breaks induced by X-rays and electrons of different energies are nearly identical. In a macroscopic scale, a two-sided electron beam configuration plus a 90-degree rotation of the apple (or the beam) provides an appropriate dose uniformity on the apple surface up to a depth of 40 μm. In the case of low-energy X-rays, apple rotation is not required when a two-sided irradiation is applied. Generally, X-rays are superior to electrons for the surface treatment of apples due to their lower dose uniformity ratios on the surface.

Understanding the impact of a non-ionic surfactant alkylphenol ethoxylate on surface-enhanced Raman spectroscopic analysis of pesticides on apple surfaces

Pesticide active ingredients (AIs) are often applied with adjuvants to facilitate the stability and functionality of AIs in agricultural practice. The objective of this study is to investigate the role of a common non-ionic surfactant, alkylphenol ethoxylate (APEO), on the surface-enhanced Raman spectroscopic (SERS) analysis of pesticides as well as its impact on pesticide persistence on apple surfaces, as a model fresh produce surface. The wetted areas of two AIs (thiabendazole and phosmet) mixed with APEO were determined respectively to correct the unit concentration applied on apple surfaces for a fair comparison. SERS with gold nanoparticle (AuNP) mirror substrates was applied to measure the signal intensity of AIs with and without APEO on apple surfaces after a short-term (45 min) and a long-term (5 days) exposure. The limit of detection (LOD) of thiabendazole and phosmet using this SERS-based method were 0.861 ppm and 2.883 ppm, respectively. The result showed that APEO decreased the SERS signal for non-systemic phosmet, while increased SERS intensity of systemic thiabendazole on apple surfaces after 45 min pesticide exposure. After 5 days, the SERS intensity of thiabendazole with APEO was higher than thiabendazole alone, and there was no significant difference between phosmet with and without APEO. Possible mechanisms were discussed. Furthermore, a 1% sodium bicarbonate (NaHCO3) washing method was applied to test the impact of APEO on the persistence of the residues on apple surfaces after short-term and long-term exposures. The results indicated that APEO significantly enhanced the persistence of thiabendazole on plant surfaces after a 5-day exposure, while there was no significant impact on phosmet. The information obtained facilitates a better understanding of the impact of the non-ionic surfactant on SERS analysis of pesticide behavior on and in plants and helps further develop the SERS method for studying complex pesticide formulations in plant systems.

Theory of an m × n apple surface network with special boundary

As is well known, any condition change in a network model will affect the research method and result. This paper studies an arbitrary m × n apple surface network with a pair of non-uniform boundary resistors, which is a complex network that has not been solved before. The research process of this paper consists of four strict basic steps according to RT-V theory. With the breakthrough of matrix transformation and eigenvector solution, two exact potential function formulae and two equivalent resistance formulae are derived. A series of applications and special cases are given to help readers understand the results. In particular, we have drawn eight visualized potential function images, which can intuitively understand the changing characteristics of potential function. The establishment of theoretical results provides a new theoretical basis for related interdisciplinary research.

Apple Grading Based on Multi-Dimensional View Processing and Deep Learning.

This research proposes an apple quality grading approach based on multi-dimensional view information processing using YOLOv5s network as the framework to rapidly and accurately perform the apple quality grading task. The Retinex algorithm is employed initially to finish picture improvement. Then, the YOLOv5s model, which is improved by adding ODConv dynamic convolution and GSConv convolution and VoVGSCSP lightweight backbone, is used to simultaneously complete the detection of apple surface defects and the identification and screening of fruit stem information, retaining only the side information of the apple multi-view. After that, the YOLOv5s network model-based approach for assessing apple quality is then developed. The introduction of the Swin Transformer module to the Resnet18 backbone increases the grading accuracy and brings the judgment closer to the global optimal solution. In this study, datasets were made using a total of 1244 apple images, each containing 8 to 10 apples. Training sets and test sets were randomly created and divided into 3:1. The experimental results demonstrated that in the multi-dimensional view information processing, the recognition accuracy of the designed fruit stem and surface defect recognition model reached 96.56% after 150 iteration training, the loss function value decreased to 0.03, the model parameter was only 6.78 M, and the detection rate was 32 frames/s. After 150 iteration training, the average grading accuracy of the quality grading model reached 94.46%, the loss function value decreased to 0.05, and the model parameter was only 3.78 M. The test findings indicate that the proposed strategy has a good application prospect in the apple grading task.

Application of aggregation behavior of nonionic surfactants to develop a smart detergent for washing fruits with emphasis on pesticide residues removal

AbstractModel smart detergents for fruit washing were designed. Their physicochemical and functional properties were determined, with particular emphasis on the efficiency of removing pesticide residues. The specific behavior of nonionic surfactants near the so‐called cloud point was used in the design of the new smart detergent. The high performance of detergents was related to the temperature‐dependent aggregation behavior of surfactants. Liquid chromatography coupled with mass spectrometry (LC–MS/MS) was used to evaluate the washing efficiency. Four types of pesticides (boscalid, acetamiprid, pyraclostrobin and pendimethalin) were applied to apples and then washed with designed detergents. Washing efficiency was evaluated based on the change in pesticide content on the apple surface. Compared to water, the model detergents were found to be more effective in removing pesticide residues, reaching values above 80%. The detergents were also evaluated for skin effects and revealed a low potential for skin irritation.

Genomic Analyses of a Fungemia Outbreak Caused by Lodderomyces elongisporus in a Neonatal Intensive Care Unit in Delhi, India.

Lodderomyces elongisporus is a recently emerging yeast pathogen predominantly reported in adult patients who had immunosuppression and/or intravenous access devices. Here, we report a fungemia outbreak caused by L. elongisporus in a neonatal intensive care unit (NICU) in Delhi, India, from September 2021 to February 2022. All 10 neonates had low birthweight, and nine of the patients survived after amphotericin B treatment. Whole-genome sequence analyses of the patient isolates as well as those from other sources in India grouped them into two clusters: one cluster consists of isolates exclusively from stored apples and the other cluster includes isolates from patients, clinical environments, and stored apples. All outbreak strains from patients were closely related to each other and showed highly similar heterozygosity patterns across all 11 major scaffolds. While overall very similar, strains from the inanimate environment of the same neonatal intensive care unit showed loss of heterozygosity at scaffold 2 (NW_001813676) compared to the patient strains. Interestingly, evidence for recombination was found in all samples. All clinical strains were susceptible to 10 tested antifungal drugs, and comparisons with strains with high fluconazole MICs derived from the surface of stored apples revealed significant genome divergence between the clinical and apple surface strains, including 119 nonsynonymous single nucleotide polymorphisms (SNPs) in 24 triazole resistance-related genes previously found in other Candida spp. Together, our results indicate significant diversity, recombination, and persistence in the hospital setting and a high rate of evolution in this emerging yeast pathogen. IMPORTANCE Lodderomyces elongisporus was initially considered a teleomorph of Candida parapsilosis. However, DNA sequence analyses revealed it as a distinctive species. Invasive infections due to L. elongisporus have been reported globally. We report an outbreak of fungemia due to L. elongisporus in a NICU affecting 10 preterm, low-birthweight neonates during a period of 6 months. The outbreak investigation identified two environmental sites, the railing and the temperature panel of the neonate open care warmer, harboring L. elongisporus. Whole-genome sequencing confirmed that the neonate isolates were closely related to each other whereas strains from the inanimate clinical environment were related to clinical strains but showed a marked loss of heterozygosity. Further, L. elongisporus strains recovered previously from the surface of stored apples showed high fluconazole MICs and alterations in triazole resistance-related genes. Genome-wide SNP comparisons revealed recombination as an important source for genomic diversity during adaptation of L. elongisporus to different environments.

Effects of surface micro-structure of fruits on droplet evaporation process: Peach and apple cases

Droplet evaporation at the fruit surface is a common issue related to the efficiency of pesticide spraying as well as freshness preservation. Many works have been carried out demonstrating the impacts of substrate characteristics on the droplet profile, wettability, and evaporation properties in the field of industrial applications. However, the influences of specific fruit skin on the droplet evaporation rate have been rarely studied. Thus, the present works aim to explore the effects of different surface micro-structures of fruits on the droplet properties experimentally through the 3D video microscope, drop shape analyzer, and infrared camera. Apple and peach have been selected respectively as two examples of waxy and hairy skins, and comparisons have been made focusing on the surface roughness, droplet profile including contact angle, contact area, and contact diameter, evaporation rate, and temperature distribution of droplets with a constant initial volume of 2ul. The results show that the differences in micro-structure for apple and peach surfaces have significant impacts on the droplet evaporation performance. A larger evaporation rate has been found for apple skin with three distinct phases including spreading, pinning, and shrinkage periods. However, the pinning phenomenon tends to be broken for the peach case due to the unique properties of super-hydrophobic surfaces and the existence of fluff. Moreover, lower values of temperature at the top center of a droplet can be always observed during the whole evaporating process with peak values of temperature gradient near the triple-phase region.

Multi-Camera-Based Sorting System for Surface Defects of Apples.

In this paper, we addressed the challenges in sorting high-yield apple cultivars that traditionally relied on manual labor or system-based defect detection. Existing single-camera methods failed to uniformly capture the entire surface of apples, potentially leading to misclassification due to defects in unscanned areas. Various methods were proposed where apples were rotated using rollers on a conveyor. However, since the rotation was highly random, it was difficult to scan the apples uniformly for accurate classification. To overcome these limitations, we proposed a multi-camera-based apple sorting system with a rotation mechanism that ensured uniform and accurate surface imaging. The proposed system applied a rotation mechanism to individual apples while simultaneously utilizing three cameras to capture the entire surface of the apples. This method offered the advantage of quickly and uniformly acquiring the entire surface compared to single-camera and random rotation conveyor setups. The images captured by the system were analyzed using a CNN classifier deployed on embedded hardware. To maintain excellent CNN classifier performance while reducing its size and inference time, we employed knowledge distillation techniques. The CNN classifier demonstrated an inference speed of 0.069 s and an accuracy of 93.83% based on 300 apple samples. The integrated system, which included the proposed rotation mechanism and multi-camera setup, took a total of 2.84 s to sort one apple. Our proposed system provided an efficient and precise solution for detecting defects on the entire surface of apples, improving the sorting process with high reliability.

Antimicrobial activity of Emodin in combination with visible light against Escherichia coli and Staphylococcus aureus: Applications for food safety technology

In order to promote the application of natural photosensitizer, the photodynamic antibacterial effect of emodin combined with visible light on Escherichia coli and Staphylococcus aureus were studied in this work. The results showed that emodin mediated photodynamic antibacterial activity was dependent on concentration, light time and pH. Confocal microscopy and scanning electron microscopy confirmed that emodin can produce reactive oxygen species to kill bacteria under visible light irradiation. After treatment with emodin and visible intermittent radiation, S. aureus inoculated on the surface of fresh-cut apples was significantly reduced to 1.5 Log CFU/mL during 5 days of storage, and the storage method had no significant effect on the weight and appearance of apples. The results indicate that photodynamic inactivation of foodborne pathogens by combining emodin with visible light is a potential food safety technology.

Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3

Apples are ranked third, after bananas and oranges, in global fruit production. Fresh apples are more likely to be appreciated by consumers during the marketing process. However, apples inevitably suffer mechanical damage during transport, which can affect their economic performance. Therefore, the timely detection of apples with surface defects can effectively reduce economic losses. In this paper, we propose an apple surface defect detection method based on weight contrast transfer and the MobileNetV3 model. By means of an acquisition device, a thermal, infrared, and visible apple surface defect dataset is constructed. In addition, a model training strategy for weight contrast transfer is proposed in this paper. The MobileNetV3 model with weight comparison transfer (Weight Compare-MobileNetV3, WC-MobileNetV3) showed a 16% improvement in accuracy, 14.68% improvement in precision, 14.4% improvement in recall, and 15.39% improvement in F1-score. WC-MobileNetV3 compared to MobileNetV3 with fine-tuning improved accuracy by 2.4%, precision by 2.67%, recall by 2.42% and F1-score by 2.56% compared to the classical neural networks AlexNet, ResNet50, DenseNet169, and EfficientNetV2. The experimental results show that the WC-MobileNetV3 model adequately balances accuracy and detection time and achieves better performance. In summary, the proposed method achieves high accuracy for apple surface defect detection and can meet the demand of online apple grading.

Evaluation of a new apple in-field sorting system for fruit singulation, rotation and imaging

Automated, computer-imaging based in-field grading and sorting of apples at the time of harvest has the potential to help growers achieve significant cost savings in postharvest storage and packing. Singulation and rotation of fruit are essential steps for a computer imaging-based grading/sorting system. We have recently developed a novel compact, low-cost in-field apple sorting system, which consists of pairs of multi-stage helical conveyance drives to singulate, rotate and advance apples so that they can be inspected by a computer vision unit for quality grading. This study was aimed at evaluating the singulating and rotating performance of the sorting system for automatic inspection and grading of apples, in terms of conveying speed, fruit size, and the percentage of apple exposure area for imaging. Two apple varieties (i.e., ‘Golden Delicious’ and ‘Delicious’) were used for experimental evaluations. To determine the rotational behavior of apples, each quadrant of the apple surface was painted with different colors. Images were acquired for the apples at 15 frames per second and then automatically segmented for the estimation of exposed areas in terms of pixels. It was found that the number of images acquired for each fruit varied from 24 to 9 images when the speed of the sorting system was changed from 1 to 3 apples per second per lane. The singulating and rotating mechanism (SRM) provided continuous and relatively even rotation of the apples while conveying, which was confirmed by cumulative percentages of the four colored areas of ‘Golden Delicious’ and ‘Delicious’ apples at the three sorting speeds. At the sorting speed of 1 apple/s, the entire surface of each fruit was imaged for more than 4 times, while at 3 apples/s, the surface of the apples was imaged for at least 1.4 times. Hence, the sorting system with 3 sorting lanes is able to perform in-field quality grading of apples at a throughput of 9 or more apples/s.

Automatic Detection of Small Sample Apple Surface Defects Using ASDINet.

The appearance quality of apples directly affects their price. To realize apple grading automatically, it is necessary to find an effective method for detecting apple surface defects. Aiming at the problem of a low recognition rate in apple surface defect detection under small sample conditions, we designed an apple surface defect detection network (ASDINet) suitable for small sample learning. The self-developed apple sorting system collected RGB images of 50 apple samples for model verification, including non-defective and defective apples (rot, disease, lacerations, and mechanical damage). First, a segmentation network (AU-Net) with a stronger ability to capture small details was designed, and a Dep-conv module that could expand the feature capacity of the receptive field was inserted in its down-sampling path. Among them, the number of convolutional layers in the single-layer convolutional module was positively correlated with the network depth. Next, to achieve real-time segmentation, we replaced the flooding of feature maps with mask output in the 13th layer of the network. Finally, we designed a global decision module (GDM) with global properties, which inserted the global spatial domain attention mechanism (GSAM) and performed fast prediction on abnormal images through the input of masks. In the comparison experiment with state-of-the-art models, our network achieved an AP of 98.8%, and a 97.75% F1-score, which were higher than those of most of the state-of-the-art networks; the detection speed reached 39ms per frame, achieving accuracy-easy deployment and substantial trade-offs that are in line with actual production needs. In the data sensitivity experiment, the ASDINet achieved results that met the production needs under the training of 42 defective pictures. In addition, we also discussed the effect of the ASDINet in actual production, and the test results showed that our proposed network demonstrated excellent performance consistent with the theory in actual production.

Electrical property of an m × n apple surface network

The circuit network model has important application value in many disciplines, and many complex problems can be simulated by establishing the network model. However, looking for the explicit solutions of the electrical property in an arbitrary resistor network is important but difficult. In this paper an arbitrary m × n apple surface network is researched by the Recursion-Transform method with voltage parameters (RT-V) for the first time, and the RT-V method consists of four rigorous basic steps. We study and derive the exact analytical expression of potential function and the equivalent resistance formula, and some special cases and applications of them are also discussed. The theoretical results obtained are concise and provide a new theoretical basis for the research of related subjects.

Raman enhancement properties of TiO2/Ag carbon fiber cloth using the ultraviolet-induced method: high sensitivity, flexible and reusable

We report a low-cost fabrication strategy to prepare a large-area carbon fiber cloth (CFC) coated by Ag nanoparticles (AgNPs) as a flexible surface-enhanced Raman scattering substrate. AgNPs were deposited on a hydrophilized CFC by ultraviolet (UV) irradiation of AgNO3 solution, named UV-AgNPs@CFC. The UV irradiation duration and AgNO3 solution concentration can affect the AgNPs structure. SERS property is investigated using rhodamine 6 G (R6G) and crystal violet (CV) as standard analytes and the detection concentration level is down to 10−10 mol/L. The analytical enhancement factor can reach 1.22 × 109. Also, the substrate has remarkable stability and uniformity; the relative standard deviation (RSD) of the characteristic peak calculated at 611cm−1 is 14.4%. Additionally, the UV-AgNPs@CFC substrate can detect two different molecules simultaneously. Our flexible SERS substrate enables efficient molecular extraction and Raman measurements on the curved surface of apples. The detection concentration level for CV is down to 10−5 mol/L. Moreover, the electric field distribution of the hybrid structure is simulated by the finite difference time domain and COMSOL Multiphysics software, and the maximal electric field intensity is 25.7 V/m. Meanwhile, we deposited AgNPs on the TiO2 nanorods-modified CFC (TiO2-CFC) by UV irradiation. A reusable self-cleaning UV-AgNPs@TiO2-CFC substrate was fabricated.

Measurement of Water Vapor Condensation on Apple Surfaces during Controlled Atmosphere Storage.

Apples are stored at temperatures close to 0 °C and high relative humidity (up to 95%) under controlled atmosphere conditions. Under these conditions, the cyclic operation of the refrigeration machine and the associated temperature fluctuations can lead to localized undershoots of the dew point on fruit surfaces. The primary question for the present study was to prove that such condensation processes can be measured under practical conditions during apple storage. Using the example of a measuring point in the upper apple layer of a large bin in the supply air area, this evidence was provided. Using two independent measuring methods, a wetness sensor attached to the apple surface and determination of climatic conditions near the fruit, the phases of condensation, namely active condensation and evaporation, were measured over three weeks as a function of the operating time of the cooling system components (refrigeration machine, fans, defrosting regime). The system for measurement and continuous data acquisition in the case of an airtight CA-storage room is presented and the influence of the operation of the cooling system components in relation to condensation phenomena was evaluated. Depending on the set point specifications for ventilation and defrost control, condensed water was present on the apple surface between 33.4% and 100% of the duration of the varying cooling/re-warming cycles.

Assessment of black rot development on apple surface using laser biospeckle technique

In our present study, the laser biospeckle technique has been applied to monitor the spread of black rot fungal disease and evaluate the infected area on apple surface for the first time to the best of our knowledge. Image processing algorithms such as inertia moment (IM), absolute value of differences (AVD) and spatial-temporal speckle correlation show the reduction of biospeckle activity of the black rot infected apples in comparison to that of the healthy apples with increase in time, once the visible symptom of the disease sets in. Spectral activity maps obtained using Fujii, inverse Fujii and parameterized Fujii show the spread of this infection with the progression of day. Under exposition test and histogram plot have been used to evaluate the increase of darkness of the infected apple with the progression of day and the results have come out as a replica of the change of colour of the region of interest. % biospeckle activity loss on the final day of the experiment has been found to be 17.83% and 11.91% for healthy apples & 92.42% and 78.63% for infected apples using IM and AVD respectively. An increase in the infected area and % increase in infected area of the region of interest on the image plane has been determined to be 0.33 mm2 and 58.57% respectively on the final day of the experiment.

Early apple bruise recognition based on near-infrared imaging and grayscale gradient images

Early apple bruises, especially those occurring within half an hour, usually have no external symptoms and are difficult to recognize. This study developed a fast and nondestructive detection method for early bruises based on near-infrared camera imaging and image recognition. A total of 31 apple samples were photographed on both sides of each apple. Grayscale images of the sound apples were captured using a near-infrared camera with a wavelength region between 900 and 2350 nm. Images (n = 62) of apples without bruises were collected. The identical apples were artificially damaged and photographed at two stages (0 h, 0.5 h) by the near-infrared camera, and a total of 186 grayscale images were collected. As the light spot on the surface of apples limits the accuracy in detecting defects, a compound image pre-processing method was proposed consisting of nonlinear grayscale transformation and frequency-domain image filtering techniques. Finally, the gradient image is obtained by taking the first-order derivative of the preprocessed image. Since bruise had distinct edges, the gradient grayscale images of apples are more favorable for bruise identification. The compound method obtained a 97.62% classification accuracy for nonbruised apples and apples with fresh bruises. The experimental results show that it is feasible to identify early bruises in apples based on near-infrared camera imaging and gradient grayscale images. In the subsequent study, the method will be further improved, especially the parameters involved in the algorithm could be adjusted to adaptive variables. In addition, the system will be explored to be suitable for online apple bruise detection.

Fate of Listeria innocua on wax-coated Fuji apple surfaces under commercial refrigerated air storage

Wax coating is a common practice to improve glossiness, reduce water and weight loss, and extend the shelf-life of fresh apples. Apples can be contaminated with Listeria monocytogenes during waxing and other steps within preharvest and postharvest processing and handling; however, little is known about the fate of Listeria spp. on wax-coated apples during commercial cold storage. The objective of this study was to assess the fate of Listeria innocua on wax-coated Fuji apples subjected to eighteen weeks of commercial refrigerated air (RA) storage operated at 0.6 °C and ∼ 90 % relative humidity within two cropping seasons. Wax application and the subsequent 2-min drying at 22–60 °C had no impact on the fate of L. innocua and endogenous yeasts and molds on apple surfaces right after drying and over twelve weeks of cold storage. During eighteen weeks of commercial RA storage in the first cropping season, L. innocua was decreased by 1.9 log10 CFU/apple on unwaxed apples and 2.2–2.4 log10 CFU/apples on wax-coated apples at the end of storage. In the following cropping season, the fate of L. innocua on unwaxed apples was not significantly different from that of the first cropping season; there were 1.7 and 1.9–2.0 log10 CFU/apple reduction of L. innocua on unwaxed and waxed apples, respectively, at the end of eighteen weeks’ storage. Wax coating did not impact the survival of endogenous yeasts and molds on apples during eighteen weeks of commercial RA storage in both cropping seasons; there was a 0.4–0.7 log10 CFU/apple increase of yeasts and molds on unwaxed and waxed apples regardless of wax type during the storage. The application of wax coating increased the glossiness of apples and maintained apple weight and diameters but could not prevent firmness and titratable acidity loss during eighteen weeks of commercial RA storage. This study provides the apple industry with information about the behavior of Listeria on waxed fruit during commercial RA storage and highlights the need to develop alternative and effective intervention methods to control Listeria on wax-coated apples.

ZnO nanorods decorated with Ag nanoflowers as a recyclable SERS substrate for rapid detection of pesticide residue in multiple-scenes

Pesticide residues threaten the ecological environment and human health. Therefore, developing high performance SERS substrate to achieve highly sensitive detection of pesticide residues is meaningful. In this study, based on the strategy of combining “hot spots” engineering and material hybridization, we construct a novel hybrid SERS substrate by depositing Ag nanoflowers (NFs) on ZnO nanorods (NRs). Benefiting from the synergistic effect of electromagnetic enhancement and charge transfer effect, the Ag NFs@ZnO NRs substrate exhibits a low detection limit (10−13 M) for crystal violet molecules. This SERS substrate has good uniformity with a relative standard deviation of 7.463 %. Besides, owning to the photocatalytic property of ZnO NRs, the hybrid substrate can degrade probe molecules after SERS detection and realize recyclability. As a demonstration, we employed our SERS substrate for the trace detection of pesticide residues on apple surface and in river water. This study provides a new idea for improving the SERS performance of hybrid substrates.