This study examined the development and trends in non-destructive testing technology for fruit quality. The status of the research field and the application hotspots were investigated to provide a reference for future research in this field. Relevant studies on the non-destructive testing of fruit quality published between 1993 and 2022 were identified in the core database Web of Science. The temporal distribution, spatial distribution, literature features, research progress, and leading research hotspots were quantified and visualised using bibliometrics. The findings revealed that there continues to be active research and publications on non-destructive testing technology for fruit quality, with a good development trend. China and the USA are the major contributors to research on non-destructive testing technology for fruit quality. The major research institutions include Zhejiang University and the United States Department of Agriculture. The major papers are published in Postharvest Biology and Technology and Acta Horticulturae, among others. These studies mainly focus on agriculture, food, and gardening, among other topics. The detection indices mainly concern internal quality, such as sugar degree and soluble solids, and apparent quality, such as hardness. The detection technologies mainly include electronic nose (E-nose) technology, machine vision technology, and spectral detection technology. In the future, technological developments in artificial intelligence and deep learning will further promote the maturation and application of non-destructive testing technologies for fruit quality.

To determine the optimal fertilizer discharging performance, a spiral fertilizer applicator was designed according to orchard agricultural requirements. The influence of different parameter combinations of the spiral speed, blade diameter, and pitch on the coefficient of variation (CV) of the fertilizer discharge uniformity was predicted using a neural-network-based model by using the Box–Behnken design (BBD) test. According to the extracted results, the neural network model has a good prediction ability, with the determination coefficient of the model and the mean relative error reaching 0.99 and 2.29%, respectively. The impact of the fertilizer discharge parameter combinations on the discharging performances was examined from both macroscopic and microscopic perspectives. During the fertilizer discharge process, the openness formed between the spiral blades and fertilizer outlet presented periodic changes with the continuous rotation of the spiral blade, thus resulting in the uneven discharge of the fertilizer particles. In addition, there are interacting force chains among fertilizer particles, which are not broken in time during the fertilizer discharge procedure, thus resulting in uneven fertilizer discharge. With comprehensive consideration of the fertilizer discharge efficiency, the fertilizer discharge effect, and CV of the fertilizer discharge uniformity, the spiral parameter combination of the fertilizer discharge after neural network optimization are as follows: rotating speed of 47.6 rpm, blade diameter of 90 mm, pitch of 60 mm, and CV of fertilizer discharge uniformity of 19.05%. Under this optimal spiral parameter combination, the fertilizer discharge effect and discharge efficiency were considered to be relatively good. Our work provides references for the design optimization of the spiral fertilizer applicator and fertilizer discharge parameter combination.

To investigate the causes of flow fluctuations in a worm-type distributor during the fertilization process, this study employed the discrete element method to simulate the fertilization process. The analysis focused on the influences of force chain evolution on particle flow fluctuations and the effects of the rotational speed. The results indicate that the flow fluctuations in the worm-type distributor are not solely attributed to its helical structure but are closely associated with the evolution of force chains within the particle systems. Furthermore, a one-to-one correspondence between the flow fluctuations and force-chain evolution exists. The rotational speed was found to exert significant influences on the axial and circumferential distributions of the force chains. As the rotational speed increases, the coefficient of variation (CV) of the axial distribution of the force chains gradually decreases. Meanwhile, the uniformity index (UI) of the circumferential distribution of the force chains initially increases and then decreases. In the context of a worm-type distributor, the axial distribution of force chains emerges as the dominant influencing factor for flow fluctuations. For the specific worm-type distributor model chosen in this study, the minimum flow fluctuations were observed at a rotational speed of 80 rpm. These research findings offer valuable insights into understanding the flow fluctuations of worm-type distributors and provide references for further exploration in this field.

To increase the commercial value of damaged fragrant pears and improve marketing competitiveness, this study explored the degree of damage degree and effects of storage time on the internal quality of fragrant pears during storage and predicted the internal quality of fragrant pears using an adaptive neural fuzzy inference system (ANFIS). The internal quality prediction models of damaged fragrant pears during storage with eight membership functions were constructed, and the optimal model was chosen, allowing for accurate internal quality prediction of damaged fragrant pears. The research results demonstrated that the hardness and soluble solid content (SSC) of fragrant pears decrease as the storage time increases. Given the same storage time, the hardness and SSC of fragrant pears are negatively correlated to the degree of damage. The ANFIS modelling technique is feasible for predicting the internal quality of fragrant pears during storage. The best prediction performances for the hardness and SSC of fragrant pears, respectively, are displayed by the ANFIS using the input membership function of trimf (RMSE = 0.1362, R² = 0.9752; RMSE = 0.0315, R² = 0.9892). The findings of this study can be used to predict the storage quality of fruits.

The detection method for technological parameter is outdates as the traditional test cycle is long as well as the measurement error and the test amount are huge. Moreover, it is difficult to disclose the operation mechanism of devices as the operation is time-consuming and laborious. Therefore, numerical simulation was used in this study to reveal the mechanism of the walnut shell-kernel winnowing device. Moreover, the influence of baffle opening combinations, inlet wind velocity and inlet angle on cleaning rate and loss rate was predicted by the neural network model. The results demonstrated that inlet wind velocity was the primary influencing factor of cleaning rate, followed by baffle opening and inlet angle. Besides, inlet wind velocity was the primary influencing factor of loss rate, followed by inlet angle and baffle opening. The winnowing device performed best (79.91% cleaning rate, 14.37% loss rate) when the baffle opening, inlet wind velocity and inlet angle were 7.01 cm, 24.36 m/s, and 9.47°. In addition, 1/8 walnut shells and 1/4 walnut kernels were incorrectly classified due to the increase in inlet wind velocity. The inlet wind velocity was considered the major cause behind the deteriorating winnowing performance of the device. Finally, the bench test and simulation optimization results were compared. The cleaning rate and loss rate relative error during the simulation test was lower than 1.06%, which ascertained the feasibility and validity of the neural network as well as the combined numerical simulation method. This study could be useful for future research and development of shell-kernel winnowing devices for hard nuts.

The separation of walnut kernels and shells has long been regarded as a bottleneck, limiting processing efficiency, product quality, and industry advancement. In response to the challenges of improving separation accuracy and the inadequacy of existing equipment for meeting industry demands, this paper proposes an innovative walnut shell–kernel separation device based on machine vision technology. An experimental system was constructed, and key parameters were optimized to enhance its performance. The device comprises five main modules: material conveyance, image acquisition, control module, sorting module, and frame. Differential separation technology is used to convert the walnut material group into a stable particle flow, addressing the issue of missed selections due to material blockages. An enhanced YOLOv8n algorithm improves small object detection and interference resistance, enabling accurate identification of walnut kernels. The Box–Behnken Design and Artificial Neural Network prediction model was used to determine the optimal operating parameters for the device. Experimental results showed that effective differential separation was achieved when the dual-stage conveyor system operated at speeds of 0.2 m/s and 1 m/s. The improved I-YOLOv8n algorithm reached an accuracy of 98.8%. Using the neural network model, the optimal operational parameters were determined: an air pressure of 0.72 MPa, a jetting component angle of 10.16°, and a sorting height of 105.12 cm. Under these conditions, the device achieved an actual cleaning rate of 93.56%, demonstrating outstanding separation performance. Compared to traditional separation methods, this device offers significant advantages in terms of efficiency, quality, and ease of operation, providing new technological pathways and support for the automation and intelligent transformation of the walnut processing industry. In the future, the device is expected to undergo further improvements to meet broader market demand and serve as a reference for the separation of other agricultural products.