Search Results (39)

To address the issues of high pod damage rate and unpicked pod rate in the picking device of peanut picking combine harvesters during the harvesting of sun-dried peanuts, a low-damage peanut picking device was developed. This device combines flat pin teeth with a two-stage round steel concave screen. Contact models between the picking components and peanut pods, as well as between pods and the concave screen, were analyzed to determine the optimal structural parameters of the picking components and the most suitable concave screen type. Using peanut plants that had been dug, windrowed, and naturally sun-dried in the field for 3–5 days as test material, bench tests were conducted with pod breakage rate and unpicked pod rate as evaluation indices. The installation direction of the picking elements and the combination form of the concave screen were used as experimental factors. The optimal configuration was determined to be flat pin teeth installed with parallel axial forward bending with a tip fillet radius of 6 mm, and a concave screen composed of right round steel + straight round steel with front sparse and rear dense type. Field comparative experiments with a conventional picking device—comprising cylindrical bar teeth and a straight round steel concave screen—showed that the pod breakage rate decreased from 1.92% to 1.17%, and the unpicked pod rate decreased from 1.14% to 0.62%. This study provides a theoretical basis for the structural optimization and performance enhancement of the threshing device in peanut picking combine harvesters. Full article

This article discusses the salt stress in strawberry seedlings under greenhouse conditions in summer. Spectral acquisition equipment was used to obtain spectral data, and the ambient and leaf temperatures were combined to model and analyze the relative chlorophyll content in the strawberry seedling leaves. Four different salt gradients were employed to culture the strawberry seedings: S1 (0 mmol/L NaCl), S2 (50 mmol/L NaCl), S3 (100 mmol/L NaCl), and S4 (150 mmol/L NaCl). The results indicated that the spectral curves of the strawberry seedlings in groups S3 and S4 began to differentiate after day 3 (D3), and their average canopy temperature increased by 2.5 °C and 3.1 °C, respectively. The performance of traditional machine learning models integrating leaf temperature improved by more than 80%. Under each stress treatment, the one-dimensional ResNet model integrated with leaf temperature performed the best, with root mean square and mean absolute errors below 1.7 and 1.5, respectively. These results highlight the potential of incorporating temperature as an additional factor to improve the accuracy of plant stress assessments. By integrating temperature with spectral data, the model enhances the ability to monitor plant health dynamically and provides a more comprehensive understanding of how environmental factors influence plant physiology. Full article

The seedling retrieval mechanism is a crucial component of fully automatic transplanting machines, significantly influencing the quality, reliability, and efficiency of the transplanting process. Nonetheless, the existing seedling retrieval mechanisms in current transplanting machines exhibit several deficiencies, including substantial damage to seedlings and inadequate retrieval accuracy. To overcome these challenges, we propose an integrated approach combining pneumatic and mechanical techniques to further improve performance. By employing a lower thimble elevation and clamping mechanism, alongside a mathematical model based on the seedling removal process, this method ensures precise seedling extraction and minimizes damage to the root system and substrate. The novelty of this study lies in its ability to reduce the adhesion between seedlings and the holes of the plug plate, thereby minimizing non-destructive extraction of the seedlings and preserving the integrity of the matrix, which is essential for ensuring healthy seedling growth. Moreover, the optimization of the seedling retrieval trajectory enhances the accuracy of the seedling retrieval mechanism while also meeting the requisite speed requirements. Experimental results indicate that at a rate of 72 seedlings per minute, the extraction success rate reached 94.90%, and the casting success rate was 98.53%. The seedling injury rate was only 1.95%, resulting in an overall success rate of 91.69%. These findings confirm that the device meets operational efficiency requirements and delivers effective performance. Full article

Soybean plants form symbiotic nitrogen-fixing nodules with specific rhizobia bacteria. The root hair is the initial infection site for the symbiotic process before the nodules. Since roots and nodules grow in soil and are hard to perceive, little knowledge is available on the process of soybean root hair deformation and nodule development over time. In this study, adaptive microrhizotrons were used to observe root hairs and to investigate detailed root hair deformation and nodule formation subjected to different rhizobia densities. The result showed that the root hair curling angle increased with the increase of rhizobia density. The largest curling angle reached 268° on the 8th day after inoculation. Root hairs were not always straight, even in the uninfected group with a relatively small angle (<45°). The nodule is an organ developed after root hair curling. It was inoculated from curling root hairs and swelled in the root axis on the 15th day after inoculation, with the color changing from light (15th day) to a little dark brown (35th day). There was an error between observing the diameter and the real diameter; thus, a diameter over 1 mm was converted to the real diameter according to the relationship between the perceived diameter and the real diameter. The diameter of the nodule reached 5 mm on the 45th day. Nodule number and curling number were strongly related to rhizobia density with a correlation coefficient of determination of 0.92 and 0.93, respectively. Thus, root hair curling development could be quantified, and nodule number could be estimated through derived formulation. Full article

This paper proposes a method for localized damage detection in tomato, with the objective of enabling the detection of bruises prior to sorting. Bioimpedance spectroscopy technology is employed to assess the extent of localized damage in tomato. An equivalent circuit model is constructed, and the impedance spectroscopy data are obtained by developing a local damage measurement platform for tomatoes using a self-designed circular four-electrode BIS sensor. The electrical parameters are then extracted by fitting the constructed equivalent circuit model to the tomato data. Subsequently, we analyze the variation rules of the electrical parameters in different damage levels. To reduce the dimensionality of the features, including biological variables, fitted electrical parameters, and tomato ripeness, we employ Spearman feature selection. We then classify the reduced features by combining the advantages of the support vector machine and the artificial neural network. The results demonstrate that the designed circular four-electrode BIS sensor can non-destructively measure localized damage conditions in tomato. A localized damage measurement platform for tomatoes has been constructed using this sensor. A comparison of the impedance measurements obtained using the designed circular four-electrode BIS sensor with those obtained using a needle sensor proposed by previous scholars revealed that both sensors exhibited a decrease in impedance with increasing damage degree. This finding indicates that the designed circular four-electrode BIS sensor is an effective tool for characterizing damage conditions in tomatoes. The design of the tomato circular four-electrode BIS sensor is an effective means of characterizing tomato damage. The Spearman-SVM-ANN damage classification algorithm, based on the Spearman feature selection, effectively classified tomato damage with a 98.765% accuracy rate. The findings of this study provide a reference for the grading and transportation of tomatoes after harvest. Full article

This study proposes a YOLOv5 inspection model based on polariser filtering (PF) to improve the recognition accuracy of the machine vision inspection model for tea leaf shoots when operating under intense outdoor light. To study the influence of the polariser parameters on the quality of the tea shoot image datasets, we improved the YOLOv5 algorithm module, inputted the results obtained from the spatial pyramid pooling structure in the backbone module into the neck module, set the up-sampling link of the neck module as a low-level feature alignment (LFA) structure, and used a bounding box similarity comparison metric based on the minimum point distance (mpdiou) to improve the accuracy of the YOLOv5 detection model. The mpdiou loss function is used to replace the original loss function. Experimental results show that the proposed method can effectively address the impact of intense outdoor light on tea identification, effectively solving the problem of poor detection accuracy of tea buds in the top view state. In the same identification environment, the model mAP50 value increased by 3.3% compared to that of the existing best mainstream detection model, and the mAP50-90 increased by 3.1%. Under an environment of light intensity greater than $5\times {10}^4 \mathrm{L}\mathrm{u}\mathrm{x}$, the proposed YOLOv5s+LFA+mpdiou+PF model reduced the leakage detection rate by 35% and false detection rate by 10% compared to that with YOLOv5s alone. Full article

As a common vegetable variety, asparagus is rich in B vitamins, vitamin A, and trace elements such as folate, selenium, iron, manganese, and zinc. With the increasing market demand, China has become the world’s largest cultivated area for asparagus production and product exportation. However, traditional asparagus grading mostly relies on manual visual judgment and needs a lot of manpower input to carry out the classification operation, which cannot meet the needs of large-scale production. To address the high labor cost and labor-intensive production process resulting from the large amount of manpower input and low accuracy of existing asparagus grading devices, this study proposed an improved asparagus grading system and method based on TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) objective evaluation and SVM (support vector machine) prediction. The key structure of classification device was analyzed first, the key components were designed, and the structural parameters were determined by theoretical calculation. Through analysis of the factors affecting asparagus quality, three key attributes were determined: length, diameter, and bruises, which were used as reference attributes to conduct experimental analysis. Then, the graded control groups were set up, combining the TOPSIS principle with weighting, and a score for each asparagus sample was determined. These scores were compared with those of a graded control group to derive the grade of each asparagus, and these subsets of the dataset were used as the training set and the test set, excluding the error caused by the subjectivity of the manual judgment. Based on a comparison of the accuracies of different machine learning models, the support vector machine (SVM) was determined to be the most accurate, and four SVM methods were used to evaluate the test set: linear SVM, quadratic SVM, cubic SVM, and medium Gaussian SVM. The test results showed that the grading device was feasible for asparagus. The bruises had a large influence on asparagus quality. The training accuracy of the medium Gaussian SVM method was high (96%), whereas its test accuracy was low (86.67%). The training accuracies and test accuracy of the quadratic and cubic SVM methods were 93.34%. The quadratic SVM and cubic SVM were demonstrated to have better generalization ability than the medium Gaussian SVM method for predicting unknown grades of asparagus and meeting the operational requirements of the asparagus grading. Full article

Tb³⁺-activated LTA zeolite-derived boro-aluminosilicate glass samples with a composition of xTb₂O₃-68(Na₂O-Al₂O₃-SiO₂)–32B₂O₃ (x = 0.2, 1.0 and 10 extra wt%) were prepared using the melt-quenching method. The emission spectra recorded upon ultraviolet (UV) excitation with two different wavelengths of 193 and 378 nm showed blue light (⁵D₃ to ⁷F_J=6,5,4 and ⁵D₄ to ⁷F₆ transitions of Tb³⁺) and green light (⁵D₄ to ⁷F₅ transition of Tb³⁺) emissions with comparable intensities up to a Tb³⁺ concentration of 10 extra wt%. Of note, the mean decay times of the green luminescence of the glass samples were relatively fast (<20 μs). The synthesized glass has potential in applications concerning UV imaging, UV detection, and plasma display panels. Full article

The formation and evolution mechanisms of complex types and scales of marine–continental transitional shale pores are still indefinite, restricting the accurate evaluation of shale reservoir and the effective evaluation of coal-bearing marine–continental transitional shale gas resource quantity. Considering the Shanxi shale in Ordos basin of China as the research object, combining the FE-SEM images and petrophysical analysis, high-pressure mercury intrusion porosimetry, and CO₂ and N₂ adsorption–desorption experiments, the structure characteristics and differential evolution mechanisms of multiscale and multitype of coal-bearing shale pores were discussed. The results show that coal-bearing marine–continental transitional shales are rich in clay minerals and organic matters (OMs). Pores developed within organic matters, clay, and brittle minerals of coal-bearing shale have decreasing porosity values. OM pores are directly related to micro- and mesopores, with high specific surface areas, while the porosity of inorganic pores increases with the increasing pore diameter. The porosity of all pores shows a positive relationship with permeability, which changes periodically with the increase in maturity. Coal-bearing shale pores are mainly plate- and ink bottle-shaped, with multimodal pore size distributions. Controlled by both diagenesis and hydrocarbon generation, the evolution of coal-bearing shale pores could be mainly divided into four stages. Furthermore, the pore evolution model of coal-bearing marine–continental transitional shale was preliminarily constructed. This study would enhance the understanding of reservoir evolution of the coal-bearing shale and provide useful information for the assessment and evaluation of reservoir capacity. Full article

The ripeness of tomatoes is crucial to determining their shelf life and quality. Most of the current methods for picking and sorting tomatoes take a long time, so this paper aims to design a device for sorting tomatoes based on force and bioelectrical impedance measurement. A force sensor installed on each of its four fingers may be used as an impedance measurement electrode. When picking tomatoes, the electrical impedance analysis circuit is first connected for pre-grasping. By applying a certain pre-tightening force, the FSR sensor on the end effector finger can be tightly attached to the tomato and establish an electric current pathway. Then, the electrical parameters of the tomato are measured to determine its maturity, and some of the electrical parameters are used for force monitoring compensation. Then, a force analysis is conducted to consider the resistance of the FSR under current stress. According to the principle of complex impedance circuit voltage division, the voltage signal on the tomato is determined. At the same time, the specific value of the grasping force at this time is determined based on the calibration of the pre-experiment and the compensation during the detection process, achieving real-time detection of the grasping force. The bioelectricity parameters of tomatoes can not only judge the ripeness of tomatoes, but also compensate for the force measurement stage to achieve more accurate non-destructive sorting. The experimental results showed that within 0.6 s of stable grasping, this system could complete tomato ripeness detection, improve the overall tomato sorting efficiency, and achieve 95% accuracy in identifying ripeness through impedance. Full article

Aiming at the problems of dense distribution, similar color and easy occlusion of famous and excellent tea tender leaves, an improved YOLOv7 (you only look once v7) model based on attention mechanism was proposed in this paper. The attention mechanism modules were added to the front and back positions of the enhanced feature extraction network (FPN), and the detection effects of YOLOv7+SE network, YOLOv7+ECA network, YOLOv7+CBAM network and YOLOv7+CA network were compared. It was found that the YOLOv7+CBAM Block model had the highest recognition accuracy with an accuracy of 93.71% and a recall rate of 89.23%. It was found that the model had the advantages of high accuracy and missing rate in small target detection, multi-target detection, occluded target detection and densely distributed target detection. Moreover, the model had good real-time performance and had a good application prospect in intelligent management and automatic harvesting of famous and excellent tea. Full article

Obtaining complete and consistent spectral images of wheat ears in the visible areas of in situ wheat canopies poses a significant challenge due to the varying growth posture of wheat. Nevertheless, detecting the presence and degree of wheat Fusarium head blight (FHB) in situ is critical for formulating measures that ensure stable grain production and supply while promoting green development in agriculture. In this study, a spectral quantitative analysis model was developed to evaluate the infection degree of FHB in an in situ wheat canopy’s visible areas. To achieve this, a spectral acquisition method was used to evaluate the infection degree of FHB in a wheat canopy’s visible areas. Hyperspectral images were utilized to obtain spectral data from healthy and mildly, moderately, and severely infected wheat ear canopies. The spectral data were preprocessed, and characteristic wavelengths were extracted using twelve types of spectral preprocessing methods and four types of characteristic wavelength extraction methods. Subsequently, sixty-five spectral quantitative prediction models for the infection degree of FHB in the in situ wheat canopy visible areas were established using the PLSR method, based on the original spectral data, preprocessed spectral data, original spectral characteristic wavelengths extracted data, and preprocessed spectral characteristic wavelengths extracted data. Comparative analysis of the models indicated that the MMS + CARS + PLSR model exhibited the best prediction effect and could serve as the spectral quantitative analysis model for the evaluation of the infection degree of FHB in an in situ wheat canopy’s visible areas. The model extracted thirty-five characteristic wavelengths, with a modeling set coefficient of determination (R²) of 0.9490 and a root-mean-square error (RMSE) of 0.2384. The testing set of the coefficient of determination (R²) was 0.9312, with a root-mean-square error (RMSE) of 0.2588. The model can facilitate the spectral quantitative analysis of the infection degree of FHB in the in situ wheat canopy visible areas, thereby aiding in the implementation of China’s targeted poverty alleviation and agricultural power strategy. Full article

As a typical binary transition metal oxide, ZnFe₂O₄ has attracted considerable attention for supercapacitor electrodes due to its high theoretical specific capacitance. However, the reported synthesis processes of ZnFe₂O₄ are complicated and ZnFe₂O₄ nanoparticles are easily agglomerated, leading to poor cycle life and unfavorable capacity. Herein, a facile microwave hydrothermal process was used to prepare ZnFe₂O₄/reduced graphene oxide (rGO) nanocomposites in this work. The influence of rGO content on the morphology, structure, and electrochemical performance of ZnFe₂O₄/rGO nanocomposites was systematically investigated. Due to the uniform distribution of ZnFe₂O₄ nanoparticles on the rGO surface and the high specific surface area and rich pore structures, the as-prepared ZnFe₂O₄/rGO electrode with 44.3 wt.% rGO content exhibits a high specific capacitance of 628 F g⁻¹ and long cycle life of 89% retention over 2500 cycles at 1 A g⁻¹. This work provides a new process for synthesizing binary transition metal oxide and developing a new strategy for realizing high-performance composites for supercapacitor electrodes. Full article

In order to obtain accurate discrete element simulation model (DEM) parameters of compound fertilizer and solve the problem of challenging measurement of contact parameters of compound fertilizer particle, simulation calibration test was carried out by using EDEM simulation soft-ware. This study measured the intrinsic parameters and contact parameters of compound fertilizer particles through physical tests and established a simulation model that corresponds with the actual situation to calibrate the contact parameters of compound fertilizer particles. By using the Blackett-Burman test, the parameters that had a significant impact on the compound fertilizer’s resting angle were determined by the fertilizer-fertilizer collision recovery coefficient, fertilizer-fertilizer rolling friction coefficient, and fertilizer-steel static friction coefficient. Utilizing the steepest ascent test, the ideal value intervals for the three key parameters were identified. Box-Burman response surface analysis was used to establish and optimize the regression model of the rest angle and significant parameters. With the actual rest angle as the target value, the best combination of significant parameters was found, which was used for the simulation verification test. The simulated rest angle was 20.61°, whereas the real rest angle was 19.95°, with a relative error of 3.31%. The results demonstrate that the calibration parameters are essentially accurate representations of the real characteristics, which can serve as a reference point for simulation research and optimization design of variable fertilizer spreader. Full article