Poultry is a light-sensitive animal and the light environment has an important influence on the growth anddevelopment of these animals. Previous studies have mainly focused on the effects of the light environment on variousphysiological indicators of poultry but seldom explored the light demand characteristics of broilers under free selection. Thisexperiment mainly studied the light demand characteristics of broilers under a yellow LED light environment and the influenceof different breeding densities [low-density (2.5 broilers/m2), high-density (7.5 broilers/m2)] on the production performance anddiet characteristics of broilers. Studies showed that the production performance indexes of low-density groups are higher thanthose of high-density groups. The feed and water consumption in the light area of the two experimental groups weresignificantly higher than those in the dark area, which means that the broilers showed a great preference for the light area.However, as the age of the broilers increased, the food and water consumption of the broilers decreased, indicating that broilershad a lower preference for light in the middle and late stages of growth. The statistical results for the residence frequencydistribution characteristics showed that broiler chickens had different light requirements at different growth stages under thecondition of active selection: 1) low-density breeding environment: 23.8L (light):0.2D (dark) for chicks and 22.3L:1.7D foradult broilers; 2) high-density breeding environment: 22.6L:1.4D for chicks and 15.0L:9.0D for adult broilers. This study willprovide a reference for the optimization and control of light environment in broiler breeding

Field environmental sensing can acquire real-time environmental information, which will be applied to field operation, through the fusion of multiple sensors. Multi-sensor fusion refers to the fusion of information obtained from multiple sensors using more advanced data processing methods. The main objective of applying this technology in field environment perception is to acquire real-time environmental information, making agricultural mechanical devices operate better in complex farmland environment with stronger sensing ability and operational accuracy. In this paper, the characteristics of sensors are studied to clarify the advantages and existing problems of each type of sensors and point out that multiple sensors can be introduced to compensate for the information loss. Secondly, the mainstream information fusion types at present are outlined. The characteristics, advantages and disadvantages of different fusion methods are analyzed. The important studies and applications related to multi-sensor information fusion technology published at home and abroad are listed. Eventually, the existing problems in the field environment sensing at present are summarized and the prospect for future of sensors precise sensing, multi-dimensional fusion strategies, discrepancies in sensor fusion and agricultural information processing are proposed in hope of providing reference for the deeper development of smart agriculture.

In recent years, worldwide research on fruit and vegetable quality detection technology includes machine vision, spectroscopy, acoustic vibration, tactile sensors, etc. These technologies have also been gradually applied to fruit and vegetable grading and sorting lines in recent years, greatly improving the income of farmers. There have been numerous reviews of these techniques. Most of the published research on fruit and vegetable quality detection technology is still carried out in the laboratory. The emphases have been on quality feature extraction, model establishment and experimental verification. The successful application in the fruit and vegetable sorting production line proves that these studies have high application potential and value, and we look forward to the performance of these sensing technologies in the fruit and vegetable picking field. Therefore, in this paper, based on the future highly automated fruit and vegetable picking mode, we will focus on three kinds of fruit and vegetable quality detection technologies including machine vision, tactile sensor and spectroscopy, to provide some reference for future research. Since there are currently limited cases of detecting quality during the fruit and vegetable picking, experiments performed on prototypes of manipulator, or devices such as Nanocilia sensors, portable spectrometers, etc., which are compact and convenient to mount on manipulator will be reviewed. Several tables and mosaics showing the performance of the three technologies in the detection of fruit and vegetable quality over the past five years have been listed. The performance of each sensing technology was relatively satisfactory in the laboratory in general. However, in the picking scenario, there are still many challenges to be solved. Different from industrial environments, agricultural scenarios are complex and changeable. Fragile and vulnerable agricultural products pose another challenge. The development of portable devices and nanomaterials have become important breakthroughs. Optical and tactile detection methods, as well as the integration of different quality detection methods, are expected to be the trends of research and development.

In order to realize the efficient and high-quality mechanical picking for Chinese wolfberry, firstly, the forcedreciprocating vibration picking principle of the Chinese wolfberry branch was studied, and the mechanical model of vibrationpicking was established based on the simplified cantilever model, and the response analysis and solution of all positions for thebranch were carried out. At the same time, the critical mechanical model of fruit detachment under the condition of fruithanging on branches was established, and the theoretical values of inertia force for each component of the branch wereobtained. Secondly, through actual measurement and finite element modeling, the natural frequency and forced vibrationresponse simulation for each component of the branch of Chinese wolfberry terminal branch model were both studied, and therelationship between single-point periodic excitation force and high-quality fruit shedding parameters was obtained. Thirdly,according to the conclusion of the picking model, a test bench with many groups of adjustable parameters was built. Finally, thelast branch of fruit-hanging Chinese wolf berry for Ningqi No.1 was taken as the experimental object, a four-level orthogonalexperiment was designed with three factors: frequency, amplitude and entrance angle. Meanwhile, the net picking rate, damagerate and false picking rate were taken as the evaluating indicators, referring to the comprehensive scores of the three factors. Itwas concluded that the primary and secondary relations of factors affecting the picking effect are frequency, amplitude andentrance angle, and the best operation parameters are frequency of 20 Hz, amplitude of 15 mm, and entrance angle of 45°, then,a hand-held vibration picker with setting parameters was trial-produced, and the optimal parameter combination was verified inthe Chinese wolfberry planting base of the National Chinese wolfberry Engineering and Technology Research Center. Theresults showed that the net picking rate of ripe Chinese wolfberry was 96.13%, the damage rate of fruit was 1.13%, and thefalse picking rate was 3.23%, mechanized picking efficiency was 30.28 kg/h, which is 6.65 times that of manual picking. Theexperimental results are consistent with the simulation results. The research results can provide an important basis for thecreation and operation standards of large-scale Chinese wolfberry vibration harvesting equipment.

Understanding the biomechanical properties of safflowers is essential for appropriately designing harvestingmachinery and optimizing the harvesting process. Safflower is a flexible crop that lacks a basis for relevant simulationparameters, which causes difficulties in designing harvesting machinery. In this study, a calibration method for safflowers wasproposed. First, a discrete element model was established by measuring the intrinsic parameters of a safflower, such as itsgeometric parameters, density, Poisson’s ratio, and modulus of elasticity. Second, the contact and bonding parameters werecalibrated using a combination of physical and simulation tests. In the contact parameter tests, the Hertz-Mindlin (no-slip)model was implemented for the stacking angle tests conducted regarding the safflower filament. A regular two-level factorialdesign was used to determine the important factors and perform the steepest climb test. Moreover, the Box-Behnken design wasadopted to obtain the optimal contact parameters. In the bonding parameter tests, the Hertz-Mindlin model with bonding contactwas applied for the safflower shear simulation tests; moreover, the optimum bonding parameters were obtained through thecentral composite design test. The results demonstrated that the relative errors between the simulated and measured stackingangles and maximum shear were 3.19% and 5.29%, respectively. As a result, the safflower simulation parameters wereaccurately calibrated, providing a reference for appropriately setting the simulation parameters and designing key mechanicalcomponents.

The anaerobic digestion (AD) disposal of stover and cattle manure is of great significance to the development of low-carbon economy and green energy in China, but it will also have an impact on the environment, and the degree of influence isdifferent for various raw materials. In this study, life cycle assessment (LCA) methods were applied to analyze and compare theimpact of corn stovers biogas projects (CSBP) and dairy manure biogas projects (DMBP) on the environment during the wholeoperation stage. The results of inventory analysis were evaluated by ReCiPe2016 Hierarchy(H) mid-point (problem-oriented)and end-point (destruction-oriented) method, respectively. The results showed that the net energy efficiency of CSBP washigher (763.903 kW·h/FU) and the greenhouse gas (GHG) emission reduction of DMBP was more (5541.418 kg CO2-eq/FU).The anaerobic digestion (AD) units have the greatest environmental impacts, and human carcinogenic toxicity is the largestenvironmental impact category (1.16-1.43 PE). The key to reducing environmental impact is reducing the input of chemicalsubstances and the waste of electric energy. Both CSBP and DMBP have a favorable impact on ecosystem quality andresources, and CSBP is more beneficial to the environment (–10.297 Pt). Co-digestion is an important measure to reduce theenvironmental damage from biogas projects. These research results provide theoretical support for the selection of rawmaterials for large-scale biogas projects in China, provide technical basis for reducing the impact of actual operation on theenvironment, and promote the resource utilization of agricultural waste and carbon dioxide emission reduction andsequestration.

Predicting crop yield timely can considerably accelerate agricultural production management and food policy-making, which are also important requirements for precise agricultural development. Given the development of hyperspectral imaging technology, a simple and efficient modeling method is convenient for predicting crop yield by using airborne hyperspectral images. In this study, the Unmanned Aerial Vehicle (UAV) hyperspectral and maturity yield data in 2014-2015 and 2017-2018 were collected. The winter wheat yield prediction model was established by optimizing Vegetation Indices (VIs) feature scales and sample scales, incorporating Partial Least Squares Regression (PLSR), Random Forest algorithm (RF), and Back Propagation Neural Network algorithm (BPN). Results showed that PLSR stands out as the optimal wheat yield prediction model considering stability and accuracy (RMSE=948.88 kg/hm2). Contrary to the belief that more input features result in higher accuracy, PLSR, RF, and BPN models performed best when trained with the top 3, 8, and 4 VIs with the highest correlation, respectively. With an increase in training samples, model accuracy improves, reaching stability when the training samples reach 70. Using PLSR and optimal feature scales, UAV yield prediction maps were generated, holding significant value for field management in precision agriculture.

To effectively obtain the downforce of the gauge wheels in real time, mechanical models of the interaction among the ground, gauge wheels, gauge wheel arms, and depth adjustment lever were constructed. A measuring method was proposed for monitoring the downforce through a two-dimensional radial sensing device, and a corresponding prototype was designed. Through simulation analysis of the sensing device with ANSYS, a 45° angle was determined to exist between the strain gauge axis and the sensing device axis, and the Wheatstone bridging circuit of R1+R3?R5?R7 (R stands for resistance strain gauge, different figures represent the strain gauge number) and R2+R4?R6?R8 was adopted. According to performance and calibration tests for the sensing device, the maximum interaction effect between the X and Y axes was 2.52%, and the output signal was stable and consistent. The standard error of the slope of the fitting equation of the downforce calculation model is 0.008. According to the field test, the average downforce of the gauge wheels was 1148, 1017, 843, and 713 N, at different sowing speeds of 6, 8, 10, and 12 km/h, respectively. The coefficients of variation were 0.40, 0.41, 0.62, and 0.71, respectively. The results indicate that the downforce fluctuation of the gauge wheels became more severe with increasing planting speed. Both the strain simulation analysis and field test verified that the measurement method is accurate and reliable, the performance of the sensing device is stable, the measurement method and sensing device meet the application requirements and lay a foundation for the research of accurate and stable control of downforce of no-till planter.

The breeding and selection of resistant varieties is an effective way to minimize wheat Fusarium head blight (FHB) hazards, so it is important to identify and evaluate resistant varieties. The traditional resistance phenotype identification is still largely dependent on time-consuming manual methods. In this paper, the method for evaluating FHB resistance in wheat ears was optimized based on the fusion feature wavelength images of the hyperspectral imaging system and the Faster R-CNN algorithm. The spectral data from 400-1000 nm were preprocessed by the multiple scattering correction (MSC) algorithm. Three feature wavelengths (553 nm, 682 nm and 714 nm) were selected by analyzing the X-loading weights (XLW) according to the absolute value of the peaks and troughs in different principal component (PC) load coefficient curves. Then, the different fusion methods of the three feature wavelengths were explored with different weight coefficients. Faster R-CNN was trained on the fusion and RGB datasets with VGG16, AlexNet, ZFNet, and ResNet-50 networks separately. Then, the other detection models SSD, YOLOv5, YOLOv7, CenterNet, and RetinaNet were used to compare with the Faster R-CNN model. As a result, the Faster R-CNN with VGG16 was best with the mAP (mean Average Precision) ranged from 97.7% to 98.8%. The model showed the best performance for the Fusion Image-1 dataset. Moreover, the Faster R-CNN model with VGG16 achieved an average detection accuracy of 99.00%, which was 23.89%, 1.21%, 0.75%, 0.62%, and 8.46% higher than SSD, YOLOv5, YOLOv7, CenterNet, and RetinaNet models. Therefore, it was demonstrated that the Faster R-CNN model based on the hyperspectral feature image fusion dataset proposed in this paper was feasible for rapid evaluation of wheat FHB resistance. This study provided an important detection method for ensuring wheat food security.

To understand the combined effect of organic and chemical fertilizers on soil carbon emissions and carbon balance of a farmland ecosystem, this study investigated the organic fertilizer nitrogen replacing different proportions of chemical fertilizer nitrogen. The results showed that, compared to F100, the O15F85 treatment increased the yield and net ecosystem productivity carbon sequestration of silage maize under mild, moderate, and severe salinization levels, as well as the contents of soil organic carbon, microbial carbon, and humin carbon, while reducing plant carbon emissions. The O15F85 treatment did not significantly increase soil carbon emissions (CEC), but O30F70, O45F55 and O100 treatments significantly increased CEC. The soil carbon balance analysis showed that the farmland ecosystem was a “sink” for atmospheric CO2 under each treatment. The O15F85 treatment produced an “excitation effect” to enhance the carbon sink effect of silage maize farmland under mild, moderate and severe salinization levels while maintaining stable production and emissions. Although the O100 treatment increased the carbon sink of farmland under different salinization levels, the yield was significantly reduced and did not represent practical production levels. Correlation analysis showed that soil organic carbon components and ecosystem carbon balance were closely related to soil total salt, pH and bulk density, while soil dissolved organic carbon, humus carbon components and carbon emissions were closely related to soil moisture and temperature. Therefore, the purpose of improving the carbon sink of saline-alkali land can be achieved through soil salt inhibition, soil structure remodeling and water supplement and warming regulation, which provides technical and theoretical support for reducing carbon emissions, achieving carbon neutrality and alleviating global warming.

Accurate irrigation and nitrogen application are essential for promoting the growth and yield of cherry tomatoes. In investigating the effects of irrigation and nitrogen on the growth, photosynthesis, and yield of cherry tomatoes, nine treatments including three levels of both irrigation and nitrogen were conducted over two growing seasons. Transverse stem diameter and horizontal stem diameter had the best performance at the irrigation level of 75% evaporation (Ep), although their responses to nitrogen were different for the two years. Plant height increased with the increase of irrigation and nitrogen. Plant dry matter (PDM) was significantly affected by irrigation and nitrogen interaction. The lowest PDM was found in the highest proportion of root dry matter, which occurred under low nitrogen level. The net photosynthetic rate (Pn) and transpiration rate enhanced with the increase of irrigation. Medium nitrogen showed promotion effect on all photosynthetic parameters in both growing seasons. Six of all fourteen indicators showed significant correlations with yield. Especially, single plant fruit number and PDM in 2018 Fall had significant positive direct effects on yield with the path coefficients of 0.648 and 1.159, while the significant direct path coefficients were 0.362 and 0.294 in Fruit dry matter and Pn for 2019 Spring, respectively. Based on the comprehensive evaluation of growth and yield by TOPSIS, the irrigation level of 75% Ep combined with medium nitrogen application produced higher yields by promoting the growth and photosynthesis of cherry tomatoes. It provides a strategy for water and nitrogen management of cherry tomatoes in Northwest China.

Large high clearance self-propelled sprayers were widely used in field plant protection due to their high-efficiencyoperation capabilities. Influenced by the characteristics of field operations such as high power, heavy weight, high groundclearance, and fast operation speed, the comprehensive requirements for the ride comfort, handling stability and roadfriendliness of the sprayer were increasingly strong. At the present stage, the chassis structure of the high clearance self-propelled sprayer that attaches great importance to the improvement of comprehensive performance still has the problems ofsevere bumps, weak handling performance and serious road damage in complex field environments. Therefore, this paperproposes an optimization design method for hydro-pneumatic suspension system of a high clearance self-propelled sprayerbased on the improved MOPSO (Multi-Objective Particle Swarm Optimization) algorithm, covering the entire process ofconfiguration design, parameter intelligent optimization, and system verification of the high clearance self-propelled sprayerchassis. Specifically, chassis structure of the hydro-pneumatic suspension suitable for the high clearance self-propelled sprayerwas designed, and a design method combining the improved MOPSO algorithm based on time-varying fusion strategy andadaptive update with the parameter optimization of hydro-pneumatic suspension based on this algorithm was proposed, andfinally the software simulation and bench performance verification were carried out. The results show that the optimized hydro-pneumatic suspension has excellent vibration reduction effect, and the body acceleration, suspension dynamic deflection andtire deflection were increased by 16.5%, 9.9% and 0.9% respectively, compared with those before optimization. Thecomprehensive performance of the hydro-pneumatic suspension designed in this study is better than that of the traditionalsuspension.

Defatted chickpea flour (DCF), which is a by-product of chickpea oil extraction industry, is rich in nutrients that arebeneficial to human health. In this study, the effects of temperature and DCF variation on the rheological properties, waterholding capacity, freeze-thaw stability and microstructure of DCF heat induced gels were investigated. The results showed thatthe viscoelasticity, frequency dependence, and resistance strength of heat induced gels increased significantly with the increaseof temperature and DCF variation. The degree of denaturation and water retention of heat induced gels increased significantlywith increased variables within the temperature and variation windows of 75°C to 95°C and 13% to 21%. The CLSM resultsrevealed that variations of both temperature and DCF variation could cause the proteins in the heat induced gels to aggregategradually and to form protein aggregations. When temperature or variation exceeded certain value (85°C or 17%), the proteinaggregations broke up and the protein clusters became smaller and more homogeneous. Therefore, the heat induced gelspresented better water holding capacity, viscoelasticity, structural stability and gel property at a temperature of 95°C or a DCFvariation of 21% within the present experimental range.

To realize the visual navigation of agricultural robots in the complex environment of orchards, this study proposed a method for fruit tree recognition and navigation based on YOLOv5. The YOLOv5s model was selected and trained to identify the trunks of the left and right rows of fruit trees; the quadratic curve was fitted to the bottom center of the fruit tree recognition box, and the identified fruit trees were divided into left and right columns by using the extreme value point of the quadratic curve to obtain the left and right rows of fruit trees; the straight-line equation of the left and right fruit tree rows was further solved, the median line of the two straight lines was taken as the expected navigation path of the robot, and the path tracing navigation experiment was carried out by using the improved LQR control algorithm. The experimental results show that under the guidance of the machine vision system and guided by the improved LQR control algorithm, the lateral error and heading error can converge quickly to the desired navigation path in the four initial states of [0 m, ?0.34 rad], [0.10 m, 0.34 rad], [0.15 m, 0 rad] and [0.20 m, ?0.34 rad]. When the initial speed was 0.5 m/s, the average lateral error was 0.059 m and the average heading error was 0.2787 rad for the navigation trials in the four different initial states. Its average driving was 5.3 m into the steady state, the average value of steady state lateral error was 0.0102 m, the average value of steady state heading error was 0.0253 rad, and the average relative error of the robot driving along the desired navigation path was 4.6%. The results indicate that the navigation algorithm proposed in this study has good robustness, meets the operational requirements of robot autonomous navigation in orchard environment, and improves the reliability of robot driving in orchard.

In response to the problems of numerous model parameters and low detection accuracy in SSD-based cotton leaf disease detection methods, a cotton leaf disease detection method based on improved SSD was proposed by combining the characteristics of cotton leaf diseases. First, the lightweight network MobileNetV2 was introduced to improve the backbone feature extraction network, which provides more abundant semantic information and details while significantly reducing the amount of model parameters and computing complexity, and accelerates the detection speed to achieve real-time detection. Then, the SE attention mechanism, ECA attention mechanism, and CBAM attention mechanism were fused to filter out disease target features and effectively suppress the feature information of jamming targets, generating feature maps with strong semantics and precise location information. The test results on the self-built cotton leaf disease dataset show that the parameter quantity of the SSD_MobileNetV2 model with backbone network of MobileNetV2 was 50.9% of the SSD_VGG model taking VGG as the backbone. Compared with SSD_VGG model, the P, R, F1 values, and mAP of the MobileNetV2 model increased by 4.37%, 3.3%, 3.8%, and 8.79% respectively, while FPS increased by 22.5 frames/s. The SE, ECA, and CBAM attention mechanisms were introduced into the SSD_VGG model and SSD_MobileNetV2 model. Using gradient weighted class activation mapping algorithm to explain the model detection process and visually compare the detection results of each model. The results indicate that the P, R, F1 values, mAP and FPS of the SSD_MobileNetV2+ECA model were higher than other models that introduced the attention mechanisms. Moreover, this model has less parameter with faster running speed, and is more suitable for detecting cotton diseases in complex environments, showing the best detection effect. Therefore, the improved SSD_MobileNetV2+ECA model significantly enhanced the semantic information of the shallow feature map of the model, and has a good detection effect on cotton leaf diseases in complex environments. The research can provide a lightweight, real-time, and accurate solution for detecting of cotton diseases in complex environments.

The shelterbelt is an indispensable barrier to the ecological and economic development of an oasis. Soil moisture, groundwater and irrigation greatly affect the shelterbelt water consumption and development. In this study, the transpiration rate of shelterbelt trees, soil moisture and meteorological data were collected to determine the effects of soil moisture and meteorological factors on the water consumption of different shelterbelt tree species via multivariate statistical methods. The results showed that the water consumption rate was positively correlated with solar radiation, air temperature and precipitation. Moreover, the leaf transpiration rate exhibited the trend of P. Russkii Jabl.?P. alba?P. simonii Carr.>P. nigracv, while the average daily water consumption decreased in the order of P. alba>P. Russkii Jabl.>P. simonii Carr.>P. nigracv. The average daily water consumption levels of P. alba, P. Russkii Jabl., P. simonii Carr. and P. nigracv were (9.15±0.92) kg/(tree?d), (6.95±1.41) kg/(tree?d), (4.43±1.32) kg/(tree?d), and (1.58±0.18) kg/(tree?d), respectively. Over the growing season, the soil water consumption levels of P. alba, P. Russkii Jabl., P. simonii Carr., and P. nigracv in each shelterbelt tree stand reached 674.8, 336.9, 358.1 and 161.7 kg, respectively. More than 96% of the soil moisture lost was provided by the upper 120-cm soil layer. Understanding the influence and contribution of soil water and meteorological factors to shelterbelt water consumption is beneficial for shelterbelt management and protection.

To improve the innovation of agricultural machinery product styling, this paper proposes a shape structure behaviorfunction (SSBF) model suitable for the industrial design field. The feature line evolution method combining shape grammar andgenetic algorithm was used for modelling the of the grader, which not only maintains the product style characteristics but alsoreflects the typical identification characteristics of the bionic prototype and produces a new product modelling scheme. Byconducting cognitive and recognition experiments on product styling features, the ranking of product styling features and thecontribution of each component to product styling were determined. The method of combining shape grammar and quadraticBézier curve was used to express and encode feature lines, and genetic algorithm was used to evolve biomimetic forms to formproduct feature lines with typical biological morphological features; The extracted form bionic elements were integrated intothe grader modelling design, and the interaction evaluation was carried out through the genetic algorithm evolution scheme.The basic form elements were extracted and analyzed, and the deduction rules were formulated and reorganized. The derivedfeature line geometric data considered the product’s image features and the bio-inspired prototype, which can be used for thefollow-up guidance of industrial design schemes.

Like every other societal domain, science faces yet another reckoning caused by a bot called ChatGPT (Chat Generative Pre-Trained Transformer). ChatGPT was introduced in November 2022 to produce messages that seem like they were written by humans and are conversational. With the release of the latest version of ChatGPT called GPT-4, and other similar models such as Google Bard, Chatsonic, Collosal Chat, these chatbots combine several (about 175 billion) neural networks pre-trained on large Language Models (LLMs), allowing them to respond to user promptings just like humans. GPT-4 for example can admit its mistakes and confront false assumptions thanks to the dialogue style, which also enables it to write essays and to keep track of the context of a discussion while it is happening. However, users may be deceived by the human-like text structure of the AI models to believe that it came from a human origin[1]. These chatbot models could be better, even though they generate text with a high level of accuracy. Occasionally, they produce inappropriate or wrong responses, resulting in faulty inferences or ethical issues. This article will discuss some fundamental strengths and weaknesses of this Artificial intelligence (AI) system concerning scientific research.

Increasing the planting density can exacerbate crop competition for water, nutrients and space which results in a decline in the crop yields. However, the effect of increasing planting density on crop growth and soil biological characteristics in barren sandy land in the semi-arid regions are still unclear. In this study, we investigated the effects of six planting densities (5.4×104, 6.45×104, 7.95×104, 9.5×104, 9.75×104 and 10.5×104 plants/hm2) on maize growth, photosynthesis characteristics, yield and soil biological characteristics in barren sandy soil in the semi-arid region of Ningxia, China. The results indicated that the stem diameter and spike length decreased linearly with increasing planting density. The plant height, spike weight, grain weight and 100-grain weight decreased with increasing plating density. Moreover, the root length increased with increasing planting density. The diameter, volume and activity increased and then decreased with increasing planting density. There was no significant difference (p>0.05) in the effect of planting density on transpiration rate intercellular CO2 concentration. As well, the soil microbial biomass carbon and microbial biomass nitrogen decreased with increasing planting density. The soil catalase activities increased and then decreased with increasing planting density. The alkaline phosphatase activity, the amounts of soil bacteria and actinomycetes increased with increasing planting density. Generally, a moderately increasing planting density can improve maize yield when water and nutrients are sufficient. The optimal planting density was 8.29×104 plants/hm2 and the highest yield was 15.84 t/hm2 in barren sandy soil in semi-arid region of Ningxia, China. This study provides a theoretical basis for high yield and high efficiency of maize.

Seeding is an important part of improving corn yield. Currently, seed guide tubes are mostly used as transportdevices. But the existing seed guide tubes cannot meet the requirements or achieve the goal of fixing the seed falling trajectory.A seed collision phenomenon occurs occasionally. So, in response to the problems that the seeds and seed guide tube collide orbounce under high speed operation, which results in a lower sowing qualification rate and poor spacing uniformity, a seedreceiving and conveying system comprising a belt-type high-speed corn seed guiding device was designed and optimized, tomeet the needs of high-speed precision sowing operations and improve the spacing uniformity.The factors affecting the seedconveying performance were obtained by analyzing the mechanical properties of the seeds at various movement stages. Thesefactors were the number of seed cavities between adjacent seeds, the forward speed, the height from the ground, and theinstallation angle. Single factor simulation experiments were conducted by selecting the paddle spacing as the test factor andusing the pass rate, reseeding rate, omission rate and coefficient of variation as the evaluation indexes to investigate theinfluence of the paddle spacing on the seed guide performance of the device and further determine the structural parameters ofthe paddle belt. Orthogonal rotation combination tests of three factors and five levels were also conducted through benchtesting.Then the test outcomes were optimized. The results indicated that the best results were obtained when the number ofseed cavity intervals between adjacent seeds was 5.16, the installation angle was 79.40°, and the height from the ground was31.84 mm. At this time, the qualified rate was 98.49%, the repeated sowing rate was 0.48%, the missed sowing rate was 1.03%,and the coefficient of variation was 6.80%. Experiments were used to validate the optimization results, and all of the obtainedindex data satisfied the criteria for accurate and quick corn sowing. The study’s findings can serve as a theoretical foundationfor a belt-type high-speed corn seed guiding device optimization test.

To improve the GNSS receiver’s accuracy, continuity, and stability in measuring the height of agricultural implements, this study proposed a variable-parameter Kalman filter (VPKF) algorithm based on GNSS and accelerometer to estimate the height of the implements optimally. The VPKF was verified, and its accuracy was evaluated by parallel rail platform and field tests. From the parallel rail test results, when the GNSS receiver was in real-time kinematic (RTK) positioning and the time delay of differential correction data (TDDCD) was less than or equal to 4 s, the root mean square error (RMSE) of the VPKF estimation was 9.82 mm. The RMSE of the GNSS measurement was 18.85 mm. When the GNSS receiver lost differential correction data within 28 s, the absolute error of VPKF was less than 30 mm, and the RMSE was 16.93 mm. The field test results showed that when the GNSS receiver was in RTK positioning and the TDDCD was less than or equal to 4 s, the RMSE of VPKF estimation was 13.43 mm, and the GNSS measurement was 14.56 mm. When the GNSS receiver lost differential correction data within 28 s, the RMSE of the VPKF estimate was 15.22 mm. These results show that VPKF can optimally estimate implement height with better accuracy. Overall, the VPKF can obtain a more accurate, continuous, and stable height of the implement, and increase the application scenarios of the GNSS receiver to measure the implement height.

Irrigation and fertilizer interaction is an efficient cultivation management strategy for facility agriculture. However, the effects of irrigation and fertilizer management on tomato growth and its physiological factors remain unclarified. In this study, two irrigation patterns (W1, conventional irrigation; W2, water-saving irrigation) and four fertilizer application patterns (CF, chemical fertilizer; BOF, biological organic fertilizer; NPK, nutrient compound fertilizer; BOF+NPK) were selected to observe the effects of their interaction on cherry tomato plant growth, leaf photosynthesis and fruit quality through pot experiments. The results showed that W2 treatments promoted plant height growth compared to W1 under the same fertilizer addition. Moreover, irrigation and fertilizer management had significant effects on net photosynthetic rate, intercellular oxidation concentration, stomatal conductance and transpiration rate at the first sequence flowering and fruiting stages. The maximum tomato plant height (99.0 cm) was achieved under the irrigation and fertilizer pattern of BOF and W2, along with the highest fruit yield of 1.98 kg/plant, which was approximately 31.1% higher than the minimum yield under the combined CF and W2 treatment. Under W2 treatments, the application of either NPK or BOF increased the soluble sugar content of tomatoes. The structural equation models showed that the soil alkali hydrolyzed nitrogen could directly significantly affect the yield and soluble sugar. The findings suggest that optimization of irrigation-fertilizer interactions positively regulates tomato growth, providing an efficient model for tomato irrigation and fertilizer management and a reference for sustainable development of facility agriculture.

The decision by Japan to begin discharging the Fukushima wastewater into the ocean on August 24, 2023 was followed by protests from several countries, including China, Russia, Korea, Vietnam, and deep concerns from the international community. This decision is related to the aftermath of the Fukushima Daiichi nuclear disaster that occurred in 2011, which destroyed the cooling system of the nuclear power plant and caused the reactor cores to overheat. Much water was used to cool down the reactors fuel rods; about 1.3 million cubic meters contaminated water with highly radioactive material was generated, which can fill more than 500 Olympic swimming pools[1]. In order to reduce the levels of radioactivity, an Advanced Liquid Processing System (ALPS) was used to remove most radioactive contaminants from water. ALPS works by circulating water through a system of tanks and filters, which removes specific contaminants such as cesium and strontium, using a multi-step process that includes coagulation, flocculation, ion exchange, and absorption[1]. Japan's government and some scientists have argued that the ALPS-treated water is safe for release into the ocean. According to their claims, the discharged water poses minimal risk to human health and the environment. However, concerns about the long-term effects of this discharge remain in scientists ‘minds.

Some agriculture machinery like the transplanter, needs to operate by following the crop-free ridges. In order to improve working efficiency and quality, some autonomous navigation systems were developed and applied to ridge-following machinery. At present, agricultural navigation systems are mainly the satellite navigation system and the machine vision system. The satellite navigation system is difficult to apply to the machinery that needs to work by following the ridge because it cannot distinguish the shape of the navigated ridge and guide the machinery working along the ridge. In this study, 697 cloudy ridge images and 235 sunny ridge images were taken in the field, and these images were used as the dataset. Moreover, a machine vision navigation method based on the color of ridges was proposed. Firstly, the regions of interest (ROI) in the ridge image were extracted according to the reaction time and the forward speed of the machine. Then, a gray reconstruction method was used to enlarge the color difference between the ridge and the furrow. The optimal threshold for the gray image segmenting was calculated real-timely by using the threshold segmentation method. Then, based on the contour detection method, the ridge contour which was not surrounded by holes was extracted. Finally, the approximate quadrilateral method was proposed to recognize the ridge center line as the navigation line. The method proposed in this study was verified by four types of ridges with different colors and textures. The experimental results showed that the recognition success rates of the light ridge, the dark ridge, the film-covered ridge, and the sunny ridge were 100%, 97.5%, 100%, and 98.7%, respectively. The recognition success rate of the proposed method was at least 8% higher than that of the existing ridge-furrow recognition methods. The results indicate that this method can effectively realize navigation line recognition. This method can provide technical support for the autonomous navigation of agricultural machinery, such as transplanters, seeders, etc., operating on the ridge without crops.

In order to address the challenge of non-destructive detection of tomato fruit ripeness in controlled environments,this study proposed a real-time instance segmentation method based on the edge device. This method combined the principlesof phenotype robots and machine vision based on deep learning. A compact and remotely controllable phenotype detectionrobot was employed to acquire precise data on tomato ripeness. The video data were then processed by using an efficientbackbone and the FeatFlowNet structure for feature extraction and analysis of key-frame to non-key-frame mapping from videodata. To enhance the diversity of training datasets and the generalization of the model, an innovative approach was chosen byusing random enhancement techniques. Besides, the PolyLoss optimization technique was applied to further improve theaccuracy of the ripeness multi-class detection tasks. Through validation, the method of this study achieved real-time processingspeeds of 90.1 fps (RTX 3070Ti) and 65.5 fps (RTX 2060 S), with an average detection accuracy of 97% compared tomanually measured results. This is more accurate and efficient than other instance segmentation models according to actualtesting in a greenhouse. Therefore, the results of this research can be deployed in edge devices and provide technical support forunmanned greenhouse monitoring devices or fruit-picking robots in facility environments.

In order to improve the screening performance and cleaning effect of the jujube harvesting machinery cleaningdevice, a vibrating curved screen device was designed in this study. By analyzing the structure mechanism of the curved sievebody, it was obtained that the arc-shaped mesh hole spacing S was 15-25 mm and the curved mesh hole curvature U was 90°-150°. By exploring the movement state and stress of jujube and impurities on the curved sieve body, it was determined that thehorizontal spacing L of the curved layer sieve was 30 mm and the vertical spacing H was 45-65 mm. Taking the verticalspacing H of the curved layer sieve, the curvature U of the curved mesh hole, and the spacing S of the curved mesh hole as theexperimental factors, considering the screening efficiency α and the impurity content β of the jujube as the response values, thethree-factor three-level quadratic regression orthogonal experiment was designed, establishing the regression mathematicalmodel of each factor and response value, and the multiple target optimization algorithm of Design-expert software was used tooptimize various factors. The results showed that the influence factors on the screening efficiency were in the descending orderas: the arc screen spacing, the vertical spacing of the curved layer screen, and the curved screen hole curvature; The significantfactors affecting the impurity content of jujube were in the descending order as: the arc screen spacing, the curved screen holecurvature, and the vertical spacing of the curved layer screen. The experimental results were verified by the optimizedcombination of parameters: when the vertical spacing H of the curved layer screen was 65 mm, the curved screen holecurvature U was 110°, and the arc screen spacing S was 23 mm, the average screening efficiency α in the test was 91.09%. Therelative error between the experimental verification value and the theoretical optimization value was 1.36%, which was lessthan 5%. The impurity content of jujube β in the test was 1.02%. The relative error between the experimental verification valueand the theoretical optimization value was 2.00%, which was also less than 5%. The test results can provide a reference for theresearch and optimization of the subsequent air-suction-type jujube harvester cleaning device.

During the harvesting process, rigid materials are prone to causing damage to the cotton stalks, which will increasethe risk of stalk breakage. A cotton stalk pulling component that blends stiff and flexible materials was devised to lower thebreaking rate. The cotton stalk pulling component was made up of rollers and flexible belts that pull the stalks using clampingforce and the forward speed of the tractor. The influence of various factors in the equipment on the harvesting effect of cottonstalks were analyzed through response surface experiments, and a multiple quadratic regression response surface model withmissing pulling rate and breakage rate as response values was established. The significant of influencing factors on the breakingrate of cotton stalks are in a descending order as: the angle of cotton stalk pulling, tractor’s forward speed, and the clampingspeed of the cotton stalk component. The working parameters of the wheel-belt type cotton stalk pulling machine have beenoptimized using the response surface combination experimental method, and the optimal parameter combination was obtainedas: tractor forward speed of 4.5 km/h, cotton stalk pulling angle of 60°, and clamping speed of the cotton stalk pullingcomponent of 349 r/min. The results of validation experiments showed that the missing pulling rate of cotton stalks was 5.06%and the breakage rate was 13.12%, indicating a good harvesting effect of the cotton stalks. The model was reasonable and theperformance parameters could meet the relevant inspection requirements. The results can provide a reference for furtherresearch on the technology of flexible cotton stalk pulling.

The energy saving of hydraulic excavators is always an essential research. An energy recovery system caneffectively recover the boom potential energy and rotational kinetic energy. Based on the standard working cycle of hydraulicexcavators, a dynamic programming (DP) control strategy for hybrid hydraulic excavators was proposed to recover the boompotential energy and rotational kinetic energy. The hybrid hydraulic excavator simulation model was built by Simulinksoftware. The simulation results indicated that the fuel consumption of hybrid hydraulic excavators using the DP controlstrategy was about 21.3% lower than that of the conventional hydraulic excavator. In order to experimentally verify thesimulation results, an experimental platform for hybrid hydraulic excavators was built. The experimental results indicated thatthe fuel consumption of hybrid hydraulic excavators using the DP control strategy was about 18.9% lower than that of theconventional hydraulic excavator. This paper shows that the DP control strategy applied to hybrid hydraulic excavators canrecycle the boom potential energy and rotational kinetic energy, and reduce the fuel consumption of hybrid hydraulicexcavators.

Seed clearing is a critical stage during precision seed metering process to ensure high seed singulation. However,there is a lack of understanding of the dynamics in the seed clearing process. In this study, a model was developed to predictinitial seed clearing angle, in the seed clearing process using vector fields. The model was applied to an existing high-speedmetering device and soybean seeds, and the model was evaluated with bench testing results. Results showed that dynamicchanges in forces and constraints of seeds during the seed clearing process could be abstracted as vectors, and the changes ofvector directions could be described by their phase angles. The phase angles were functions of the rotational angle of the seedmeter. The phase angle of the constraint boundary linearly increases with the increase of the rotational angle. The phase angleof the force fluctuates, as the rotational angle changes. Initial seed clearing angle obtained from the phase angles varies from 8°to 59°, depending on the seeder travel speed. When comparing the values of the initial seed clearing angles predicted by themodel with those from the bench tests, the root mean square error (RMSE) were from 2.73 to 3.14, and the correlation (r)between predict and observer were all higher than 0.98, indicating that the model had reasonably good accuracy.

This study investigated the influence of different linearly polarized spectrum lights on locusts polartactic responsecharacteristics linearly polarized vector sensitivity mode and polartactic response) by using linearly polarized spectrum vectorlight module and experimental device. The objective was to clarify the vector sensitivity characteristics and functional effect oflinearly polarized light spectrum intensity on locusts polartactic response, determine the influence specificity of linearlypolarized spectrum illumination properties on locusts polarization-related behavior. When spectrum and illumination wereconstant, locusts polartactic response, presenting the response feature of sine and cosine function change specificity, was relatedto spectrum attribute. The visual acuity effect stimulated by violet spectrum was the best, whereas the optical distancemodulation effect induced by orange spectrum was the strongest. When illumination was enhanced, locusts vector sensitivitymode shifted to present the specific sensitivity prompted by light intensity at long distance and inhibited by light intensity atshort distance. Moreover, the regulating function of violet spectrum was the strongest, and the regulatory mutation effect oforange spectrum was the least significant. Simultaneously, locusts polartactic sensitivity to 300° vector at 100 lx, whereas to240° vector at 1000 lx of linearly polarized violet light was the strongest. Locusts polartactic aggregation and visual tendencysensitivity to 90° vector at 100 lx, whereas to 270° vector at 1000 lx of linearly polarized violet light was the strongest. Theheterogeneous regulation function of different linearly polarized spectrum couplings with light intensity led to significantvariations in locusts vector sensitivity mode. This was derived from the antagonistic and specific tuning characteristics oflocusts polartactic vision, reflecting the integrated output effect of locusts vector dependence regulated by linearly polarizedspectrum intensity attribute. The findings were significant for the construction of pest polarization induction light sources andthe investigation of the sensitive physiology pathway of locusts polarization vision.

Multi-machine collaboration of agricultural machinery is one of the international frontier and hot research in the field of agricultural equipment. However, the current domestic multi-machine collaborative operation of agricultural machinery is limited to the research of task goal planning and collaborative path optimization in a single production link. In order to achieve the purpose of zero inventory of agricultural materials and precise and efficient production operations, a new technology of agricultural machinery multi-machine collaboration with multi-dimension and full chain was proposed, which takes into account the whole process of agricultural production, as well as agricultural machinery system and external supply chain, storage and transportation chain collaboration. The problems of data collaboration, process collaboration and organization collaboration were analyzed. And the realization conditions of new multi-machine cooperative technology were analyzed. Meanwhile, the zero inventory mode and precise operation mode of agricultural materials under the background of multi-machine cooperation of intelligent agricultural machinery were studied. Then, a precise and efficient agricultural production mode based on data-process-organization collaboration was constructed. The results showed that the multi-machine cooperative technology mode of multi-dimensional and full-chain agricultural machinery could greatly improve the efficiency of agricultural machinery, operation quality, land utilization rate and reduce production cost.