ABSTRACT The plastic film materials are a widely used agricultural means of production. Recently, two types of new agricultural film mulches are developed and are used popularly. They are ethylene-vinyl acetate copolymer (EVA) and polyolefin (PO) film mulches. The transmittances of film mulches are related to thermal reservation and plant photosynthesis. We experimentally measure transmittances of several EVA and PO film mulches under different wavelengths. We also find that the transmittances of mulches vary with light intensity change at one wavelength. The variable transmittances for these mulches are also measured in experiments. These data measured can provides help for agricultural production and accurate numerical simulation for the establishment microclimate of plastic greenhouse, even the degradation of plastic films. Recently popularly ethylenevinyl ethylene vinyl (EVA (PO photosynthesis wavelengths wavelength experiments greenhouse films

ABSTRACT The seed deterioration process during storage is inevitable, but it can be slowed down using packaging that reduces the influence of the external environment on stored corn seeds. Therefore, this study aimed to evaluate the physiological quality of corn seeds stored in different packages and subjected to storage conditions in the central region of Brazil. Thirty-seven corn genotypes were analyzed in three storage environments: multiwall paper, 10 °C temperature, and 40% relative air humidity; multiwall paper under uncontrolled conditions; and hermetic packaging under uncontrolled conditions. The results of germination, water content, electrical conductivity, accelerated aging, soilless cold test, and field emergence test were used. Physiological quality assessments indicated that storage under conditions of 10 °C temperature and 40% relative air humidity is more efficient in preserving corn seeds' physiological quality than packaging in an uncontrolled environment. inevitable Therefore Brazil Thirtyseven Thirty seven environments 1 C 40 germination content conductivity aging used 4

ABSTRACT This study aims to investigate the influence of moisture contents and grouser heights on traction force exerted on different surfaces in the track-soil interaction system. A sandy loam was employed for acquiring the soil parameters, such as kc, kφ, n, C, Ca, δ, φ, and γ. These parameters were obtained through the mean of the bevameter technique. Moisture contents of the sandy loam were changed from low to high levels, which could be listed as 1.4%, 8.0%, 14.7%, and 22.2% at last. The direct shear and penetration tests were performed for each soil condition of the moisture content, respectively. The model of the single-grouser-shoe (track model) had a length of 9 cm, a width of 15 cm, and varied grouser heights from 0 to 15 cm with a 0.5 cm interval. Based on the calculation result, in general, the shearing force produced by the bottom surface always performs better than either the shear force on the grouser-tip surface or two lateral surfaces. Simultaneously, prediction results also show that the shearing forces were significantly influenced by the level of moisture content in the soil. tracksoil track system kc kφ n C Ca δ φ γ technique levels 14 1 4 1.4% 80 8 8.0% 147 7 14.7% 222 22 2 22.2 last respectively singlegrousershoe single shoe 05 5 0. interval result general grousertip tip Simultaneously 1.4 8.0 14.7 22. 1. 8. 14.

ABSTRACT Accurate crop classification, crucial for a macro-level understanding of food production, formulating relevant agricultural policies, and predicting comprehensive agricultural productivity, enables precise crop distribution. In remote sensing image classification, feature selection and representation play a pivotal role in accuracy. An augmented U-Net algorithm, named ASPP-SAM-UNet, integrating spatial attention mechanisms and multi-scale features is proposed for the enhancement of typical crop classification accuracy in remote sensing. The ASPP-SAM-UNet design integrates features over multiple scales, boosts the representational capacity of shallow features, and expands the neural network’s receptive field by incorporating Atrous Spatial Pyramid Pooling (ASPP) into the convolutional components of the standard U-Net encoder via residual connections. The integration of the residual module allows for a profound fusion of deep and shallow features, thereby enhancing their utility. The spatial attention mechanism amalgamates spatial and semantic information, empowering the decoder to reclaim more spatial information. This study focused on Bayan County, Harbin City, Heilongjiang Province, China, employing GF-6 WFV remote sensing images for crop classification. Empirical outcomes showed a significant improvement in classification accuracy with the advanced algorithm, boosting the overall accuracy (OA) from 89.49 to 92.80%. Specifically, the segmentation accuracy for maize, rice, and soybean increased from 89.90, 89.96, and 87.37% to 93.47, 94.82, and 89.35%, respectively. The suggested algorithm offers a pioneering performance standard for crop classification leveraging GF-6 WFV remote sensing imagery. macrolevel macro level production policies productivity distribution UNet U Net ASPPSAMUNet, ASPPSAMUNet ASPP SAM UNet, multiscale multi scale scales networks network s (ASPP connections utility information County City Province China GF6 GF 6 GF- OA (OA 8949 89 49 89.4 9280 92 80 92.80% Specifically maize rice 8990 90 89.90 8996 96 89.96 8737 87 37 87.37 9347 93 47 93.47 9482 94 82 94.82 8935 35 89.35% respectively imagery 894 8 4 89. 928 9 92.80 899 89.9 873 3 87.3 934 93.4 948 94.8 893 89.35 92.8 87. 93. 94. 89.3 92.

ABSTRACT Current corn sowing equipment shows relatively low levels of automation and intelligent response. Issues such as wheel slipping and chain jumping occur in seeders driven by wheels and chains, leading to a decrease in the seeding qualification rate. To solve the problems of wheel slipping and low control strategy accuracy, an electric control sowing system has been designed. This system uses radar to collect the locomotive speed and automatically adjusts the motor speed. To improve the control accuracy of the electric control system, a dual closed-loop control method is employed, and the proportional-integral-derivative (PID) controller parameters are optimized by combining the particle swarm optimization (PSO) algorithm and the fuzzy algorithm. Control accuracy tests and field tests are conducted for both the traditional PID control system and the fuzzy PID control system based on the PSO algorithm. The average error of this system is 0.622%, which is 1.6% lower than that for the traditional method. Field tests show that in operation, the sowing system achieves the average seeding qualification index of 93.99%, which is 3.19% higher than that for the traditional PID control method. The fuzzy PID control system shows improved sowing effectiveness compared to the traditional PID control method. response chains rate designed closedloop closed loop employed proportionalintegralderivative proportional integral derivative (PID (PSO 0622 0 622 0.622% 16 1 6 1.6 operation 9399 93 99 93.99% 319 3 19 3.19 062 62 0.622 1. 939 9 93.99 31 3.1 06 0.62 93.9 3. 0.6 93. 0.

ABSTRACT Safflower stands out among oilseeds due to its oil quality and crop resilience in dry and cold regions. However, there have been limited studies on its water requirements. This study aimed to assess water stress indices and determine phenological stages with the highest water demands during safflower cultivation. The experiment was conducted in two phases: one in a greenhouse and another in the field. Treatments involved different irrigation timings during the stages of crop development. The variables analyzed included plant height, stem diameter, fresh and dry aboveground biomass, number of capitula, and number and weight of seeds. The results showed that safflower cultivation benefited from irrigation during its vegetative stage, in conjunction with irrigation during the reproductive and/or grain formation stage. It exhibited susceptibility to water deficit when irrigated only during the vegetative stage. Safflower can be cultivated in various climatic regions of Brazil and become an economically important species due to its adaptability, production, and potential. However requirements phases field development height diameter biomass capitula seeds stage andor or adaptability production potential

ABSTRACT Maize yields are important for human food security, and the issue of how to quickly and accurately segment areas of maize disease is an important one in the field of smart agriculture. To address the problem of irregular and multi-area clustering of regions of maize leaf lesions, which can lead to inaccurate segmentation, this paper proposes an improved Swin-Unet model called squeeze-and-excitation Swin-Unet (SE-Swin Unet). Our model applies Swin Transformer modules and skip connection structures for global and local learning. At each skip connection, a SENet module is incorporated to focus on global target features through channel-wise attention, with the aims of highlighting significant regions of disease on maize leaves and suppressing irrelevant background areas. The improved loss function in SE-Swin Unet is based on a combination of the binary cross entropy and Dice loss functions, which form the semantic segmentation model. Compared to other traditional convolutional neural networks on the same dataset, SE-Swin Unet achieves higher mean results for the intersection over union, accuracy, and F1-score, with values of 84.61%, 92.98%, and 89.91%, respectively. The SE-Swin Unet model proposed in this paper is therefore better able to extract information on maize leaf disease, and can provide a reference for the realisation of the complex task of corn leaf disease segmentation. security agriculture multiarea multi area lesions SwinUnet squeezeandexcitation squeeze excitation SESwin SE Unet. . Unet) learning channelwise channel wise attention functions dataset union accuracy F1score, F1score Fscore F1 score, score F F1-score 8461 84 61 84.61% 9298 92 98 92.98% 8991 89 91 89.91% respectively 846 8 6 84.61 929 9 92.98 899 89.91 84.6 92.9 89.9 84. 92. 89.

ABSTRACT During high-speed seeding operations, collisions between seeds and seed guide tubes are very significant, resulting in poor seed spacing uniformity. To solve this problem, a high-speed seed guide device with conical spiral airflow was designed, which can use high-speed positive pressure airflow to move seeds smoothly. To improve the uniformity of the airflow field in the device, a single-factor test was carried out to determine the reasonable range of each factor. Then, the four-factor and five-level central composite test was carried out. The four main factors were screw pitch, cone angle, width of gap, and angle of air-tube, and the evaluation index was the mean variance of airflow velocity. The test results showed that the optimal parameters of the high-speed seed guide device with conical spiral airflow were a screw pitch of 24.98 mm, cone angle of 7.47/55, width of gap of 5.31 mm, and angle of air-tube of 25.09º. The verification test showed that the relative error between the verification test value and the predicted value was less than 5%, indicating that the model had high reliability. This research can provide references for the research of high-speed seed guide technology and the innovative design of seed guide devices. highspeed speed operations significant problem designed smoothly singlefactor single factor Then fourfactor fivelevel five level airtube, airtube air tube, tube velocity 2498 24 98 24.9 mm 74755 7 47 55 7.47/55 531 5 31 5.3 2509º º 25 09º 25.09º 5% reliability devices 249 2 9 24. 7475 4 7.47/5 53 3 5. 747 7.47/ 74 7.47 7.4 7.

ABSTRACT Vegetation indices are a quick and practical alternative for monitoring crops due to the availability of satellite images on various platforms for free, allowing a quick analysis of the vegetative state of the crop and interventions in the field in case of signs of diseases and pests. In this context, this study aimed to evaluate the vegetative state of the coffee crop using vegetation indices (NDVI, SAVI, ARVI, EVI, and VDVI) in an agricultural year. The study was carried out on a commercial farm using satellite images from the Planet platform, during an agricultural coffee growing season (2021/2022). The indices selected for the study were the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Atmospherically Resistant Vegetation Index (ARVI), Enhanced Vegetation Index (EVI), and Visible Difference Vegetation Index (VDVI). The index data were analyzed using descriptive statistics, Pearson correlation, classification/interpretation proposal, and the Kappa index. NDVI and SAVI are efficient in monitoring coffee cultivation in an agricultural year, as the Kappa index was higher than 90%. ARVI and EVI had Kappa index values close to 90% and can be used to monitor the crop. VDVI was inefficient, with a low Kappa index value when compared to the others. The proposed classification for vegetation indices based on NDVI classes and values consisted of an important tool for classifying and interpreting the values of these indices, assisting monitoring and management of coffee cultivation. free pests context NDVI, (NDVI year platform 2021/2022. 20212022 2021/2022 . 2021 2022 (2021/2022) , (NDVI) SoilAdjusted Soil Adjusted (SAVI) (ARVI) (EVI) VDVI. (VDVI) statistics correlation classificationinterpretation interpretation proposal 90 inefficient others 2021202 2021/202 202 (2021/2022 (SAVI (ARVI (EVI (VDVI 9 202120 2021/20 20 (2021/202 20212 2021/2 2 (2021/20 2021/ (2021/2 (2021/ (2021 (202 (20 (2 (

ABSTRACT To address the poor fitness and low accuracy of multiobjective parameter optimization, the BP neural network-based constrained multiobjective optimization method was applied to optimize a seed-metering device. Taking the 2BQ-15 type Panax notoginseng seed-metering device as the research object, the picking hole column diameter, forward velocity, and dropping seed point-to-picking hole roll distance were selected as the experimental factors, and the quality index, missing index and multiple index were selected as the performance indicators. The experimental scheme was designed by the quadratic orthogonal rotation combination, and the BP neural network of the precision seed-metering device was built from the experimental data. The seed-metering device was optimized by the proposed method, and the optimal parameter combinations were obtained as follows: the picking hole column diameter was 27 mm, the forward velocity was 0.50 m/s, and the dropping seed point-to-picking hole roll distance was 330 mm. Under such parameter combinations, the quality index is 93.4%, the missing index is 3.15%, and the multiple index is 3.35%. Finally, a verification test was carried out on the basis of the optimization results, the errors were within the allowable range, and the test results and optimized results were consistent. networkbased based seedmetering metering 2BQ15 BQ 2BQ 15 2BQ-1 object pointtopicking point factors indicators combination data follows 2 mm 050 0 50 0.5 ms m s m/s 33 934 93 4 93.4% 315 3 3.15% 335 35 3.35% Finally range consistent 2BQ1 1 2BQ- 05 5 0. 9 93.4 31 3.15 3.35 93. 3.1 3.3 3.

ABSTRACT Rapeseed cutting and sunning are important processes associated with rapeseed harvesting in China. However, existing rapeseed windrowers have problems such as a tendency to entangle rapeseed plants, low laying quality, low operational efficiency, and high cutting power consumption for tall rapeseed plants. Hence, a new type of rapeseed windrower was designed. A new cutter with a planetary gear driving double moving cutting bars was designed, a motion model of the planetary gear mechanism was established, and the motion and structural parameters of the cutter were analysed. A theoretical analysis of this new cutting system showed that it not only had low cutting impact, minimal disturbance to rapeseed stalks, and high cutting continuity, but also low power consumption and stable operating performance. A reel based on a double-crank planar five-link mechanism was designed, and kinematics modelling and analysis of the new reel were carried out. The structure and motion parameters for the new reel were obtained. Finally, field experiments were conducted on both the newly designed rapeseed windrower and the original one. The results indicated that the newly designed windrower was suitable for high-quality, efficient cutting and sunning operations for tall rapeseed plants. China However plants quality efficiency Hence established analysed impact stalks continuity performance doublecrank crank fivelink five link out obtained Finally one highquality, highquality high-quality

ABSTRACT This study aimed to obtain the sorption isotherms of grain sorghum flour, fit mathematical models to experimental data, and recommend safe water levels for preserving the material at different temperatures. Sorghum grains with an initial moisture content of 34% dry basis (db) were subjected to drying at a temperature of 60 °C, reaching moisture contents ranging from 32.20 to 9.8% db. Water activity was obtained using Hygropalm Aw1 equipment placed inside a BOD chamber at temperatures of 10, 20, 30, and 40 °C. The experimental data were fitted to mathematical models frequently used to predict the isotherms of plant products. The modified Oswin model presented the best fit to estimate the sorption isotherms of sorghum flour. Safe moisture content limits for storing sorghum flour are 18.45, 17.3, 16.08, and 15.2% db for temperatures of 10, 20, 30, and 40 °C, respectively. The isosteric heat for the range of equilibrium moisture contents from 9.80 to 32.20% db varied from 2779.49 to 2498.46 kJ kg−1. 34 (db 6 C °C 3220 32 20 32.2 98 9 8 9.8 Aw 10 30 4 products 1845 18 45 18.45 173 17 3 17.3 1608 16 08 16.08 152 15 2 15.2 respectively 980 80 277949 2779 49 2779.4 249846 2498 46 2498.4 kg1 kg 1 kg−1 322 32. 9. 184 18.4 17. 160 0 16.0 15. 27794 277 2779. 24984 249 2498. kg− 18. 16. 27 24

ABSTRACT To meet environmental standards and sustainability policies, diesel-biodiesel-ethanol blends have been investigated as partial replacements for diesel. This study aimed to evaluate the emissions and performance of a diesel engine using a blend of biodiesel and ethanol. Four blends of biodiesel (7, 10, 15, and 20%) and four blends of ethanol (0, 1, 3, and 5%) were used. The power remained stable with the addition of biodiesel but decreased with the addition of ethanol for all blends. The power was reduced from 76 to 74 kW when the ethanol content increased from 0 to 5%. The addition of 1% ethanol increased the maximum torque linearly from 372 to 378 kW. A slight increase in the specific consumption was observed with the use of biodiesel and ethanol in the blend. An increase in the ethanol fraction from 0 to 5% reduced nitrogen oxide emissions, especially at high loads. Ethanol caused a decrease of up to 42% in nitrogen oxide. At high loads, a reduction in carbon monoxide emissions was observed with an increase in the blends of ethanol and biodiesel. Blends of biodiesel and ethanol with concentrations of 20% biodiesel and 5% ethanol are alternatives to diesel in agricultural tractor engines. policies dieselbiodieselethanol 7, 7 (7 10 15 20 0, (0 1 3 5 used 37 loads 42 engines ( 2 4

ABSTRACT Considering the spectral differences between broadleaf weeds and narrow-leaf crops and the influence of terrain and soil variables on weed infestations, integrating such information into a machine-learning algorithm can lead to accurate weed maps. Therefore, we aim to evaluate the effectiveness of these variables in classifying the occurrence of broadleaf weeds in narrow-leaf crops. Weed data was collected at georeferenced points across two areas covering 200 ha (pasture) and 106 ha (sorghum), creating classes 0 (absence) and 1 (presence). For each sample point, we obtained 11 variables: soil clay content, cation exchange capacity, soil organic matter, terrain elevation, slope, NDVI, EVI, CIgreen, BGND (derived from PlanetScope images), and spatial information (X and Y coordinates). These variables were used as predictors of broadleaf weed presence and absence in the Random Forest classification algorithm. The presence and absence of broadleaf weeds were correctly classified in 84% and 74% of all predictions in the test sample sets for pasture and sorghum areas, respectively. This strategy represents an efficient way to map and manage the occurrence of broadleaf weeds in narrow-leaf crops. narrowleaf narrow leaf infestations machinelearning machine learning maps Therefore 20 (pasture 10 sorghum, , (sorghum) (absence presence. . (presence) point content capacity matter elevation slope NDVI EVI CIgreen derived images, images images) X coordinates. coordinates coordinates) 84 74 respectively 2 (sorghum (presence 8 7

ABSTRACT The geometric characteristics, physical parameters and contact parameters of coated cotton seeds were obtained through theoretical analysis and experimental measurements. A discrete element model reflecting the intrinsic parameters of cotton seeds was established, the contact parameters of coated cotton seeds were calibrated using Discrete Element Method (DEM), and the significant factors affecting the stacking angle were screened using the Plackett-Burman method. The optimal ranges of the parameters were determined by the steepest ascent method, and a second-order regression model of the stacking angle and significant parameters was established by a central composite design. The following optimal values were obtained for cotton seeds: collision recovery (0.38), static friction (0.57) and rolling friction (0.19) coefficients. The average value of the stacking angle obtained by simulations with the best combination of parameters was 30.52°, the result of the simulation test was 30.91°, and the relative error was 1.278%. It is shown that the discrete element particle model and calibration parameters of cotton seed are closer to the actual situation, which can provide theoretical reference for analyzing the precise seeding of cotton seed and optimizing the structural parameters of seed disperser. characteristics measurements DEM, DEM , (DEM) PlackettBurman Plackett Burman method secondorder second order design 0.38, 038 0.38 0 38 (0.38) 0.57 057 57 (0.57 0.19 019 19 (0.19 coefficients 3052 30 52 30.52° 3091 91 30.91° 1278 1 278 1.278% situation disperser (DEM 03 0.3 3 (0.38 0.5 05 5 (0.5 0.1 01 (0.1 305 30.52 309 9 30.91 127 27 1.278 0. (0.3 (0. 30.5 30.9 12 2 1.27 (0 30. 1.2 ( 1.