Abstract in English:

ABSTRACT The combine harvester is a widely used piece of agricultural equipment in modern agriculture, and the seed loss rate is one of the important indexes used to measure its operational performance, so the monitoring of the seed loss rate is crucial for adjusting the operational parameters of combine harvesters and improving the quality of grain harvesting. Aiming at the problems of the slow response speed and low monitoring accuracy of the existing domestic seed loss rate monitoring models, this paper proposed a rice seed loss rate monitoring method based on the whale optimization algorithm-back propagation neural network (WOA-BP). The loss rate monitoring device consisted of a piezoelectric ceramic sensor module, charge amplification circuit, band-pass filter circuit, analog-to-digital (AD) converter, main control unit, etc. The WOA-BP algorithm, which has a high accuracy and fast response speed, was used to classify and count the signals to realise seed loss rate monitoring. The indoor test results showed that the relative errors of the monitoring results are less than 8.5% under the condition of a conveying speed of 1.3-2.1m/s, and the relative errors showed an increasing trend as the proportion of straw increased.

Abstract in English:

ABSTRACT At present, there are serious problems in sorghum harvesting, such as lodging entanglement and the loss of broken stems due to the lack of an external dividing device for the cutting table and holding device. Based on the physical and mechanical characteristics of sorghum plants, an integrated outer divider was developed. The main structural parameters and working parameters of the outer divider were determined. A comparative test of the working quality of the outer divider was carried out. The results show that the working quality of the cutting table with the outer divider is obviously better than that without the outer divider and that the harvest loss of the cutting table is effectively reduced. The Box–Behnken experimental design method was used to investigate the effects of the forward speed, rotation speed of the grain lifter and dividing angle of the outer divider on the lodging and broken stem loss rates during sorghum harvesting. The regression mathematical model and response surface of the lodging and broken stem loss rates and the analysis factors were established, and the optimal working parameters of the outer divider were determined as follows: the dividing angle of the outer divider was 20°, the forward speed was 0.8 m/s, and the rotating speed of the grain lifter was 330 rpm. Under these parameters, the loss rate of the fall was 1.08%, and the loss rate of broken stems was 1.05%, which met the requirements of sorghum cutting tables

Abstract in English:

ABSTRACT The anti-clogging device of a no-till seeder is an important component that affects the seeding quality. In one year two crop area of China, a no-tillage seeder for corn generally includes a passive anti-clogging device, and there are often problems with straw winding around working parts due to the low straw cleaning rate. To solve these problems, a support cutting anti-clogging device with vibration for a no-till seeder was designed in this study to efficiently cut wheat stalks and remove them to the sides of the seedbed. Through theoretical calculations and kinematic analysis, the main structural parameters of the device were limited to small ranges of values: the mounting angle of the vibrating knife was in the range θ1 = 70–82°, the amplification was in the range l1 = 14–24 mm, and the frequency was in the range ωm = 240–340 rad/min. To analyse the effects of the main parameters on the average straw cutting rate, soil bin experiments were carried out using an orthogonal multinomial regressive experimental design with three factors and three levels. The optimal structural parameters derived in this way were as follows: mounting angle for the vibrating knife θ1 = 70.7°, amplification l1 = 19 mm, and frequency ωm = 330.2 rad/min. The results of adaptability and comparison tests showed that the average straw cutting rate was 93.30% when the proposed anti-clogging device with vibration was used. Compared to traditional devices, the cutting rate for the device was increased by 12.82%, and the metrics were superior to those of a traditional anti-clogging device. This research can serve as a reference for designing anti-clogging devices for no-till seeders.

Abstract in English:

ABSTRACT Computer vision systems combined with machine learning techniques have demonstrated success as alternatives to empirical methods for classification and selection. This study aimed to classify tomatoes based on their colorimetric characteristics, which influence consumer purchasing potential, using the decision tree algorithm. Tomatoes were categorized into two classes based on ripeness: Higher Purchasing Potential (20 fruits) and Lower Purchasing Potential (40 fruits). Images were captured in the RGB color model and converted to HSI and CIELab models. Principal component analysis was employed to evaluate the influence of colorimetric characteristics within each class, and the decision tree algorithm was applied to classify the fruits into the respective categories. Tomatoes in the Higher Purchasing Potential class were primarily influenced by red intensity and chromaticity a and b, while tomatoes in the Lower Purchasing Potential class were influenced by green intensity and hue. The decision tree achieved an accuracy of 83.6% and an F1-score of 90.9%, demonstrating its potential for classifying tomatoes based on colorimetric characteristics linked to consumer preferences.

Abstract in English:

ABSTRACT Due to the complex distribution of greenhouse seedbeds and the limited area of greenhouses, the accuracy of the positioning of modular planting of crops is required to be high. Moreover, modern greenhouses are mostly vertical planting structures. Most of the existing positioning modes only support plane positioning, and cannot determine the specific position of the seedbed in space through three-dimensional space positioning. In order to spray pesticides on various crops planted vertically in a modern greenhouse, a greenhouse fixed-point spraying system based on Ultra-Wideband (UWB) indoor positioning was designed. The system uses UWB indoor positioning technology to cope with the complex environments in modern greenhouses. Four base stations and one label are used to obtain the position coordinates of vertically planted crops. This information is processed through an embedded kernel, and then the upper computer sends instructions remotely. The motor drives a screw to rotate and move the nozzle to the location of the crop to complete the pesticide spraying. Experiments show that the real-time accuracy in the coordinates collected by this system is below 10 cm, which makes fixed-point spraying feasible in a modern greenhouse.

Abstract in English:

ABSTRACT Emitter clogging is the main limitation of drip irrigation systems operating with wastewater. This paper aimed to employ discriminant analysis (DA) to generate classification functions that characterize aquaculture wastewater (AW) dilutions in well water (WA), delivered through non-self-compensating drippers. Five AW dilutions in WA were tested (D1: 100% AW; D2: 75% AW + 25% WA; D3: 50% AW + 50% WA; D4: 25% AW + 75% WA; and D5: 100% WA) to investigate the clogging susceptibility of three non-self-compensating dippers: TS (1.6 L h-1), SL (1.6 L h-1), and NJ (1.7 L h-1) after 160 h of operation. Three hydraulic performance evaluations of the drippers were performed in this period. During the same interval, the quality attributes of the AW dilutions in WA were also quantified. The statistical analyses included correlation matrix and DA. The correlation matrix identified 188 variables with significant correlations. Discriminant functions were constructed for each dripper using DA. These functions revealed Mg2+ as the most significant variable. The classification matrix of these functions achieved a 100% success rate.

Abstract in English:

ABSTRACT The selection of materials and substrates is essential for optimizing the thermal performance of green roofs. However, there has been limited research on green roof characteristics under subtropical conditions. Therefore, this study aims to evaluate the internal and substrate temperatures of six green roof prototypes and one control prototype. Prototypes with clay tiles (control), clay substrates with and without vegetation, sandy substrates with and without vegetation, and organic matter substrates with and without vegetation are evaluated. The experimental design involves randomized blocks and the internal and substrate temperatures are monitored. The vegetated sandy substrate prototype exhibits the highest thermal performance, with internal temperatures 0.6 ℃ lower than those of other green roof prototypes and 1.7 ℃ lower than that of the control with clay tiles. This is attributed to the high porosity of the sandy substrate, which enhances thermal insulation. To provide optimal thermal performance, the substrate must have a water retention capacity that is sufficient to guarantee vegetation development, but not excessive so that it constantly increases the thermal conductivity owing to substrate saturation.

Abstract in English:

ABSTRACT During blueberry picking operations, changes in the blueberry harvester’s body posture (BHBP) caused by undulating farmland surfaces significantly affect the operational stability of the harvesting device. To mitigate these effects, an active suspension (AS) control system was developed. First, considering the coherence and time lag of the four-wheel tracks, the filtered white noise method was chosen to generate road excitations. Subsequently, the virtual prototype model of the blueberry harvester (BH) was built in ADAMS with the nonlinear properties of the passive suspension. A fuzzy proportion integral differential (PID) control algorithm and decoupled control strategy were employed to design the AS control system. Finally, ADAMS-MATLAB co-simulations and field tests were conducted. The results indicate that the measured farmland road profile conforms to standard D-level road excitations according to ISO8608, and the co-simulation model accurately predicts the BH’s dynamic response. Under AS control, the vertical acceleration, pitch acceleration, and roll acceleration of the BH decreased by 38.52%, 37.39%, and 34.29%, respectively, during simulations compared to the passive suspension. In field tests, these reductions were 36.51%, 33.84%, and 30.21%, respectively. The AS control system proposed in this study significantly improves the stability of the BH under farmland road excitation, effectively mitigating equipment impacts and wear caused by machine jolts while enhancing harvesting performance. This research addresses the gap in applying AS technology within the field of harvesting machinery, offering a novel technical approach for the development of vehicle control systems for harvesting machinery targeting blueberries and other shrub crops. It holds considerable theoretical value and practical significance.

Abstract in English:

ABSTRACT In light of the problems of large operation resistance and small soil fragmentation during the harvesting operations of existing cassava harvesters, a long- and short-toothed digging shovel was designed. A virtual simulation soil trough model of cassava ridge soil particles was established using the discrete element method, and the Hertz–Mindlin with JKR contact model was employed to simulate the operation quality of the long- and short-toothed digging shovel and the original digging shovel. In the movement and force analysis of the digging shovel, the angle of entry, the advance speed of the machine, and the height of the digging adjustment were the test factors. The response surface test was conducted on the digging rate and the damaged cassava rate. The results of the experimental field trial showed that the average digging rate of harvested cassava increased by 2.56%, and the average rate of damaged harvested cassava decreased by 1.54%, compared with the original digging shovel. The digging operation process was stable and met the requirements of cassava harvesting field operations. The results of this study may inform future studies on the design and improvement of a cassava harvester.

Abstract in English:

ABSTRACT Traditional methods for assessing crop water status have limited practicality for field applications. Conversely, remote sensing via remotely piloted aircraft (RPA) offers a promising alternative, though its effectiveness requires validation in specific studies. This study utilized RPA-derived imagery to support strategies for monitoring water deficit (WD) in corn crops and enabling yield prediction. The AG 1051 corn genotype underwent deficit irrigation levels (20, 40, 60, 80, and 100% of crop evapotranspiration—ETc) at different phenological stages: initial (E1), vegetative growth (E2), flowering (E3), and physiological maturity (E4) in Iguatu, Ceará, Brazil. We measured ten vegetation indices (VIs) and ear and biomass yield. Pearson's correlation analysis revealed that, during E2, the normalized difference vegetation index (NDVI) (r = 0.98) and green leaf index (GLI) (r = 0.99) were the most reliable for distinguishing water stress levels. These indices were also effective in predicting yield in E2 through regression analysis. The findings demonstrate that vegetation indices derived from RPA imagery provide a robust method for assessing water conditions and forecasting yield.

Abstract in English:

ABSTRACT Precision control of tillage depth is crucial for optimizing soil preparation in tea plantations. This study presents an adaptive real-time tillage depth control system based on Linear Active Disturbance Rejection Control (LADRC) to address precision challenges in the tillage depth of electric rotary tillers in tea plantations. The system, constructed using body posture sensors, control units, and hybrid stepper motors, integrates sensor data and LADRC technology to drive the stepper motor, enabling precise tillage depth control. The displacement sensor signals were collected, and the actual tillage depth was compared with the target values, allowing adjustments to achieve closed-loop control of the rotary tiller. Field experiments at speeds of 0.5 km/h and 0.8 km/h with tillage depths of 80 mm and 100 mm demonstrate the system’s effectiveness. The LADRC system achieved a standard deviation of 3.2 mm, outperforming fuzzy PID (10.5 mm) and sliding mode control (5.9 mm). The rate of depth variation was reduced by 44.8% and 68.9% compared to the fuzzy Proportional Integral Derivative (PID) and SMC, respectively. These results confirm that the LADRC-based system effectively minimizes interference during rotary-tiller operation, ensuring the stability and reliability of tillage depth control.

Abstract in English:

ABSTRACT The integration multispectral sensors with machine learning algorithms has demonstrated increasing efficacy in the classification of various maize morphophysiological characteristics. The hypothesis of this study is that maize plants subjected to different irrigation management practices exhibit distinct spectral behaviors, allowing for their classification through machine learning modeling. Thus, the objective of this study is to classify maize hybrids in different irrigation management practices using multispectral images. This involves identifying the most effective machine learning algorithms and inputs variables that enhance model performance for accurate classification. The experiment was conducted at the experimental facility of the Federal University of Mato Grosso do Sul, in Chapadão do Sul – MS. Seven hybrids were evaluated: H1 (AS 1868), H2 (DKB 360), H3 (FS 615 PWU), H4 (K 7510 VIP3), H5 (NK 520 VIP3), H6 (P 3858 PWU), and H7 (SS 182E VIP3). These hybrids were subjected to irrigation and non-irrigation management practices. Sixty days after crop emergence, images were captured in the blue (475 nm, B_475), green (550 nm, G_550), red (660 nm, R_660), red edge (735 nm, RE_735), and near-infrared (790 nm, NIR_790) bands using the Sensefly eBee RTK fixed-wing Remotely Piloted Aircraft, equipped with a Parrot Sequoia multispectral sensor and RTK (Real-Time Kinematics) technology. Through the collected band data, the ESRI ArcGIS 10.5 geographic information system software was used to calculate 41 vegetation indices (VIs). Data were analyzed using machine learning techniques, testing six algorithms: Logistic Regression (RL), REPTree (DT), J48 Decision Trees (J48), Random Forest (RF), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). Three accuracy metrics were utilized to evaluate the algorithms in the classification of irrigation management: correct classifications (CC), Kappa coefficient and F-Score. The ANN and RF algorithms demonstrated better accuracy in classifying maize hybrids with respect to irrigation management. The use of Vegetation Indices (IVs) and Spectral Bands + Vegetation Indices (SB+IVs) enhanced performance of these algorithms.

Abstract in English:

ABSTRACT The potential of oilseed radish at two sowing dates for use as a raw material in biogas (biomethane) production based on laboratory anaerobic digestion with the addition of inoculum with a 60-day incubation period was investigated. The results of biogas productivity were compared with traditional cruciferous species used for bioenergy purposes. In the spring-sown variants, the achievable level of ground bioproductivity of oilseed radish was set at 25.17 t ha−1 of raw and 3.20 t ha−1 of dry matter, which provided a biomethane yield (SMY) of 320.07 ± 31.39 LN kg−1ODM and an indicator of methane accumulation intensity (Rm(ef)) of 130.76 ± 10.20 LN kg−1ODM d−1 with an appropriate biochemical portfolio of the formed biomass. During the summer sowing period, the average bioproductivity of oilseed radish was 18.42 t ha−1 in raw weight and 2.81 t ha−1 in dry matter, which provided an SMY of 262.97 ± 24.64 LN kg−1ODM and an Rm(ef) of 122.22 ± 3.62 LN kg−1ODM d−1 with its appropriate biochemical composition. The maximum level of biomethane production from oilseed radish was achieved with spring sowing under the conditions of 2021, resulting in the following technological parameters of productivity: MS 55.84 ± 9.39%, SMY 359.25 ± 11.24 LN kg−1ODM, Rm(ef) 138.15 ± 1.78 LN kg−1ODM d−1, Rm(full) 31.51 ± 1.69 LN kg−1ODM d−1, t50 4.12 ± 0.34 days, and λ 1.74 ± 0.17 days.

Abstract in English:

ABSTRACT Hydrological data are typically sequential and often correspond to large datasets, and not all application programs allow their retrieval, storage, and manipulation. This technical article aims to contribute to this subject by reporting the applicability of the Data Storage System of the Hydrologic Engineering Center (HEC-DSSVue) using 5-min rainfall datasets collected from 10 self-recording rain gages (2014–2023) installed in Southern Brazil. Although the HEC-DSSVue is commonly used to integrate databases into other HEC models (HEC-HMS and HEC-RAS), its application in managing hydrological gaging networks remains undocumented. Our methodology comprised six steps conducted within the HEC-DSSVue: i) acquiring all files generated from each hydrological campaign, ii) addressing irregular-interval time series, iii) evaluating file management functionalities, iv) identifying data visualization functionalities, v) assessing mathematical operations, and vi) examining edition and export functions. The main conclusion was that the HEC-DSSVue is a powerful tool for efficiently managing datasets from hydrological gage networks and supporting data manipulation, thereby making routine tasks more efficient and less susceptible to user-introduced errors.