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Abstract Background This study explores the use of Unmanned Aerial Vehicles (UAVs) for estimating wheat biomass, focusing on the impact of phenotyping and analytical protocols in the context of late-stage variety selection programs. It emphasizes the importance of variable selection, model specificity, and sampling location within the experimental plot in predicting biomass, aiming to refine UAV-based estimation techniques for enhanced selection accuracy and throughput in variety testing programs. Results The research uncovered that integrating geometric and spectral traits led to an increase in prediction accuracy, whilst a recursive feature elimination (RFE) based variable selection workflowled to slight reductions in accuracy with the benefit of increased interpretability. Models, tailored to specific experiments were more accurate than those modelling all experiments together, while models trained for broad-growth stages did not significantly increase accuracy. The comparison between a permanent and a precise region of interest (ROI) within the plot showed negligible differences in biomass prediction accuracy, indicating the robustness of the approach across different sampling locations within the plot. Significant differences in the within-season repeatability (w2) of biomass predictions across different experiments highlighted the need for further investigation into the optimal timing of measurement for prediction. Conclusions The study highlights the promising potential of UAV technology in biomass prediction for wheat at a small plot scale. It suggests that the accuracy of biomass predictions can be significantly improved through optimizing analytical and modelling protocols (i.e., variable selection, algorithm selection, stage-specific model development). Future work should focus on exploring the applicability of these findings under a wider variety of conditions and from a more diverse set of genotypes.

Deficit irrigation is a common strategy to reduce water use and improve the sustainability of cotton production. However, the effects of water deficits on crop productivity and quality are subject to genotype by management by environmental interactions. This study investigated effects of water deficits and frequency of irrigation on cotton performance grown in semi-arid region, Xinjiang, the main cotton-growing area in China. Two field trials (2020 and 2021) with split experimental design, including main factors of three irrigation levels (moderate-deficit, mild-deficit and full-irrigation) and split factors of three irrigation frequencies (4, 8 and 12 days) were conducted. Results from two trials both showed little negative influence of irrigation levels on yield, and higher irrigation frequency improved yield under same irrigation level. Significant effects of irrigation levels on yield components were found in 2021, with a 22 % increase in boll number and an 18 % reduction in boll weight under moderate-deficit irrigation compared with those under full-irrigation. Interactions between irrigation levels and frequencies significantly affected harvest index (HI), showing that reduced irrigation might be beneficial for improving HI. However, decreased fibre length while increased fibre micronaire were found under deficit irrigation. A strong association between radiation use efficiency (RUE) and boll growth rate was observed, suggesting that RUE might be the driving force of yield formation. A tight correlation between both biomass and transpiration efficiency versus delta temperature between air and canopy (ΔTair-canopy) was observed, suggesting ΔTair-canopy could be used as an efficient tool to assess plant production under deficit irrigation. This study provided an improved understanding of the physiological basis of cotton yield formation and further identified a high-throughput and instantaneous method to monitor effects of deficit irrigation on crop productivity.

The present study aimed to investigate the potential of ultrasonic treatment during fermentation for enhancing the quality of fortified wines with varying time and power settings. Chemical analysis and sensory evaluation were conducted to assess the impact of ultrasonic treatment on wine quality. Results showed that ultrasonic treatment could increase total anthocyanin and total phenol content, reduce anthocyanin degradation rate, and improve color stability. Moreover, ethyl carbamate content was lower in the ultrasonic group after aging compared to non-ultrasonic group. A combination of 200 W for 20 min resulted in higher sensory scores and more coordinated taste, while a combination of 400 W for 40 min produced higher levels of volatile compounds (21860.12 μg/L) leading to a richer and more elegant aroma. Therefore, ultrasound can be used as a potential technology to improve the quality of wine.

It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m2 m−2) (root mean square error (RMSE): 0.23 to 0.89 m2 m−2), including for sparse canopy (LAI less than 0.3 m2 m−2) grown on different soil types (RMSE: 0.02 to 0.25 m2 m−2). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water–nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.

High-throughput phenotyping has become the frontier to accelerate breeding through linking genetics to crop growth estimation, which requires accurate estimation of leaf area index (LAI). This study developed a hybrid method to train the random forest regression (RFR) models with synthetic datasets generated by a radiative transfer model to estimate LAI from UAV-based multispectral images. The RFR models were evaluated on both (i) subsets from the synthetic datasets and (ii) observed data from two field experiments (i.e., Exp16, Exp19). Given the parameter ranges and soil reflectance are well calibrated in synthetic training data, RFR models can accurately predict LAI from canopy reflectance captured in field conditions, with systematic overestimation for LAI

Existing corn harvester cutting blades have problems associated with large cutting resistance, high energy consumption, and poor cut quality. Using bionics principles, a bionic blade was designed by extracting the cutting tooth profile curve of the B. horsfieldi palate. Using a double-blade cutting device testing system, a single stalk cutting performance contrast test for corn stalks obtained at harvest time was carried out. Results show that bionic blades have superior performance, demonstrated by strong cutting ability and good cut quality. Using statistical analysis of two groups of cutting test data, the average cutting force and cutting energy of bionic blades and ordinary blades were obtained as 480.24 N and 551.31 N and 3.91 J and 4.38 J, respectively. Average maximum cutting force and cutting energy consumption for the bionic blade were reduced by 12.89% and 10.73%, respectively. Variance analysis showed that both blade types had a significant effect on maximum cutting energy and cutting energy required to cut a corn stalk. This demonstrates that bionic blades have better cutting force and energy consumption reduction performance than ordinary blades.

Wheat is one of the most common and productive crops in the world, and the growth and production of wheat are sensitive to climate change. Climate change affects wheat phenology by influencing sowing/maturity dates and the duration of growing stages, and ultimately affects the grain yield. Reliable prediction of changes in wheat yield is significant for maintaining global food security. However, studies addressing the potential impact of crop phenology changes on grain yield are rarely reported, which is a big challenge. Using the CERES-Wheat model and first-order differential equation models, we quantified the impact of future climate change on wheat phenology and grain yield for two periods (P1: 2011-2040; P2: 2041-2070) under three representative concentration pathways (RCP2.6, RCP4.5, RCP8.5) in China's twomain wheat-growing areas (Northwest SpringWheat-Growing Area(NWSWA) and Huang-Huai-Hai Winter Wheat-Growing Area (HHWWA)); the baseline period was 1981-2010. Accumulated thermal time during thewhole growing period (WGP) tends to significantly increase in NWSWA and HHWWA under all RCPs and periods. This rising accumulated thermal time will reduce the time to anthesis and maturity and shorten the vegetative growing period (VGP), reproductive growing period (RGP), and theWGP. In P2, spring wheat grain yield is projected to diminish by a regional average of 2.67% for RCP2.6, 4.07% for RCP4.5, and 5.66% for RCP8.5, which is dominated by the lower accumulated solar radiation during the VGP in NWSWA. Additionally, for the winter wheat grain yield in HHWWA in P2, its grain yield reduction is projected to decline by 4.8% (RCP2.6), 7.9% (RCP4.5), and 9.8% (RCP8.5), which is caused by increasing accumulated thermal time in the VGP. These results suggest that breeding high-temperature-tolerant varieties with high calorie requirements is an adaptive strategy to mitigate the negative impact of climate change on wheat yield in the study areas. [ABSTRACT FROM AUTHOR]

As one of the most populous countries, China has policies protecting the amount of land available for crop production, but it does not consider the quality of land. Thus, an important issue is the impact on crop production by land use change under such policies and by notable climate variability during the past decades. Using a productivity attenuation method, we estimated the climatic productivity of winter wheat (WWCP) in the North China Plain, based on land use classification data and daily weather data from 1980 to 2010. Impacts of historic climate variability and land use change on WWCP were quantified. Results indicate: (1) on average, WWCP for newly-increased dry land was lower than that for the lost land, which may result in reduced land quality associated with conversion. (2) In recent years, the increasing area of dry land provided sufficient wheat yield to compensate for the loss resulted from reduction of WWCP. (3) Both historic climate variability and land use change decreased WWCP, but the effects of the latter (within -1%) were less than those of the former (about -3%). Given the scarce land resource and rapidly growing food demand, the approach through expanding the area of cultivated land to maintain grain yield is not a long-term guarantee of food security. Thus, cultivated land conservation policy should shift from protecting land quantity into preserving land productivity. In addition, agriculture policies should further emphasize the critical role of adaptation strategies in coping with climate change. Obtaining higher yield production will depend upon adopting adaptation measures which could effectively mitigate effects of dramatic climate change or even take advantage of possible climate conditions. [ABSTRACT FROM AUTHOR]

As agricultural labor is decreasing, famous tea production is nearly hard to sustain and is getting more and more expensive in China. To improve the mechanization of tea plucking, this paper designs a self-propelled track-type tea plucking machine that integrates mechanic, hydraulic and electronics. The main parameters of chassis were optimized based on conditions of tea garden. And, three different kinds of cutter blades were designed and analyzed about vibration performance with finite element software. Also, a rigid-flex mixed motion model was established. Based on the model and plots, it is explicit that: 1) the blades move in a sine law; 2) the blades take a symmetrical deform from middle to end while moving; 3) the deformation increases along the blade from the center to sides. The single factor experiment revealed that both travel speed and speed ratio of cutting and travelling had a significant effect on quality indexes (integrity rate, unpicking rate, stubble unevenness) of plucked tea leaf. By universal rotary combination experiment of quadratic regression, the regression equations of three indexes about two experimental factors were established. And, the comprehensive optimization of three indexes was conducted with software Design expert. The optimal operating parameters of machine are as follow: travel speed is 0.41 m/s, speed ratio of cutting and travelling is 1.06; prediction of three indexes are that integrity rate could be reach to 78.26%, unpicking rate reduced to 0.82%, stubble unevenness reduced to 1.30 mm. The results of verification experiment are that: integrity rate could be reach to 77.41%, unpicking rate reduced to 0.87%, stubble unevenness reduced to 1.23 mm. These test indexes that are very close to the predicted values, all surpass the requests specified in the standard of tea plucking with machine. In all, the optimized parameter set is indeed synthetically optimal one that enables the wide use of this tea plucking machine, which can be used in the tea garden that has a lengthways slope less than 18°, a crosswise slope less than 20°, and a height of tea trees between 50-120 cm. From above, the tea plucking machine investigated in this paper provides an effective solution to present tea picking problem that is the bottleneck of tea industry. Further studies need to do for making this machine more automated and intelligent. [ABSTRACT FROM AUTHOR]

Currently, the process parameters for compression molding of pineapple rind residue are not clear. In view of this problem, a single die hole compression molding test device was designed in this study, and the force of material in a mold hole was analyzed. Using the test device, a three-factor three-level orthogonal test was carried out by using the particle size, moisture content, and die hole length-to-diameter ratio of pineapple rind residue as the factors and the particle molding rate, relax density, and specific energy consumption as the indicators. The test results were analyzed by range analysis, variance analysis, and fuzzy comprehensive evaluation. The test results show that the main and secondary factors affecting the comprehensive performance of pineapple rind residue compression molding are length-to-diameter ratio, particle size, and moisture content. The optimal parameter combination is the material particle size of 6-9 mm, moisture content of 16%, and length-to-diameter ratio of 4:1. The best indicators under these conditions are particle molding rate of 97.80%, relax density of 1.32 g/cm, and specific energy consumption of 44.17 J/g. These research results can provide a reference for the selection of processing parameters and the design of molding equipment. [ABSTRACT FROM AUTHOR]

In order to solve the problems of uneven stubble, low cutting efficiency and frequent breaking and blocking in the cutting and conveying links of ramie combine harvester, a reciprocating double movable blades cutter and a double-layer chain conveyor were designed, and the operating parameter test and optimization were carried out by using the central combination test design theory, with the emphasis on the influence of the forward speed, the cutting speed of the cutter and the conveying speed of the chain on the cutting efficiency, the failure rate and the conveying rate, and the multi-objective optimization was carried out based on these response indicators. Firstly, the structure and operating parameters of the cutting-conveying mechanism of ramie combine were studied. Then, the experiment was designed by the quadratic orthogonal rotation combination test method, and the data is processed by Design-Expert. The regression mathematical model of cutting efficiency, failure rate and conveying rate was established and variance analysis was carried out. By analyzing the effect of interaction of various factors on cutting efficiency, failure rate and conveying rate by response surface methodology, and performing multi-objective optimization on the regression model according to the importance of the optimization target, the optimal combination of the operating parameters of the cutting-conveying parts of the ramie combine harvester was obtained: when the forward speed was 0.85 m/s, the cutting speed was 1.40 m/s, and the chain conveying speed was 1.33 m/s, the cutting efficiency and conveying rate were the maximum and the failure rate was the minimum, with the values of 44.36 plants/s, 93.60% and 4.16%, respectively. The optimized parameters were verified in the field on the ramie combine harvester. In the field test, the cutting efficiency, conveying rate, and failure rate were 43.80 plants/s, 92.45%, and 4.52%, respectively, and the relative errors with the optimized values were 1.3%, 1.2%, and 8.7%, respectively, which was relatively consistent. [ABSTRACT FROM AUTHOR]

Aiming at the planting characteristics of hemp in southern hilly regions, a two-wheeled walking hemp harvester suitable for harvesting hemp in southern hilly regions is studied and designed. The harvester mainly consists of a header frame, single-moving cutter, cutter mechanical transmission, stalk lifter and reel, stalk divider, stalk horizontal conveyor, wheeled chassis, motor, gearbox, etc. To improve the cutting performance of the two-wheeled walking hemp harvester, response surface tests of three levels are conducted for three factors influencing the operation quality, including the cutting speed, blade length, and forward speed, on the constructed hemp cutting test bench. Moreover, test results are analyzed with the response surface method, and multi-objective optimization is carried out for the regression mathematical model with Design-Expert software. Results show that when the cutting speed is 1.2 m/s, the blade length is 120 mm, forward speed is 0.6 m/s, the cutting efficiency is 38.92 stalks/s, the cutting power is 776.37 W and the failure rate is 6.24%. Trial production of sample machine and field trial are finished according to the optimized parameters and structural design scheme, and the test results reveal that the cutting rate can reach 92.5%, the rate of transmission can reach 86.7%, the productivity is 0.18 hm2/h, and all performance indexes can meet the design requirements. This research can provide references for resolving the mechanical harvesting of hemp. [ABSTRACT FROM AUTHOR]