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Plant protection UAVs have developed rapidly as a new type of orchard pesticide application equipment. This study was conducted to explore the influence of plant protection UAVs' operation parameters on the properties of droplet deposition. The DJI T30 plant protection UAV was used to affect the influences of spray volume (60 L/ha and 90 L/ha), spray height (2m and 2.5m), with or without an adjuvant and spray patterns (Regular pattern and Orchard pattern) on characteristics of droplet deposition. The test results showed that the interaction of multiple factors has no significant difference in most droplet deposition on various layers of fruit trees. The deposition characteristics of droplets were improved at a spray height of 2 m and a spray volume of 90 L/ha in the presence of adjuvants. In the orchard spray pattern, the penetration of droplet deposition is improved, but the impact on droplet density and coverage is different. Overall, the orchard spraying pattern can achieve better coverage, while the droplet density is the opposite. The spray performance is closely related to the spray volume, with or without an adjuvant, spray pattern, and spray height. The research results provide a theoretical basis for selecting appropriate operating parameters and spray patterns for plant protection UAVs in apple orchards. • The orchard spray pattern can obtain better coverage, and penetration. • Deposition on the ground will be affected by the downwash from the UAV. • The spray volume is the main factor affecting droplet deposition. • The regular spray pattern can obtain better droplet density. [ABSTRACT FROM AUTHOR]

Abstract: Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed. [Copyright &y& Elsevier]

• The PLL/WS2/LIG electrode with 2D-on-2D layered structure exhibits an excellent electrochemical property towards CBZ. • The flexible electrochemical sensor, encapsulated by 3D printing, achieve the non-destructive in-situ detection of CBZ on tea leaves. • Easy-to-use visual online cloud platform is developed for convenient and intelligent detection. • Real-time intelligent analysis is realized to monitor the change in CBZ residues to guarantee food safety. Excessive use of carbendazim (CBZ) for controlling fungal disease may result in pesticide residues in growth environments. There is an urgent requirement for real-time detection of CBZ content to enable rapid assessment of pesticide residues. Herein, a novel electrochemical sensor was created, which utilizes a laser-induced graphene (LIG) electrode modified with two-dimensional (2D) tungsten disulfide (WS 2) nanosheets and a poly(L-lysine) (PLL) film to construct a 2D-on-2D layered structure. The PLL/WS 2 /LIG electrode exhibits strong charge transfer, excellent electrocatalytic performance, and high CBZ adsorption capacity, thus leading to enhanced sensitivity. As a result, the sensor exhibits a low detection limit of 1.2 ng/mL (6.2 nM), high sensitivity up to 135.1 μA(μg/mL)−1cm−2, and a wide response range of 0.01 to 1.5 μg/mL. Moreover, an intelligent analysis system, consisting of a portable device that connects to an online cloud platform equipped with a machine learning model and visualization software, has been developed for the real-time detection of CBZ residues on tea leaves in tea plantations. This study demonstrates that the PLL/WS 2 /LIG sensor and the intelligent analysis system have great potential to monitor pesticide residues to guarantee food safety. [Display omitted] [ABSTRACT FROM AUTHOR]

Real-time monitoring of leaf area index (LAI) in cotton (Gossypium hirsutum L.) plays a vital role in guiding field fertilization, water management, growth observation and yield prediction. The use of unmanned aerial vehicles (UAVs) equipped with diverse sensors enables flexible and rapid LAI measurement across extensive areas. This study evaluates the efficacy of UAV-mounted LiDAR and high-resolution camera-derived point cloud data in LAI prediction. We assessed the integration of canopy spectral-textural characteristics from multispectral data with structural features from point cloud data for LAI forecasting. Furthermore, we compared various machine learning and deep learning models, selected the optimal one, and applied the SHAP (Shapley Additive Explanations) method to identify key features and their influence patterns in this model. The findings are distilled into four key points: (1) The performance of canopy structure metrics based on the two sensors varied across fertility periods due to differences in canopy closure; (2) The DNN-based (Deep Neural Network) LAI prediction model excelled with a single-period dataset, achieving an R2 of 0.81 and an RMSE% of 11.36 %. Similarly, in full-period multimodal data fusion, it demonstrated superior performance, evidenced by an R2 of 0.84 and an RMSE% of 9.94 %. (3) Compared to the unimodal data model, the multimodal data model yielded superior results and exhibited greater robustness. (4) In the DNN-based LAI prediction model utilizing multimodal data, texture features contributed most significantly. The results suggest that the DNN model, when employing multimodal data fusion, offers not only relatively precise and robust estimates of crop LAI but also contributes valuable insights for crop phenotyping and enhanced field management. This approach subsequently improves spatial prediction accuracy and the quality of decision-making in crop production. • Canopy structure metrics improve cotton LAI estimation accuracy. • Point cloud data and multispectral fusion improve LAI prediction accuracy. • Compared two sensors' structural indicators for LAI prediction. • DNN performs best in LAI prediction. [ABSTRACT FROM AUTHOR]

• An approach based on VNIR-SWIR hyperspectral imaging was proposed to detect bioactive compound contents in tea. • Prediction models were constructed for detecting the contents of five bioactive compounds in tea. • An IBS-CARS-Fusing method was developed to refine feature wavelengths for VNIR-SWIR hyperspectral data. • Thermal map of LCMA was employed to visualize spatial distribution of bioactive compounds. Hyperspectral imaging (HSI) provides opportunity for non-destructively detecting bioactive compounds contents of tea leaves and high detection accuracy require extracting effective features from the complex hyperspectral data. In this paper, we proposed a feature wavelength refinement method called interval band selecting-competitive adaptive reweighted sampling-fusing (IBS-CARS-Fusing) to extract feature wavelengths from visible-near-infrared (VNIR) and short-wave-near-infrared (SWIR) hyperspectral images. Combined with the proposed IBS-CARS-Fusing method, a kernel ridge regression (KRR) model was established to predict the contents of bioactive compounds including chlorophyll a , chlorophyll b , carotenoids, tea polyphenols, and amino acids in Dancong tea. It was revealed that the IBS-CARS-Fusing method can improve R p 2 of KRR model for these bioactive compounds by 4.77%, 4.60%, 6.74%, 15.52%, and 13.10%, respectively, and R p 2 of the model reached high values of 0.9500, 0.9481, 0.8946, 0.8882, and 0.8622. Additionally, a leaf compound mass per area thermal map was used to visualize the spatial distribution of the compounds. [ABSTRACT FROM AUTHOR]

Abstract: Milk thistle (Silybum marianum L.) is an excellent source of silymarin used as an antioxidant. Microwave-assisted extraction (MAE) was employed to extract silymarin from milk thistle seeds. The effects of four independents variables in terms of extraction time, temperature, ethanol concentration, and solid–liquid ratio on the silymarin yield were determined and the optimal conditions for silymarin were evaluated by means of response surface methodology. Correlation analysis of the mathematical regression model indicated that a quadratic polynomial model could be employed to characterize MAE process for the silymarin. Response surface plots showed that these independent variables, except for extraction time, and interactions significantly influenced the extraction yield of silymarin. The optimal extraction parameters to obtain the highest silymarin yield were time duration of 60min, temperature of 112°C, ethanol concentration of 81.5% (v/v), and a solid–liquid ratio of 1:38 (g/mL). The average experimental silymarin yield under the optimum conditions was found to be 56.67±1.36mg/g, which agree with the predicted value of 57.40mg/g. MAE method was applied successfully to extract silymarin from milk thistle seeds. [Copyright &y& Elsevier]

• The defoliation is influenced by UAV spraying volumes. • The boll opening rate is not associated with the spraying volumes. • The defoliation of UAV is similar to that of ground-based machine. • No negative influence is observed in fiber quality and lint cotton yield components by UAV application. Chemical harvest aids applied by ground-based machines with heavy wheels and high-volume spraying in cotton (Gossypium hirsutum L.) fields to defoliate leaves and ripen bolls before harvest threaten soil health and result in cotton yield loss. For example, ground-based machines roll over cotton plants at corners and pull the cotton branches between rows, resulting in yield loss. Furthermore, heavy wheels can cause soil compaction, resulting in decreased soil biodiversity. High-volume spraying often results in harvest aids polluting the soil. A new harvest-aid application method that can save water, increase harvest-aid utilization rate without damage to soils and crops is needed to apply chemical harvest aids before harvest. Small plant protection unmanned aerial vehicles (UAVs) with low-volume spraying methods have emerged as pesticide application technologies in recent years in China due to their high efficiency, high flexibility, low labor requirements, water savings and lack of damage to crops and soils. Here, UAVs are developed for chemical harvest-aid application by evaluating the cotton defoliation efficacy, boll opening rate, lint cotton yield and fiber quality components at two cotton field experimental sites in Xinjiang Uygur Autonomous Region of China in 2016 and 2017. Ground-based machine treatment is used as a reference. The assessment is proceeded with the following steps: (1) The spray volume of UAV is optimized in 2016; (2) The experiment of 2016 is repeated to determine the optimal spray volume in 2017. The results show the following: (1) The harvest-aid performance is sensitive to the spray volume in the UAV low-volume spraying treatment, and optimal defoliation efficacy is achieved when the spray volume is 22.5 L/ha and flight velocity is 4 m/s, according to the field tests in 2016 and 2017. (2) Low-volume spraying using UAV has no negative influence on the lint cotton yield and fiber quality components. In conclusion, it is feasible to use plant protection UAV for cotton harvest-aid applications in preparation for machine harvesting. [ABSTRACT FROM AUTHOR]

• A multi-view density-based field-road classification method was proposed. • An object detection method was used for field-road classification. • The combination of two field-road classification results used the DBI metric. Field-road classification that automatically identifies the operation modes (either in-field or on-road) of GNSS (Global Navigation Satellite System) points plays an important role for the operational performance analysis of agricultural vehicles. Intuitively, a field often has high point density because in-field driving speed is rather low and the distance between consecutive strips is closed. In this paper, two methods were used to capture the in-field high-density characteristic: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and an object detection model. DBSCAN is a widely-used density-based clustering algorithm, which clusters the points with high point density into a cluster. Alternatively, a trajectory can be transformed into an image, and an object detection model can be applied to detect objects in the image, where an object is a set of pixels with high pixel density (i.e., a set of points with high point density). Finally, the two field-road classification results are combined using DBI (Davis Bouldin index), a metric which can evaluate the quality of either classification result. The developed method was validated by the harvesting trajectories of two crops (wheat and paddy), and the density-based field-road classification achieved 85.97% and 73.34% accuracy on the wheat data and the paddy data, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Saponins are rich in the legumes which are known to provide many health benefits for human beings. Saponin B is the main component in the saponins group present in navy beans. The stability of saponin B during food processing is a key issue in evaluating the quality and nutrition of food products. The effects of different soaking and cooking methods and conditions on the stability of saponin B were investigated. The effects of the soaking process on saponin reduction followed a first order kinetic model. The soaking time and the seed-to-water ratio significantly affected the stability of saponin B during the soaking process. Short time soaking and lower seed-to-water ratio would keep more saponin B in the soaked beans. The cooking medium and methods greatly influenced saponin B degradation during cooking. Water–oil mixed cooking media enhanced saponins stability in the seeds during the cooking process, as compared to a water-only cooking medium. Combined soaking and ordinary cooking induced more saponin degradation in ordinary cooked seed samples. An autoclave cooking method eliminated most of the saponin B from the autoclaved beans. [Copyright &y& Elsevier]

Abstract: In this study, starch-based microparticles (MPs) fabricated by a water-in-water (w/w) emulsification-crosslinking method could be used as a controlled release delivery vehicle for food bioactives. Due to the processing route without the use of toxic organic solvents, it is expected that these microparticles can be used as delivery vehicles for controlled release of food bioactives. Octenyl succinic anhydride (OSA) starch was used as raw material. Optical microscopy showed OSA starch-based microparticles (OSA-MPs) had a good dispersibility. Scanning electron microscopy (SEM) showed OSA-MPs had a solid structure and spherical shape. X-ray diffraction (XRD) patterns revealed that OSA-MPs were of amorphous structure. A Plackett–Burman screening design methodology was employed to evaluate the effects of the process and formulation parameters on the particle size of OSA-MPs. Considering the statistical analysis of the results, it appeared that the OSA starch concentration (P =0.0146), poly(ethylene glycol) (PEG) molecular weight (P =0.0155), volume ratio of dispersed phase/continuous phase (P =0.0204) and PEG concentration (P =0.0230) had significant effect on particle size. [Copyright &y& Elsevier]

• Navigation line extraction was difficult for 6–14 leaf corn crop environment. • By white pixels proportion of total pixels index to get stalk location points. • By maximum value of column pixel adding curve to get root locating points. • Navigation line was extracted effectively using stalk and root locating points. The presence of weeds undermines the accurate algorithmic detection of navigation lines in cornfields, which plays a pivotal navigational role in the operation of intelligent agricultural machinery for tasks such as weeding and spraying. A method was proposed for extracting navigation lines by using the composite locations of the root and stalk of corn in the 6th- to 14th-leaf stages growing in a complex environment. The positional and area features were first used to eliminate the interference regions and obtain the root locations. The white pixel percentage index was used to filter the line segments obtained through the Hough transform and thereby determine the stalk locations. Experiments showed that the navigation lines were detected at an accuracy of 93.8%, an improvement of 10.2% over that achieved using conventional methods. [Display omitted] [ABSTRACT FROM AUTHOR]

• The main effect and interaction effect among the flight parameters were discussed. • The flight parameters of UAVs have remarkable effects on the droplet distribution. • The influence of flight parameters on droplet distribution uniformity is limited. A common problem of unmanned aerial vehicles (UAVs) spraying is that fewer droplets could reach the lower layer, and the depositions among the spray width show a significant difference. However, ensuring the penetration and uniformity of the spray is particularly important in the cotton harvest aid applications. As a new spraying method, poor droplet penetration and uniformity of droplet distribution may lead to inconsistent defoliation among canopy rows and poor defoliation in the lower part of the cotton canopy. The operational parameters of UAVs have remarkable effects on the droplet distribution, which will improve the defoliation rate of cotton. Therefore, full factorial tests were conducted in two experimental locations in the Xinjiang cotton-producing region in 2018 and 2019. The selected operational parameters in 2018 included the spray volume (three levels: 8.7, 12, and 15 L/ha), droplet size Dv50 (three levels: 100, 150, and 200 μm), and flight height (three levels: 1, 2, and 3 m). Meanwhile, the operational parameters in 2019 included the spray volume (three levels: 18, 22.5, and 30 L/ha) and droplet size Dv50 (three levels: 100, 150, and 200 μm). The main effects and interaction effects among the operational parameters were analyzed and discussed. The spray penetration and uniformity of droplet distribution were compared separately. The results show that flight height, spray volume, and droplet size have significant effects on spray penetration. Lowering the flight height of the drone, increasing the spray volume and Dv50 of droplets can improve the distribution of droplets at the bottom of the cotton canopy. As for droplet distribution uniformity, with the increase of the application volume and decrease of the flight height, the uniformity of the droplet distribution becomes worse. The influence of flight parameters on the uniformity of droplet distribution is very limited. [ABSTRACT FROM AUTHOR]

Rapid acquisition and mapping of normalized difference vegetation index (NDVI) values is essential for a timely evaluation of nitrogen use efficiency (NUE) in sustainable agriculture. The zonal nutrient management techniques for sustainable agriculture require timely and efficient NDVI data collection and analysis capabilities, – a need that is still rarely addressed. However, as with the use of manned aircraft, the primary limitation of established unmanned aerial vehicle (UAV) imaging technologies is the limited time window for large-area data collection. Furthermore, as the spatial resolution increases, the post-processing time generally becomes longer. To overcome problems associated with the need for rapid data acquisition and post-processing, we have developed a UAV-based sensor system called the Accurate and Speed Scanner (AS-Scanner). The data acquisition accuracy and NDVI mapping speed of the system were assessed. According to the results of a ground-level experiment, the mean absolute error (MAE) between the reflectance data of the AS-Scanner and the corresponding data of the ASD FieldSpec 3 spectrometer is less than 1.11%. Furthermore, results of a plot-level experiment suggest that the NDVI values of a raw point cloud from AS-Scanner -based UAV platform (AS-NDVI) and UAV-mounted MicaSense RedEdge camera (REG-NDVI) are highly correlated (0.54 < R2 < 0.57, p < 0.01). In a map-level experiment, the interpolated, 15 m × 15 m pixel size AS-NDVI values were in good agreement with the REG-NDVIs over a 4.01 ha (40100 m2) rice field (0.55 < R2 < 0.65, p < 0.01). The average processing times of the inverse distance weighted (IDW, 0.45 s), kriging linear (KL, 0.73 s), and nearest neighbor (NN, 1.05 s) interpolations of the AS-Scanner data prove that it is possible to efficiently create NDVI maps using data from the sensor. Besides, the effect of the varying solar irradiance values on the AS-NDVIs was analyzed under varied illumination conditions. Moreover, the AS-NDVIs were also used to assess the NUE in a paddy rice field at the panicle stage. The results show that nutrients can be effectively assessed using AS-Scanner data. Despite some disadvantages such as the potential of NDVI value saturation over dense canopies, the AS-Scanner provides a method for quickly obtaining reasonable spatial resolution crop information for agricultural nutrient monitoring and management. This also offers the potential to reduce environmental pressure which may be caused by untimely estimation of NUE. Image 1 • A UAV system with practicality for rapid acquisition and mapping of NDVI was designed. • The reflectance precision of the present system was similar to ASD spectrometer. • The NDVI results are highly correlated to those of the RedEdge camera (R2 > 0.54). • NDVI mapping time was shortened by using the present UAV system. • Beneficial to the nutrient zonal management technology of precision agriculture. [ABSTRACT FROM AUTHOR]