查看更多>>摘要：? 2022 The Author(s)Land suitability assessment (LSA) provides geospatial information about growing crops where they are best suited and can play a crucial role in addressing contemporary challenges such as feeding 9 billion people by 2050, coping with climate change, and enabling sustainable production. Despite the known limitations of the current mapping units (conventional soil map units) used for LSA, alternative methods to objectively delineate mapping units, such as geographic object-based image analysis (GEOBIA) or geomorphons have never been tested for LSA. The objective of this work is to quantitatively assess the effects of different polygon-based mapping units on LSA for agriculture: 1) conventional soil map units; 2) units delineated semi-automatically using GEOBIA; 3) geomorphons. In addition, the three delineation procedures will be compared with the pixel-based LSA conducted as a benchmark. LSA is conducted within the framework of the existing Romanian rating methodology for land suitability, which was developed based on FAO guidelines for land evaluation. We use georeferenced soil profiles with field-measured soil properties and digital terrain models to digitally map 17 eco-pedological indicators (e.g. soil pH, soil texture, soil porosity, gleization, carbonate content, humus content). Based on several lookup tables, these maps are transformed into digital maps with suitability ratings for 14 crops, 7 fruit trees, and 2 land-use types, ranging from 0 (not suitable) to 1 (maximum suitability). The product (multiplication) of the 17 maps with LSA ratings is the final suitability map for each crop and land use. Overall, the best maps were obtained when the LSA was conducted using the GEOBIA units (similar accuracy to the pixel-based approach), whereas geomorphons and conventional soil map units resulted in much poorer maps. GEOBIA is much more suitable for LSA than the conventional soil map units and geomorphons, showing a much higher accuracy and internal homogeneity. Another conclusion is that the LSA based on conventional soil map units recorded the poorest accuracies by far. Our results show that the GEOBIA-based units or pixel-based approach are the right choices when conducting LSA, however, if the map user (eg. farmer, land manager) needs delineated units as semi-permanent and stable regions to manage them as stable spatial entities then GEOBIA technique should be used for LSA.
查看更多>>摘要：? 2022 Elsevier B.V.Blueberries have become an important fruit crop around the world. Most blueberries are hand-harvested to maintain high quality before the packing process for fresh market distribution. However, in the last 10 years increasing acreage has been machine harvested to reduce labor costs although machine harvesting causes bruising in more than 20% of blueberries. Non-bruised blueberries remain firmer and can be cold stored longer than bruised blueberries, while the bruised fruit cannot be sold, leading to a substantial economic loss. Current packing line sorting technology can sort soft berries but is unable to detect and sort out bruised fruit. Blueberry bruise assessment is necessary for providing the means to improve the harvester efficiency and fruit sorting process in the packing house. The goal of this study was to develop a web browser-based application (Web App) that users can access easily and determine the blueberry bruises accurately and quickly. We annotated 1725 blueberries to train MobileNet SSD and MobileNet-UNet, two deep learning models, generating a berry detection model, a berry segmentation model, and a bruise segmentation model. The average precision (AP) for the berry detection model was 0.977. The mean intersection over union (IoU) for berry segmentation and bruise segmentation was 0.979 and 0.773, respectively. Bruises were assessed for 56 images with 50 sliced blueberries in each image using the trained models that implemented in the Web App we developed. The bruise ratio data obtained from three different hardware devices were compared with the results that were manually annotated. Linear regression analyses showed a high correlation between the results from the deep learning models and the ground truth. The bruise ratio prediction using three hardware devices achieved an accuracy of 78.7%, 79.0%, and 78.9%, respectively, indicating that the model performance was satisfactory regardless of the hardware configuration. The average processing time for each image under three hardware configurations revealed that our Web App was superior to the manual method. The Web App reduced the time needed for assessing and tabulating bruise damage for 50 fruit samples from about 15 min needed for manual visual method to less than 30 s with comparable accuracy. This Web App is a robust tool for blueberry breeders, farmers and packers for evaluating berry bruises.
查看更多>>摘要：? 2022 Elsevier B.V.To reduce rice harvest loss and explore the performance of different combs for threshing prior to cutting, a three-dimensional tip disturbance comb and a three-dimensional bionic disturbance comb were designed. Based on high-speed camera technology and bench tests, the throwing mechanisms, distribution characteristics and loss rates of three kinds of combs, including primitive plane angular combs, were explored. Through comparative analysis of the material diffusion angle and throwing mechanism, it was obtained that the average material diffusion angle of the three combs decreased gradually with increasing rotating speed and that the material diffusion angle and stability of the three-dimensional bionic disturbance comb performed best. At the same rotating speed, the fitting slope ratio of the three-dimensional bionic disturbance comb was the largest, and the effect of throwing and transporting materials was the best. The total loss rates of three kinds of comb teeth were obtained by analyzing the falling grain loss rate and uncombed loss rate: three-dimensional tip disturbance comb > primitive plane angular comb > three-dimensional bionic disturbance comb; the corresponding minimum total loss rates were 6.17 %, 5.76 % and 2.71 %.
查看更多>>摘要：? 2022 Elsevier B.V.Stored-grain pests cause serious economic losses during grain storage. Therefore, it is important for us to know the accurate number and species of stored grain pests as soon as possible so that appropriate measures can be taken to reduce economic losses. However, current research on grain pest detection has two problems. The background of the dataset does not contain any grain, so the results cannot be applied to detect pests in actual situations. The other problem is that methods based on pest traps cannot reflect situations on the surface of the whole granary. This paper proposes an automatic system of pest detection and counting to solve these problems. This system consists of two main parts: a deep learning object detection model called YOLOv4 and a small car with a camera and a supplementary light. And YOLOv4 model that has been trained is embedded in the car. The car can run on the surface of the granary. The camera on the small car can take photos of the pests for YOLOv4 to identify the species and number of pests. In the experiment, a few hundred kilograms of wheat were used to lay a simulated granary. And 2 typical stored-wheat pests, namely, the red flour beetle and the rice weevil, were taken as the research objects. The experimental results demonstrated that the mean average precision (mAP) of the proposed method reached 97.55%, which can meet the accuracy requirements of the detection and counting of pests in the granary in practical applications. The system solves the randomness and insufficient accuracy of pest traps and human eye recognition. And the system can be used to the early warning of pests in granary, which has high accuracy and completely release manual labor.
查看更多>>摘要：? 2022Early spring corresponds to the snowmelt season in apple orchards of the northern hemisphere. This has a significant effect on the initial growth of shoots and flower buds of apple trees. Understanding the soil environment of the orchard during the non-growth period as monitored by farmers is important for managing year-round apple production. However, the details of mass transport, such as that of soil water and solute, during the non-growing season in the Andosols remain unclear. This study conducted soil environment monitoring in an Andosol apple orchard in Aomori Prefecture, Japan, during the non-growing season in winter to examine the year-round changes in the soil environment, using a field monitoring system that is necessary for understanding the soil environment. Volumetric water content, soil bulk electrical conductivity, and soil temperature were measured, and the cumulative changes in total soil moisture (ΣΔTSM) were calculated from the monitored volumetric water content. Results showed that although ΣΔTSM tended to increase (i.e., dry out) annually with periodicity, the variation was small compared to that of apple orchards with gravelly brown forest soil. These results could significantly influence fertilization management of apple orchard soil during the summer drought season and early spring when the roots of apple trees begin taking up nutrients. In particular, it was revealed that if sufficient nutrients remain in the soil pore water in early spring, the negative impact of excessive fertilization on the surrounding environment can be reduced. Therefore, this study constitutes an innovative step in the implementation of field monitoring system to understand the details of mass transport in the Andosol apple orchard soil.
查看更多>>摘要：? 2022 The Author(s)This study presents a deterministic constrained optimisation algorithm developed for using in a pressurized irrigation network. In irrigation networks —or water networks supplied by a head tank— utility managers can fully adapt the delivery times to suit their needs. The program provides a strategy for scheduling water delivery at a constant flow rate (opening and closing of hydrants, units, and subunits) to minimise energy consumption. This technique improves on earlier approaches by employing a deterministic method with little computing time. This method has been tested in the University of Alicante pressurized irrigation network, where decision-makers have identified the need to diminish the energy expenditure for watering University's gardens.
查看更多>>摘要：? 2022 Elsevier B.V.In irrigated agriculture, soil spatial variability is a primary factor for low irrigation efficiency. In regions such as the Brazilian Cerrado, where there is a continuous growth of irrigated agriculture and limited water supplies, it is important to seek alternatives to reach an efficient and sustainable irrigated agriculture. In this context, precision irrigation has great potential. Better strategies must be established, especially for center pivot irrigation conditions. Irrigation considering management zones is a promising option; however, criteria must be set to delimitate management zones and evaluate irrigation performance in such situations. This study aims to assess the impact of the use of management zones on irrigation performance. Therefore, two center pivots, PivoBHBV and PivoBHALPA, with distinct soil water physical traits, were evaluated. Two management zones strategies were considered to carry out the simulations. One of the strategies was based on regions in the format of square grids with areas of 25, 100 and 225 m2. In the other strategy, the unsupervised grouping algorithm Fuzzy C-Means was used to create management zones. The irrigation performed on those management zones was assessed by comparing their performance with the irrigation conducted in the grid areas. The irrigation management zones were delimited based on the interpolated maps of soil available water capacity (AWC), and the management was conducted individually for each zone. The simulations were made through a model developed with the Python language. The results indicate similarity between the irrigation with management zones and the grid irrigation, as it did not significantly increase water and energy demands or reduce soybean yield. Precision irrigation practiced management zones increased irrigation efficiency, reducing the effect of soil spatial variability. The total irrigation depth applied was, on average, 1.47 % lower than the irrigation by grids. Comparing the grid and the management zones irrigations, water and energy savings potential and the average yield increase potential were 1.85 %, 1.75 %, and 0.41 %.
查看更多>>摘要：? 2022The external airflow and the auxiliary airflow of air-assisted application make the leaves vibrate during operation. Such vibration affects the deposition behavior of spray droplets on the leaves. In this study, A multiphase flow computational fluid dynamics (CFD) simulation model was developed and validated. Dynamic process simulations were performed using a validated CFD simulation model, which set spray droplet particle size, impact velocity, leaf tilt angle, and rotational angular velocity as variables. The simulation test results revealed that: the droplet velocity and leaf tilt angle were crucial factors affecting the deposition behavior of droplet adhesion, bouncing, and splitting; compared to static leaves, leaf vibration did not impact the deposition behavior selection of droplets under the influence of 5–10 m/s air velocity. However, the increase of leaves rotation angular velocity increased the droplet spreading to a certain degree. The CFD model validation test results conform with the simulation results. More specifically, the relative error of the longitudinal spreading factor was within 10% and the relative error of the spray droplet spreading diameter was within 20%. This study provides CFD models that can be used for the study of spray droplet deposition behavior on the surface of vibrating rice leaves, thereby providing a reference for rice plant protection and the selection of pesticide application parameters.
查看更多>>摘要：? 2022 Elsevier B.V.Decision making capability of a system is highly dependent upon the quality and quantity of training data. Majority of beehive monitoring systems developed for research purposes are designed to collect data through a small set of sensors, and from locations with little geographic diversity. This hinders the development of a dataset that can be used to effectively train machine learning models. In this work, we explain the design and development of a multi-sensory, remote data acquisition system for beehives (BeeDAS), with focus on low-power consumption and long-range communication. We address design challenges associated with such systems and highlight the critical issues that need consideration. The proposed system enables collection of data from beehives at remote locations and harsh environment. Results of field deployments elucidate the effectiveness of various sensors which measure temperature, humidity, atmospheric pressure, CO2, acoustics, vibrations and the weight of a hive in hostile environment. This work also uses random forest regression to evaluate the feature importance of different sensors, environmental variables such as temperature, humidity, rain, wind speed as well as the information related to seasons, towards estimating the daily hive weight change, on a dataset comprised of 1,250 days of sensor recordings. We also evaluate the protocol designed for communication using Narrow Band Internet of Things (NB-IoT). The issues related to power optimization, sleep intervals and data storage in remote monitoring are also discussed.
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