查看更多>>摘要：? 2022Nowadays, the management of pressurised irrigation networks requires plenty of information to provide an efficient and reliable service to farmers. Perturbation is the propagation of pressure waves through the networks pipes which could expose the network to a serious risk that could cause components damaging. Several computational codes were developed to simulate such phenomenon. The most recent ones are efficient enough to provide a good image of the perturbation occurrence through different indicators, but they are time and computationally expensive. For real time decision making and more flexible management, there is a need for faster models to be developed. In this study the directly programmed models were used as big data generators to train a developed deep learning model. This approach was applied on a pressurised on-demand irrigation system located in south of Italy that consists of 19 hydrants (service outlets) and covers 57 ha. Two thousand configurations (operational scenarios) were simulated using a predeveloped directly programmed model and fed to train a deep learning model with the objective of forecasting the maximum pressure occurred due the perturbation at each section. The occurred pressure is represented as classes according to the case sensitivity and the required precision. In the present work, scenarios for 1, 2 and 3 bars steps were simulated. The model proved to be significantly time saving compared to previous approaches as the results are produced instantaneously with a forecasting accuracy of 85 %. Furthermore, from the called confusion matrix, the error committed by the model is of one class lower or higher that may be considered tolerable according to the system sensitivity. Thus, modelling the perturbation in the on-demand pressurised irrigation networks would add a significant contribution to provide practical recommendations for real-time decision-making processes.
查看更多>>摘要：? 2022 Elsevier B.V.Precision Agriculture (PA) requires accurate spatial and temporal information of soil properties at a very fine scale. Traditional soil characterization methods are time consuming, laborious and invasive and do not allow long-term repeatability of measurements. Ground Penetrating Radar (GPR) appears to be a particularly suitable methodology for characterizing soil and subsurface from a physical property point of view. Visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance spectroscopy has now become a widespread technique in soil analysis. Information on soil variability can be improved by the integration of data from multiple sensors. The overall objective of this paper was to examine the potential of fusing GPR data with hyperspectral data using multivariate geostatistics for delineating the management zones in the soil of an olive grove of centuries-old trees in Italy. A linear model of coregionalization (LMC) was individually fitted for the raw hyperspectral data and for GPR data including for each case a nugget effect and two spherical models at short scale and at longer scale. After that, one data set was obtained from the fusion of the two sensor data sets and a LMC was fitted for the combined data to be then used in factor cokriging. The application of this technique produced a delineation of the field into homogeneous zones, highlighting a wide southern-central zone, characterized by different granulometric and chemical properties. The proposed approach was then effective to discriminate areas with different properties by using multi-sensor data. It then has the potential to be used in PA.
查看更多>>摘要：? 2022 Elsevier B.V.The air temperature estimates derived from satellite data products would facilitate site-specific prediction of pest outbreaks. Here we propose an approach to estimate air temperature with spatial portability using both land surface temperature (LST) and atmospheric profile (AP) products based on MODIS satellites. In the approach, quantitative and qualitative variables were used as inputs to assess and improve the spatial portability of random forest (RF) models. Daily temperature data at sites in the Korean peninsula were collected from a weather database operated by the Korean Meteorological Administration. Sets of input variables were defined to represent temperature data from the LST (LT) and AP (AT) products, geographical properties (GE), data quality and cloud conditions (QC), land cover type (LC), and auxiliary properties (AX), respectively. The combinations of these sets were used as inputs to the RF models, which were cross-validated using satellite data and weather observation data in South Korea from 2013 to 2019. Concordance Correlation Coefficient (CCC) and Spatial Portability Index (SPI) were determined to compare the accuracy of the RF models across the sites of interest. The RF model that uses AT, LT, and QC variable sets as inputs (RFALQ) tended to have high values of CCC among test sites in South Korea, which ranged from 0.91 to 0.99. RFALQ also had higher values of SPI at sites in North Korea than the other models in which either AT or LT was excluded. Furthermore, the emergence date of Asian Corn Borer (Ostrinia furnacalis) was predicted with high values of CCC (0.94–0.97) across the study sites when RFALQ was used to prepare the input data to an insect phenology model. Our results suggest that both LST and AP products would contribute to higher spatial portability of air temperature estimation. This also hints that the temperature estimation model with high spatial portability would facilitate the pest models, which can be used to reduce the risk of food insecurity in developing countries with sparse weather station networks.
查看更多>>摘要：? 2022 Elsevier B.V.Sensors are vital in controlled environment agriculture for measuring parameters for effective decision-making. Currently, most growers randomly install a limited number of sensors due to economic implications and data management issues. The microclimate within a protected cultivation system is continuously affected by the macroclimate (ambient), which further complicates decision-making around optimal sensor placement. The ambient weather's effect on the indoor microclimate makes it challenging to predict or acquire the ideal condition of the systems through using sensors. This study proposed and implemented a machine learning (K-Means++) algorithm to select optimal sensor locations through clustering. Temperature and relative humidity data were collected from 56 different locations within the greenhouse for over a year covering and these covered four major seasons (spring, summer, autumn, and winter). The data was processed to remove outliers or noise interference using interquartile. The original temperature and relative humidity data were transformed to other air properties (dew point temperature, enthalpy, humid ratio, and specific volume) and used in simulations. The results obtained showed that the number of optimal sensor locations ranged between 3 and 5, and there were similar sensor locations among the air properties. An online machine learning web-based system was developed to systematically determine the optimal number of sensors and location.
查看更多>>摘要：? 2022Piglet suckling behaviour is a critical indicator of piglet liveability and health status; however, automated detection of this important behaviour is rarely reported in literatures. In this study, we proposed a two-step computer vision-based detection method for piglet suckling behaviour. In the first step, an anchor-free deep learning network was employed in instance segmentation of individual sows and piglets. Firstly the localization head detected piglets to obtain the features of the region of interest (ROI). The features extracted from ROI were passed to a novel attention graph convolution-based structure to distil element-wise features. The distilled features were further encoded by a graph convolutional network and were fed into the boundary head and the mask head for piglet contour and mask prediction, respectively. In the second step, the piglets, in adhesion with the sow, were tracked by intersection over union (IOU) which was calculated between adjacent frames. Piglet motion features were derived from the maximum, minimum, variance, and average values in IOU sequence within the 21-frame (3-s) independent processing units. The extracted motion features were input into an SVM, classifying a piglet into suckling or nonsuckling. The dataset, for training and verifying the proposed network respectively, was composed of 100 1-min and 7-fps short video clips as well as one 8-h long video episode, from seven pens of Large White sows and piglets. Our method achieved favourable detection performance with F1 score of 93.6%, Recalls of 92.1%, and Precisions of 95.2% in short video clips, which showed that detecting suckling behaviours for piglets using amodal instance segmentation was feasible. The time budgets of at least one piglet, more than half of the piglets, or all piglets exhibiting suckling behaviour were 74.0 min, 65.6 min, and 1.1 min in an 8-h long video.
查看更多>>摘要：? 2022 Elsevier B.V.In this paper, an integrated numerical model is presented and validated to investigate the particle charging and migration behaviors in a wire-plate electrostatic precipitator (ESP) for the collection efficiency (η) enhancement of the ESP in the piggery. Calculations of the RNG k-ε model, corona model, and particle tracking model are coupled in our model by utilizing MATLAB and COMSOL Multiphysics. This model is used to investigate the influence of various factors, such as the particle diameter (dp), flow velocity (u), and applied voltage (V), on the performance of ESP. Simultaneously, a visual experimental platform for PM2.5 migration is established for verification. The calculated results show that the predicted values of potential distribution, u distribution, and η are in good agreement with the previously reported experimental data. Moreover, the calculated results indicate that two elliptical shapes are produced by a relatively high electric potential and space charge density in the surrounding of the discharge corona wires. Furthermore, η first decreases with the increase of dp, remains unchanged in the range of 0.1–1.0 μm, and then increases until became constant in the range of 1.0–10 μm, revealing the interaction between diffusion charging, electric field charge, and fluid drag. In addition, the experimental and calculation results indicate that the η of PM2.5 increases with the decrease of u and increase of V. Notably, η of 2.0 μm is 100% at u = 1 m/s, V = 50 kV. Therefore, both the migration behavior solution method and the rules of η dependence on PM2.5 provide a scientific parameter control method for improving the air quality in pig house.
查看更多>>摘要：? 2022 The AuthorsQuantifying the conductive heat loss of sows is important in establishing the heat balance equation of sows to evaluate the heat stress because sows spend a significant amount of time lying on the floor. However, previous studies did not offer explicate relationships between the floor heat transfer coefficient (FHTC) and the potential influential factors, making it difficult to estimate the conductive heat loss from sows in commercial productions. This study employed steady-state CFD-CHT simulations to investigate the conductive heat transfer from sows to three typical types of floor system in sow houses, considering the sizes of sows, contact ratios, air distribution strategies, and ambient temperatures. A co-simulation approach was proposed in order to involve the thermoregulation effect of sows under different ambient thermal conditions. The results indicated that the FHTC increased with the air movements and contact ratios, and decreased with the sizes of sows. The proportion of the conductive heat loss decreased with increased ambient temperature and contact ratio. The energy partition suggested that the conductive heat loss accounted for 6–19% of the total heat loss from sows at 20 °C ambient temperature and reduced to 5%-11% at 30 °C ambient temperature. The established relationships between the FHTC and convective heat transfer coefficient, and between the FHTC and interface temperature enabled the calculation of conductive heat loss from sows under various circumstances.
查看更多>>摘要：? 2022 Elsevier B.V.Inconsistencies in seed spacing can occur between rows when a traditional maize precision planter drives along a curve because the inner and outer rows travel faster or slower than the center rows. This results in the wastage of expensive seeds and a decrease in the yield. In this study, a low-cost turn compensation control system (TCCS), based on a dual-radar system, for maize precision planters was developed to address these problems. The system automatically modifies the seeding rates for different rows by individually adjusting the rotary speeds of the drive motors. During the research, a mathematical model for calculating the individual travel speed of each plant row was developed according to rigid body kinematics and the Ackerman steering principle. A simulation model was also built in the MATLAB Simulink software to qualitatively evaluate the primary factors (speed accuracy (SA), turning radius (TR), and turning speed (TS)) that affected the performance of the TCCS. Field trial results showed that the average seeding rate error (AEQ) did not exceed 4.52%, 2.12%, and 2.77% for TRs of 7 m, 14 m, and 28 m, respectively. These values were significantly lower than those at the equivalent TR conditions when the turn compensation function was turned off: 19.41%, 9.04%, and 5.47%, respectively. With the newly developed TCCS, a uniform seed spacing for each plant row was obtained, regardless of the changes in TR and TS. Additionally, the maximum average seed spacing variation coefficient (APREC) did not exceed 19.93%, a value lower than the requirements set by Chinese national standards. In summary, the proposed TCCS reduced maize production costs and improved net profits by ensuring the same seed spacing for all the plant rows. The total cost of the TCCS for a six-row precision planter was only $4,496.82, which is significantly less than that for functionally similar control systems produced by some well-known agricultural machinery manufacturers, making it more suitable for cost-sensitive farmers.
查看更多>>摘要：? 2022 The Author(s)In recent years, data science has evolved significantly. Data analysis and mining processes become routines in all sectors of the economy where datasets are available. Vast data repositories have been collected, curated, stored, and used for extracting knowledge. And this is becoming commonplace. Subsequently, we extract a large amount of knowledge, either directly from the data or through experts in the given domain. The challenge now is how to exploit all this large amount of knowledge that is previously known for efficient decision-making processes. Until recently, much of the knowledge gained through a number of years of research is stored in static knowledge bases or ontologies, while more diverse and dynamic knowledge acquired from data mining studies is not centrally and consistently managed. In this research, we propose a novel model called ontology-based knowledge map to represent and store the results (knowledge) of data mining in crop farming to build, maintain, and enrich the process of knowledge discovery. The proposed model consists of six main sets: concepts, attributes, relations, transformations, instances, and states. This model is dynamic and facilitates the access, updates, and exploitation of the knowledge at any time. This paper also proposes an architecture for handling this knowledge-based model. The system architecture includes knowledge modelling, extraction, assessment, publishing, and exploitation. This system has been implemented and used in agriculture for crop management and monitoring. It is proven to be very effective and promising for its extension to other domains.
查看更多>>摘要：? 2022 Elsevier B.V.Apple tree phenotyping can reflect individual development of single apple tree, which mainly involves tree height, crown width, and diameter of apple tree trunk (DATT). This study aimed to estimate diameter of grafted apple tree trunk, whose target position of diameter estimation is about 10 cm above grafting position. An estimated DATT approach of combining red–greenblue-depth (RGB-D) sensor with SOLOv2 was proposed. Firstly, Kinect V2 was employed to obtain original RGB images and point clouds of the grafted apple trees simultaneously. There were 120 and 60 RGB images and corresponding point clouds randomly collected from two modern apple orchards. Secondly, SOLOv2 deep learning model was selected and trained to instance segment grafting position from RGB image for determining it automatically. Then, corresponding exact position of the grafting position in point cloud was mapped by coordinate transformation of its pixel coordinates, which was obtained by trained SOLOv2 model. Finally, DATT was estimated by calculating the difference between maximum and minimum Y coordinates of points selected by distance thresholds in X, Y, and Z directions near the target position, which were 0.10 m, 0.035 m, and 0.20 m, respectively. Results showed that average precision and average recall of the trained SOLOv2 model for instant segmenting the grafting position were 0.811 and 0.830, respectively. Mean absolute error, mean absolute percentage error, and root mean square error of the proposed method were 3.01 mm, 5.86%, and 3.79 mm, respectively. It illustrates that the proposed method can estimate DATT and thus contribute to automatic apple tree phenotyping.
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