Information Processing in Agriculture最新文献

This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology, inspired by the needs of problem solving for biological, agricultural, aquacultural and environmental systems. The challenging practical problem is to develop a framework for automatic generation of causally right and balance-based, unified models that can also be applied for the effective coupling amongst the various (sophisticated field-specific, sensor data processing-based, upper level optimization-driven, etc.) models. The scientific problem addressed in this innovation is to develop Programmable Process Structures (PPS) by combining functional basis of systems theory, structural approach of net theory and computational principles of agent based modeling. PPS offers a novel framework for the automatic generation of easily extensible and connectible, unified models for the underlying complex systems. PPS models can be generated from one state and one transition meta-prototypes and from the transition oriented description of process structure. The models consist of unified state and transition elements. The local program containing prototype elements, derived also from the meta-prototypes, are responsible for the case-specific calculations. The integrity and consistency of PPS architecture are based on the meta-prototypes, prepared to distinguish between the conservation-laws-based measures and the signals. The simulation is based on data flows amongst the state and transition elements, as well as on the unification based data transfer between these elements and their calculating prototypes. This architecture and its AI language-based (Prolog) implementation support the integration of various field- and task-specific models, conveniently. The better understanding is helped by a simple example. The capabilities of the recently consolidated general methodology are discussed on the basis of some preliminary applications, focusing on the recently studied agricultural and aquacultural cases.

The precision livestock farming (PLF) has the objective to maximize each animal's performance while reducing the environmental impact and maintaining the quality and safety of meat production. Among the PLF techniques, the personalised management of each individual animal based on sensors systems, represents a viable option. It is worth noting that the implementation of an effective PLF approach can be still expensive, especially for small and medium-sized farms; for this reason, to guarantee the sustainability of a customized livestock management system and encourage its use, plug and play and cost-effective systems are needed. Within this context, we present a novel low-cost method for identifying beef cattle and recognizing their basic activities by a single surveillance camera. By leveraging the current state-of-the-art methods for real-time object detection, (i.e., YOLOv3) cattle's face areas, we propose a novel mechanism able to detect the ear tag as well as the water ingestion state when the cattle is close to the drinker. The cow IDs are read by an Optical Character Recognition (OCR) algorithm for which, an ad hoc error correction algorithm is here presented to avoid numbers misreading and correctly match the IDs to only actually present IDs. Thanks to the detection of the tag position, the OCR algorithm can be applied only to a specific region of interest reducing the computational cost and the time needed. Activity times for the areas are outputted as cattle activity recognition results. Evaluation results demonstrate the effectiveness of our proposed method, showing a [email protected] of 89%.

As the new generation of artificial intelligence (AI) continues to evolve, weather big data and statistical machine learning (SML) technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery weather. Accurate fishery weather services play a crucial role in fishery production, serving as a great safeguard for economic benefits and personal safety, enabling fishermen to carry out fishery production better, and contributing to the sustainable development of the fishery industry. The objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery weather. Specifically, we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather, which mainly include three aspects: fishery weather scenario generation, fishery weather forecasting, and fishery extreme weather warning. We also illustrate the main technical means and principles of SML technology. Finally, we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.

Some of the problems attributed to traditional laboratory analyses that limit the correct assessment of the nutrient contents in the soil are time requirements and high cost of the soil nutrient determinations. To solve these problems, a study was carried out to evaluate the use of visible, near-infrared, and short-wave infrared (Vis-NIR-SWIR) spectroscopy in the prediction of soil available ions submitted to the application of rock powders. The study was carried out on an Arenosol in Paranavaí City/Brazil. Treatments (rock powders) were arranged within a split-plot system designed in randomized blocks with four repetitions. Sugarcane was cultivated for 14 months after the application of rock powders. Later, 96 soil samples were collected for measuring the pH and available ions P, K⁺, Ca²⁺, Mg²⁺, S-SO₄^2-, Si, Cu²⁺, Fe²⁺, Mn²⁺, and Zn²⁺ as well as spectral reading through a Vis-NIR-SWIR spectroradiometer to predict the soil chemical attributes through the partial least square regression (PLS) technique. The results showed that the elements K⁺, Ca²⁺, Mg²⁺, Cu²⁺, and Fe²⁺ could be predicted with a reasonable rightness degree (R²_p > 0.50, RPD_p > 1.40) from spectral models. However, for the attributes pH, P, S-SO₄^2-, Si, Mn²⁺, and Zn²⁺, there were no satisfactory models (R²_p < 0.50, RPD_p < 1.40). Thus, the application of rock powder changed the spectral curves and, because of that, allows the building of PLS models to predict the elements K⁺, Ca²⁺, Mg²⁺, Cu²⁺, and Fe²⁺. Therefore, Vis-NIR-SWIR spectroscopy is a promising alternative to the routine analyses of soil fertility since it has advantages such as fast analytical speed, low cost, easy to operate, non-destructive, and environmentally friendly, because it does not use harmful chemicals.

Compared with manual sorting of citrus fruit, vision-based sorting solutions can help achieve higher accuracy and efficiency. In this study, we present a vision system based on CNN-LSTM, which can cooperate with robotic grippers for real-time sorting and is readily applicable to various citrus processing plants. A CNN-based detector was adopted to detect the defective oranges in view and temporarily classify them into corresponding types, and an LSTM-based predictor was used to predict the position of the oranges in a future frame based on image sequential data. The fusion of CNN and LSTM networks enabled the system to track defective ones during rotation and identify their true types, and their future path was also predicted which is vital for predictive control of visually guided robotic grasping. High detection accuracy of 94.1% was obtained based on experimental results, and the error for path prediction was within 4.33 pixels 40 frames later. The average time to process a frame was between 28 and 62 frames per second, which also satisfied real-time performance. The results proved the potential of the proposed system for automated citrus sorting with good precision and efficiency, and it can be readily extended to other fruit crops featuring high versatility.

Preconsolidation pressure (${\sigma }_P$) of soil can be considered as an indicator of the Load Bearing Capacity (LBC), which is the tolerated surface pressure before compaction, often caused by the traffic of agricultural machinery. In this pioneering study, a remote sensing approach was introduced to estimate LBC through ${\sigma }_P$ from soils of the “Rio Preto” Hydrographic Basin, Bahia State, Brazil, in a monthly time lapse from 2016 to 2019. Traditionally, ${\sigma }_P$ is measured by a laborious and time demanding laboratory analysis, making it unfeasible to map large areas. The innovative methodology of this work consists of combining active–passive satellite data on soil moisture and pedotransfer functions of clay content and water matric potential to obtain geo-located estimates of ${\sigma }_P$. Estimates were analysed under different classes of soil use, land cover and slope; 95% confidence intervals were built for the time series of mean values of LBC for each class. The overall seasonal variation in LBC estimates is similar in areas with annual crops, grasslands and natural vegetation, and flat areas are less affected by soil moisture variations over the year (between seasons). LBC decreased, in general, at about 0.5% a year in flat areas. Therefore, these areas demand attention, since they occupy 86% of the Basin and are mostly subjected to agricultural soil management and surface pressure by heavy machinery.

The droplet deposition is a key index to evaluate the quality of unmanned aerial vehicle (UAV) spraying. The detection of the droplet deposition is time-consuming and costly, therefore, it is difficult to achieve large-scale and rapid acquisition in the field. To solve the above problems, a droplet deposition acquisition system (DDAS) was developed. It was composed of the multiple sensors, processing units, remote server database and Android-based software. A droplet deposition prediction model based on field experimental data was established by using a one-dimensional convolutional neural network (1D-CNN) algorithm, and the effects of different inputs on the prediction ability of the model were analyzed. The results showed that adding temperature and humidity data to the inputs can achieve higher prediction accuracy than only using UAV spraying operation parameters and wind speed data as the inputs to the model. In addition, the prediction accuracy of the 1D-CNN model was the highest when compared with other models such as back propagation neural network, multiple correlation vector machine, and multiple linear regression. The 1D-CNN model was embedded into the DDAS, and the evaluation experiments were carried out in the field. The correlation analysis was conducted between two datasets of the droplet deposition obtained by the DDAS and water sensitive paper (WSP), respectively. The R² was 0.924, and the RMSE was 0.026 μL/cm². It is proved that the droplet deposition values obtained by the DDAS and WSP have high consistency, and the DDAS developed can provide an auxiliary solution for the intelligent evaluation of UAV spraying quality.

The advent of remote livestock monitoring systems provides numerous possibilities for improving on-farm productivity, efficiency, and welfare. One potential application for these systems is for the detection of calving events. This study describes the integration of data from multiple sensor sources, including accelerometers, global navigation satellite systems (GNSS), an accelerometer-derived rumination algorithm, a walk-over-weigh unit, and a weather station for parturition detection using a support vector machine approach. The best performing model utilised data from GNSS, the ruminating algorithm, and weather stations to achieve 98.6% accuracy, with 88.5% sensitivity and 100% specificity. The top-ranking features of this model were primarily GNSS derived. This study provides an overview as to how various sensor systems could be integrated on-farm to maximise calving detection for improved production and welfare outcomes.