Information Processing in Agriculture最新文献

The lack of information creates problems for Colombian small-scale farmers, as it impedes them from selling at fair prices and knowing efficient production techniques. Around the world, many technological interventions have proven helpful in reducing information asymmetries. Therefore, we proposed a technological scheme based on a genetic algorithm and a natural language processor (NLP) that enables producers to obtain knowledge through information processing. Also, we ran fieldwork in twenty municipalities and a survey among 500 Colombian cocoa small-scale farmers in different regions in Colombia. This fieldwork helps us determine small-scale farmers' necessities, market conditions, and the relevance of an Artificial Intelligence (AI) tool. The results have shown that AI methodologies could improve the economic conditions of small farmers by providing access to information on prices, weather, and production techniques. The fieldwork evidence that a technological tool is a good option only if there are dynamic trade cycles. AI tools could transmit and process information to become producers' knowledge and help them evolve into collective strategies. The methodology, which combines genetic algorithms, NLP, and fieldwork for cocoa farming, is a novelty that contributes to information asymmetry reduction. We contributed to the literature about adopting AI tools to develop cocoa small-scale farming better.

Detection and counting of abalones is one of key technologies of abalones breeding density estimation. The abalones in the breeding stage are small in size, densely distributed, and occluded between individuals, so the existing object detection algorithms have low precision for detecting the abalones in the breeding stage. To solve this problem, a detection and counting method of juvenile abalones based on improved SSD network is proposed in this research. The innovation points of this method are: Firstly, the multi-layer feature dynamic fusion method is proposed to obtain more color and texture information and improve detection precision of juvenile abalones with small size; secondly, the multi-scale attention feature extraction method is proposed to highlight shape and edge feature information of juvenile abalones and increase detection precision of juvenile abalones with dense distribution and individual coverage; finally, the loss feedback training method is used to increase the diversity of data and the pixels of juvenile abalones in the images to get the even higher detection precision of juvenile abalones with small size. The experimental results show that the [email protected] value, [email protected] value and [email protected] value of the detection results of the proposed method are 91.14%, 89.90% and 80.14%, respectively. The precision and recall rates of the counting results are 99.59% and 97.74%, respectively, which are superior to the counting results of SSD, FSSD, MutualGuide, EfficientDet and VarifocalNet models. The proposed method can provide support for real-time monitoring of aquaculture density for juvenile abalones.

Collections of biological specimens are essential in entomology laboratories for scientific knowledge and the characterization of natural varieties. It is vital to liberate useful information from physical collections by digitizing specimens, allowing them to be shared, examined, annotated, and compared more readily. As a result, current research has concentrated on developing 3D modeling machine systems to digitize insect specimens. Despite many great outcomes, these systems have certain drawbacks. In this research, a new scanning machine is proposed for creating 3D virtual models of insects. Our method has overcome certain previous constraints by aiding in the automation of the entire imaging process at a low cost, lowering shooting time, and generating 3D models with accurate color, high resolution, and high accuracy of insect samples with small sizes and complicated structures. Because of its ease of installation and modification, our system may be expanded and utilized in a variety of settings and areas.

Rural energy plays an important role in realizing the goals of “carbon peak” and “carbon neutrality” in China. In this paper, the countryside was regarded as the research object, and the rural energy internet was constructed to study the impact of rural energy development on rural carbon emissions. The most advanced energy and informative technologies in the development of rural energy were introduced from three perspectives, including rural living, rural planting and rural breeding. The benefits of rural energy internet in practical application, including energy and carbon benefits, were presented through three application cases. In general, a low-carbon, digital and intelligent rural energy will be developed, and the goals of “carbon peak” and “carbon neutrality” will be achieved by constructing and applying of rural energy internet in China.

With the vigorous development of intelligence agriculture, the progress of automated large-scale and intensive pig farming has accelerated significantly. As a biological feature, the pig face has important research significance for precise breeding of pigs and traceability of health. In the management of live pigs, many managers adopt traditional methods, including color marking and RFID identification, but there will be problems such as off-label, mixed-label and waste of manpower. This work proposes a non-invasive way to study the identification of multiple individuals in pigs. The model was to first replace the original backbone network of YOLOv4 with MobileNet-v3, a popular lightweight network. Then depth-wise separable convolution was adopted in YOLOv4′s feature extraction network SPP and PANet to further reduce network parameters. Moreover, CBAM attention mechanism formed by the concatenation of CAM and SAM was added to PANet to ensure the network accuracy while reducing the model weight. The introduction of multi-attention mechanism selectively strengthened key areas of pig face and filtered out weak correlation features, so as to improve the overall model effect. Finally, an improved MobileNetv3-YOLOv4-PACNet (M-YOLOv4-C) network model was proposed to identify individual sows. The mAP were 98.15 %, the detection speed FPS were 106.3frames/s, and the model parameter size was only 44.74 MB, which can be well implanted into the small-volume pig house management sensors and applied to the pig management system in a lightweight, fast and accurate manner. This model will provide model support for subsequent pig behavior recognition and posture analysis.

UAVs (Unmanned Aerial Vehicles) have become increasingly popular in the agricultural sector, promoting and enabling the application of aerial image monitoring in both the scientific and business contexts. Images captured by UAVs are fundamental for precision farming practices. They enable us do a better crop planning, input estimates, early identification and correction of sowing failures, more efficient irrigation systems, among other tasks. Since all these activities deal with low or medium altitude images, automated identification of crop lines plays a crucial role improving these tasks. We address the problem of detecting and segmenting crop lines. We use a Convolutional Neural Network to segment the images, labeling their regions in crop lines or unplanted soil. We also evaluated three traditional semantic networks: U-Net, LinkNet, and PSPNet. We compared each network in four segmentation datasets provided by an expert. We also assessed whether the network’s output requires a post-processing step to improve the segmentation. Results demonstrate the efficiency and feasibility of these networks in the proposed task.

Plant growth monitoring techniques are of great interest to agricultural engineering. The interaction between root and soil water is one important plant response to environmental variations. This paper aims to develop a new method to estimate plant biological response using root-soil water interaction. It provides a case study on moisture transfer at the boundary area of a soil water retention zone (SWRZ). We produced a SWRZ around growing roots of a cultivated tomato plant in homogenous dried soil using water-saving drip irrigation. The irrigation was designed to supply moisture only in the root zone to meet the minimum need of plant growth. High-resolution soil moisture sensors were used to detect moisture transfer at the boundary area of the SWRZ. We applied frequency analysis to the acquired vibration spectrum using filtering and Fast Fourier Transform (FFT) in order to investigate the frequency content at each sensor location. Distinct frequencies of moisture vibration were identified at the boundary area of the SWRZ which indicated water transfer to the roots caused by plant water absorption. A mechanical vibration model was proposed to describe this phenomenon. The pinpoint irrigation to the root zone in the water-saving cultivation method enabled a well-structured spherical root system to form via hydrotropism. This enabled a simple method to analyze moisture transfer based on a mechanical vibration model. The results suggest a new method to estimate plant biological response by studying root-soil water interaction.

The importance of Model Predictive Control (MPC) has significant applications in the agricultural industry, more specifically for greenhouse’s control tasks. However, the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the system. Subspace methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed data. In this paper, we introduce an application of Constrained Model Predictive Control (CMPC) for a greenhouse temperature and relative humidity. For this purpose, two Multi Input Single Output (MISO) systems, using Numerical Subspace State Space System Identification (N4SID) algorithm, are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation actions. In this sense, linear state space models were adopted in order to evaluate the robustness of the control strategy. Once the system is identified, the MPC technique is applied for the temperature and the humidity regulation. Simulation results show that the regulation of the temperature and the relative humidity under constraints was guaranteed, both parameters respect the ranges 15 °C ≤ T_int ≤ 30 °C and 50 % ≤ H_int ≤ 70 % respectively. On the other hand, the control signals u_f and u_h applied to the fan and the heater, respect the hard constraints notion, the control signals for the fan and the heater did not exceed 0 ≤ u_f ≤ 4.3 Volts and 0 ≤ u_h ≤ 5 Volts, respectively, which proves the effectiveness of the MPC and the tracking tasks. Moreover, we show that with the proposed technique, using a new optimization toolbox, the computational complexity has been significantly reduced. The greenhouse in question is devoted to Schefflera Arboricola cultivation.

Tiger puffer is a commercially important fish cultured in high-density environments, and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding. However, the detection precision and recall of farmed tiger puffer are low due to target blurring and occlusion in real farming environments. The farmed tiger puffer detection model, called knowledge aggregation YOLO (KAYOLO), fuses prior knowledge with improved YOLOv5 and was proposed to solve this problem. To alleviate feature loss caused by target blurring, we drew on the human practice of using prior knowledge for reasoning when recognizing blurred targets and used prior knowledge to strengthen the tiger puffer's features and improve detection precision. To address missed detection caused by mutual occlusion in high-density farming environments, a prediction box aggregation method, aggregating prediction boxes of the same object, was proposed to reduce the influence among different objects to improve detection recall. To validate the effectiveness of the proposed methods, ablation experiments, model performance experiments, and model robustness experiments were designed. The experimental results showed that KAYOLO's detection precision and recall results reached 94.92% and 92.21%, respectively. The two indices were improved by 1.29% and 1.35%, respectively, compared to those of YOLOv5. Compared with the recent state-of-the-art underwater object detection models, such as SWIPENet, RoIMix, FERNet, and SK-YOLOv5, KAYOLO achieved 2.09%, 1.63%, 1.13% and 0.85% higher precision and 1.2%, 0.18%, 1.74% and 0.39% higher recall, respectively. Experiments were conducted on different datasets to verify the model's robustness, and the precision and recall of KAYOLO were improved by approximately 1.3% compared to those of YOLOv5. The study showed that KAYOLO effectively enhanced farmed tiger puffer detection by reducing blurring and occlusion effects. Additionally, the model had a strong generalization ability on different datasets, indicating that the model can be adapted to different environments, and it has strong robustness.