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.

The purpose of the current study is to investigate the qualitative characterization of nine different pure vegetable oil samples using dielectric spectroscopy which is a vastly resourceful and reasoned technique in the temperature range 0 ℃ to 25 ℃. Time-domain reflectometry technique is applied up to the microwave frequencies of 50 GHz for the first time for qualitative characterization of the selected vegetable oil samples with a special focus on the variances of dielectric properties like dielectric permittivity (ε′), dielectric loss (ε″), relaxation time concerning temperature and other physiochemical properties of the vegetable oil specimens.

The experimental methodology involves the use of time-domain reflectometry (TDR) measurements up to the scale of 50 GHz done to analyse the aspects like lower and higher scales of values towards the static dielectric permittivity (ε_s) and relaxation time (τ) (ps) to further meaningfully compare and correlate this values with the fatty acid profiles of each of the nine vegetable oil samples to reason and draw comparative inferences about the quality aspects of vegetable oils. Microwave TDR studies provide an effective, alternate, simple, rapid, and viable way to exercise quality control and actuate data regarding the quality status of vegetable oils. Variances of dielectric permittivity (ε′) concerning dielectric loss (ε″) are graphically interpreted using the Cole Davidson model. The static dielectric permittivity (ε_s) was further recertified and measured accurately by using a precision LCR meter. Thermodynamic properties of all the nine vegetable oil samples like enthalpy (ΔH) (kJ/mol) and entropy of activation (ΔS) (J/mol ∙ K) are also calculated to further insight the dependence of dielectric properties of these oil samples concerning temperature.

This dielectric spectroscopic study affirms the association of the quality aspects of these nine vegetable oil samples with their dielectric properties by providing meaningful correlations, comparatives and concurrencies of dielectric properties concerning the physiochemical properties which are a part of fatty acid profiles of these samples, which is a novel aspect of this study. The Cole-Cole plot underlines the tendency of realignment of dipoles as per the applied field. The complex permittivity spectra indicate the dwindling nature of molecular alignment including a slow decline to average coinciding values depending on the molecular bonding pattern of vegetable oil samples. The activation energy (ΔH) in (kJ/mol) is calculated for all the samples which are indicative of endothermic nature which experimentally proves that high energy is required for rotation of unsaturated oil sample molecules with low relaxation times.

The highlight of the current dielectric spectroscopic study is that it conclusively divides the nine vegetable oil samples into