Hg2+ removal characteristics of a strain of mercury-tolerant bacteria screened from heavy metal-contaminated soil in a molybdenum-lead mining area.

In this study, the mercury-tolerant strain LTC105 was isolated from a contaminated soil sample collected from a molybdenum-lead mine in Luanchuan County, Henan Province, China. The strain was shown to be highly resistant to mercury, with a minimum inhibitory concentration (MIC) of 32 mg·L-1. After a 24-h incubation in LB medium with 10 mg·L-1 Hg2+, the removal, adsorption, and volatilization rates of Hg2+ were 97.37%, 7.3%, and 90.07%, respectively, indicating that the strain had significant influence on mercury removal. Based on the results of Fourier infrared spectroscopy (FTIR) and scanning electron microscopy (SEM), the investigation revealed that the primary function of LTC105 was to encourage the volatilization of mercury. The LTC105 strain also showed strong tolerance to heavy metals such as Mn2+, Zn2+, and Pb2+. According to the results of the soil incubation test, the total mercury removal rate of the LTC105 inoculation increased by 16.34% when the initial mercury concentration of the soil was 100 mg·L-1 and by 62.28% when the initial mercury concentration of the soil was 50 mg·kg-1. These findings indicate that LTC105 has certain bioremediation ability for Hg-contaminated soil and is a suitable candidate strain for microbial remediation of heavy metal-contaminated soil in mining areas.

Research on SPAD Estimation Model for Spring Wheat Booting Stage Based on Hyperspectral Analysis.

With the rapid progression of agricultural informatization technology, the methodologies of crop monitoring based on spectral technology are constantly upgraded. In order to carry out the efficient, precise and nondestructive detection of relative chlorophyll (SPAD) during the booting stage, we acquired hyperspectral reflectance data about spring wheat vertical distribution and adopted the fractional-order differential to transform the raw spectral data. After that, based on correlation analysis, fractional differential spectra and fractional differential spectral indices with strong correlation with SPAD were screened and fused. Then, the least-squares support vector machine (LSSSVM) and the least-squares support vector machine (SMA-LSSSVM) optimized on the slime mold algorithm were applied to construct the estimation models of SPAD, and the model accuracy was assessed to screen the optimal estimation models. The results showed that the 0.4 order fractional-order differential spectra had the highest correlation with SPAD, which was 9.3% higher than the maximum correlation coefficient of the original spectra; the constructed two-band differential spectral indices were more sensitive to SPAD than the single differential spectra, in which the correlation reached the highest level of 0.724. The SMA-LSSSVM model constructed based on the two-band fractional-order differential spectral indices was better than the single differential spectra and the integration of both, which realized the assessment of wheat SPAD.

A Cascaded Model Based on EfficientDet and YOLACT++ for Instance Segmentation of Cow Collar ID Tag in an Image.

In recent years, many imaging systems have been developed to monitor the physiological and behavioral status of dairy cows. However, most of these systems do not have the ability to identify individual cows because the systems need to cooperate with radio frequency identification (RFID) to collect information about individual animals. The distance at which RFID can identify a target is limited, and matching the identified targets in a scenario of multitarget images is difficult. To solve the above problems, we constructed a cascaded method based on cascaded deep learning models, to detect and segment a cow collar ID tag in an image. First, EfficientDet-D4 was used to detect the ID tag area of the image, and then, YOLACT++ was used to segment the area of the tag to realize the accurate segmentation of the ID tag when the collar area accounts for a small proportion of the image. In total, 938 and 406 images of cows with collar ID tags, which were collected at Coldstream Research Dairy Farm, University of Kentucky, USA, in August 2016, were used to train and test the two models, respectively. The results showed that the average precision of the EfficientDet-D4 model reached 96.5% when the intersection over union (IoU) was set to 0.5, and the average precision of the YOLACT++ model reached 100% when the IoU was set to 0.75. The overall accuracy of the cascaded model was 96.5%, and the processing time of a single frame image was 1.92 s. The performance of the cascaded model proposed in this paper is better than that of the common instance segmentation models, and it is robust to changes in brightness, deformation, and interference around the tag.

Performance Evaluation of a Cicada-Inspired Subsoiling Tool Using DEM Simulations.

Subsoiling practice is an essential tillage practice in modern agriculture. Tillage forces and energy consumption during subsoiling are extremely high, which reduces the economic benefits of subsoiling technology. In this paper, a cicada-inspired biomimetic subsoiling tool (CIST) was designed to reduce the draught force during subsoiling. A soil-tool interaction model was developed using EDEM and validated using lab soil bin tests with sandy loam soil. The validated model was used to optimize the CIST and evaluate its performance by comparing it with a conventional chisel subsoiling tool (CCST) at various working depths (250-350 mm) and speeds (0.5-2.5 ms-1). Results showed that both simulated draught force and soil disturbance behaviors agreed well with those from lab soil bin tests, as indicated by relative errors of <6.1%. Compared with the CCST, the draught forces of the CIST can be reduced by 17.7% at various working depths and speeds; the design of the CIST obviously outperforms some previous biomimetic designs with largest draught force reduction of 7.29-12.8%. Soil surface flatness after subsoiling using the CIST was smoother at various depths than using the CCST. Soil loosening efficiencies of the CIST can be raised by 17.37% at various working speeds. Results from this study implied that the developed cicada-inspired subsoiling tool outperforms the conventional chisel subsoiling tool on aspects of soil disturbance behaviors, draught forces, and soil loosening efficiencies. This study can have implications for designing high-performance subsoiling tools with reduced draught forces and energy requirements, especially for the subsoiling tools working under sandy loam soil.

Improved YOLO-FastestV2 wheat spike detection model based on a multi-stage attention mechanism with a LightFPN detection head.

The number of wheat spikes has an important influence on wheat yield, and the rapid and accurate detection of wheat spike numbers is of great significance for wheat yield estimation and food security. Computer vision and machine learning have been widely studied as potential alternatives to human detection. However, models with high accuracy are computationally intensive and time consuming, and lightweight models tend to have lower precision. To address these concerns, YOLO-FastestV2 was selected as the base model for the comprehensive study and analysis of wheat sheaf detection. In this study, we constructed a wheat target detection dataset comprising 11,451 images and 496,974 bounding boxes. The dataset for this study was constructed based on the Global Wheat Detection Dataset and the Wheat Sheaf Detection Dataset, which was published by PP Flying Paddle. We selected three attention mechanisms, Large Separable Kernel Attention (LSKA), Efficient Channel Attention (ECA), and Efficient Multi-Scale Attention (EMA), to enhance the feature extraction capability of the backbone network and improve the accuracy of the underlying model. First, the attention mechanism was added after the base and output phases of the backbone network. Second, the attention mechanism that further improved the model accuracy after the base and output phases was selected to construct the model with a two-phase added attention mechanism. On the other hand, we constructed SimLightFPN to improve the model accuracy by introducing SimConv to improve the LightFPN module. The results of the study showed that the YOLO-FastestV2-SimLightFPN-ECA-EMA hybrid model, which incorporates the ECA attention mechanism in the base stage and introduces the EMA attention mechanism and the combination of SimLightFPN modules in the output stage, has the best overall performance. The accuracy of the model was P=83.91%, R=78.35%, AP= 81.52%, and F1 = 81.03%, and it ranked first in the GPI (0.84) in the overall evaluation. The research examines the deployment of wheat ear detection and counting models on devices with constrained resources, delivering novel solutions for the evolution of agricultural automation and precision agriculture.

Hydrothermal synthesis and electrochemical properties of Sn-based peanut shell biochar electrode materials.

Using activated-carbon-based electrodes derived from waste biomass in super-capacitor energy technologies is an essential future strategy to achieve sustainable energy and environmental protection. Biomass feed-stocks such as bamboo and straw have been used to prepare activated carbon-based electrodes. This experiment used peanut shells (waste biomass) as carbon precursors. Peanut shell-activated biochar materials were prepared using KOH activation and heat treatment, and SnO2@KBC-CNTs, a composite electrode material of biochar loaded with tin oxide. It was produced through hydrothermal synthesis, utilizing SnCl4-5H2O as the tin precursor. The application of KOH activators during pyrolysis markedly enhanced the porosity and specific surface area of the resultant activated biochar, due to effective dispersion and degradation of pyrolytic products. Characterized by a micro-mesoporous structure, the composite's pores boasted a specific surface area of 158.69 m2 g-1. When tested at a density of current of 0.5 A g-1, the specific capacitance of SnO2@KBC-CNTs reached 198.62 F g-1, nearly doubling the performance of the KBC electrode material alone. Moreover, the composite demonstrated a low ion transfer resistance of 0.71 Ω during charge-discharge cycles.

Modelling and analysis of penetration resistance of probes in cultivated soils.

At present, the measurement of tillage depth is mainly based on manual measurement, but the manual raking method results in low measurement accuracy and high labor intensity. Due to the complexity of soil, theoretical research on tillage depth is relatively scarce. In order to provide a new research direction and research idea for soil stratification, topsoil was taken as the research object of this paper. The correlation between penetration resistance and penetration depth of a probe in a cultivated soil was studied, and a mathematical model was established. There is a certain similarity between the process of spherical cavity expansion and the process of probe penetration, so we introduced the theory of spherical cavity expansion into the modeling of penetration resistance of the cultivated soil. In this paper, the spherical cavity expansion theory of unsaturated soil was used as the basis for solving the penetration resistance. And the unified strength criterion was employed as a yield condition of the soil to set a stress solution and a displacement solutionin into of the probe penetrating into the elasto-plastic zone of the cultivated soil to determine the model of expansion force. We have carried out indoor tests to revise the expansion force model. Firstly, according to the range of soil density and water content in the field, the soil densities were classified into 1.1×103kg/m3, 1.2×103kg/m3 and 1.3×103kg/m3, and the water contents were divided into 10%, 15% and 20%. In addition, the orthogonal tests were performed at different levels. The soil was put into the barrel, and the probe was inserted into the soil in the barrel at the speed of 8mm/s to determine the test values of the change of the probe penetration resistance with depth. Finally, the expansion force model was fitted with the results of the indoor test, and coefficient B was introduced to express the influence degree of density and water content on the resistance. Coefficient B was substituted into the expansion force model to obtain the penetration resistance model of the cultivated soil. Through the goodness of fit analysis of the penetration resistance model, the results show that the overall average goodness of fit of the penetration resistance modelat was up to 0.871 at different water contents and densities, which was a good fit and could present novel insights into the study relating to soil stratification theory.

Isolation and Identification of Mercury-Tolerant Bacteria LBA119 from Molybdenum-Lead Mining Soils and Their Removal of Hg2.

AIMS: To screen heavy metal-tolerant strains from heavy metal-contaminated soil in mining areas and determine the tolerance of the strains to different heavy metals and their removal rates through experiments. METHODS: Mercury-resistant strain LBA119 was isolated from mercury-contaminated soil samples in Luanchuan County, Henan Province, China. The strain was identified by Gram staining, physiological and biochemical tests, and 16S rDNA sequences. The LBA119 strain showed good resistance and removal rates to heavy metals such as Pb2+, Hg2+, Mn2+, Zn2+, and Cd2+ using tolerance tests under optimal growth conditions. The mercury-resistant strain LBA119 was applied to mercury-contaminated soil to determine the ability of the strain to remove mercury from the soil compared to mercury-contaminated soil without bacterial biomass. RESULTS: Mercury-resistant strain LBA119 is a Gram-positive bacterium that appears as a short rod under scanning electron microscopy, with a single bacterium measuring approximately 0.8 × 1.3 µm. The strain was identified as a Bacillus by Gram staining, physiological and biochemical tests, and 16S rDNA sequence analysis. The strain was highly resistant to mercury, with a minimum inhibitory concentration (MIC) of 32 mg/L for mercury. Under a 10 mg/L mercury environment, the optimal inoculation amount, pH, temperature, and salt concentration of the LBA119 strain were 2%, 7, 30 °C, and 20 g/L, respectively. In the 10 mg/L Hg2+ LB medium, the total removal rate, volatilization rate, and adsorption rate at 36 h were 97.32%, 89.08%, and 8.24%, respectively. According to tolerance tests, the strain showed good resistance to Pb2+, Mn2+, Zn2+, Cd2+, and other heavy metals. When the initial mercury concentration was 50 mg/L and 100 mg/L, compared with the mercury-contaminated soil that contained an LB medium without bacterial biomass, LBA119 inoculation increased 15.54-37.67% after 30 days of culture. CONCLUSION: This strain shows high bioremediation potential for mercury-contaminated soil.

Quality Information Detection of Agaricus bisporus Based on a Portable Spectrum Acquisition Device.

As one of the most popular edible fungi in the market, the quality of Agaricus bisporus will determine its sales volume. Therefore, to achieve rapid and nondestructive testing of the quality of Agaricus bisporus, this study first built a portable spectrum acquisition device for Agaricus bisporus. The Ocean Spectromeper was used to calibrate the spectral data of the device, and the linear regression analysis method was combined to analyze the two. The results showed that the Pearson correlation coefficient of significance between the two was 0.98. Then, the spectral data of Agaricus bisporus were collected, the spectral characteristic wavelength of Agaricus bisporus was extracted by the SPA and PCA algorithms, and the moisture content and whiteness prediction models based on a BP neural network and PLSR, respectively, were built. The parameters of the BP neural network model were optimized by SSA. The R2 values for the final moisture content and the predicted whiteness were 0.95 and 0.99, and the RMSE values were 5.04% and 0.60, respectively. The results show that the portable spectral acquisition and analysis device can be used for the accurate and rapid quality detection of Agaricus bisporus.

Parameter calibration of the discrete element simulation model for soaking paddy loam soil based on the slump test.

The discrete element computer simulation method is an effective tool that enables the study of the interaction mechanism between the pulping components and the paddy soil during the paddy field pulping process. The findings are valuable in optimizing the parameters of the paddy beating device to improve its working quality and efficiency. However, the lack of accurate soil models for paddy soil has limited the application and development of the discrete element method in paddy pulping research. This study selected the Hertz-Mindlin with Johnson-Kendall-Roberts discrete element model for the pre-pulping paddy loam soil and used the slump error as the test index to select nine parameters, including soil Poisson's ratio and surface energy, as test factors to calibrate the model parameters. The Plackett-Burman test identified soil shear modulus, surface energy, and soil-iron plate static friction coefficient as significant factors affecting the test index. The steepest ascent test results determined the test range of the above parameters. The Box-Behnken test obtained the regression model between the significant factors and the test index, and the regression model was optimized using the slump error as the target. The optimal combination of parameters was surface energy of 3.257 J/m2, soil shear modulus of 0.709 MPa, and static friction coefficient between soil and iron plate of 0.701. The slump simulation test using this combination of parameters yielded an average slump error of 2.04%. The collective results indicate the accuracy of the calibrated discrete element simulation parameters for paddy loam soil. These parameters can be used for discrete element simulation analysis of the paddy pulping process after paddy field soaking.

Design and test of potato seeding apparatus based on double-layer seed picking scoop structure.

At present, potato seeders in China generally have poor uniformity of seed rows and high coefficients of variation in plant spacing during seed rows, causing difficulties for subsequent mechanized plant protection and harvesting. Based on the effect of seed discharge to analyze the sowing process, a potato seed discharger with a double-layer seed picking spoon structure was designed. By analyzing the seed discharging mechanism and its operation process, the shape and size structural parameters of the seed picking spoon were determined. Finite element simulation of the seed pickup process and seed carrying process of the seed discharging mechanism was carried out by EDEM software to determine the double-layer seed scoop scheme and the range of factors for subsequent tests. A two-factor test was conducted with seeding line speed and seed drop height as test factors, and plant spacing coefficient of variation and seed potato lateral offset dispersion as test indexes. The test results showed that the double-layer seeding spoon chain seeder reduced the coefficient of variation in plant spacing by 5.8%, and the dispersion in lateral offset by 5.5 mm, compared with the single seeding spoon seeder, when the seeding speed was 0.184 m/s and the height of falling seed was 9 cm.

Hydroponic lettuce defective leaves identification based on improved YOLOv5s.

Achieving intelligent detection of defective leaves of hydroponic lettuce after harvesting is of great significance for ensuring the quality and value of hydroponic lettuce. In order to improve the detection accuracy and efficiency of hydroponic lettuce defective leaves, firstly, an image acquisition system is designed and used to complete image acquisition for defective leaves of hydroponic lettuce. Secondly, this study proposed EBG_YOLOv5 model which optimized the YOLOv5 model by integrating the attention mechanism ECA in the backbone and introducing bidirectional feature pyramid and GSConv modules in the neck. Finally, the performance of the improved model was verified by ablation experiments and comparison experiments. The experimental results proved that, the Precision, Recall rate and mAP0.5 of the EBG_YOLOv5 were 0.1%, 2.0% and 2.6% higher than those of YOLOv5s, respectively, while the model size, GFLOPs and Parameters are reduced by 15.3%, 18.9% and 16.3%. Meanwhile, the accuracy and model size of EBG_YOLOv5 were higher and smaller compared with other detection algorithms. This indicates that the EBG_YOLOv5 being applied to hydroponic lettuce defective leaves detection can achieve better performance. It can provide technical support for the subsequent research of lettuce intelligent nondestructive classification equipment.

Improving effect of disaccharides and maltodextrin on preparation of egg yolk powder by microwave-assisted freeze-drying: Functional properties, structural properties, and retention rate of active IgY.

The preparation of egg yolk powder (EYP) with excellent solubility and high retention of active IgY is of great significance for increasing the added value and promoting the application of EYP. A new method of preparing EYP by microwave-assisted freeze-drying (MFD) was researched. Confocal laser scanning microscopy results demonstrated that the supplementation of excipients (sucrose, trehalose, and maltodextrin) could inhibit lipoproteins aggregation in egg yolk induced by freezing. Scanning electron microscopy indicated that drying further damaged the structure of lipoproteins in EYP, leading to lipid separation from it. FTIR and fluorescence spectra confirmed this finding, indicating that excipients enhance protein stability. Compared with conventional freeze-drying (FD), EYP prepared by MFD, particularly that containing excipients, had higher solubility (63 g/100 g), active antibody retention rate and shorter drying time. Therefore, excipients can significantly improve the solubility and stability of EYP and the retention rate of active IgY.

Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network.

To solve the problem of low survival rate caused by unscreened transplanting of seedlings. This study proposed a selective transplanting method of leafy vegetable seedlings based on the ResNet 18 network. Lettuce seedlings were selected as the research object, and a total of 3,388 images were obtained in the dataset. The images were randomly divided into the training set, validation set, and test set in the ratio of 6:2:2. The ResNet 18 network was used to perform transfer learning after tuning, identifying, and classifying leafy vegetable seedlings, and then establishing a model to screen leafy vegetable seedlings. The results showed that the optimal detection accuracy of the presence and health of seedlings in the training data set was above 100%, and the model loss remained at around 0.005. Nine hundred seedlings were selected for the validation test, and the screening accuracy rate was 97.44%, the precision rate of healthy seedlings was 97.56%, the recall rate was 97.34%, the precision rate of unhealthy seedlings was 92%, and the recall rate was 92.62%, which was better than the screening model based on the physical characteristics of seedlings. If they were identified as unhealthy seedlings, the manipulator would remove them during the transplanting process and perform the seedling replenishment operation to increase the survival rate of the transplanted seedlings. Moreover, the seedling image is extracted by background removal technology, so the model processing time for a single image is only 0.0129 s. This research will provide technical support for the selective transplantation of leafy vegetable seedlings.

Calibration and analysis of discrete element simulation parameters of Chinese cabbage seeds.

OBJECTIVE: To improve the accuracy of parameters used in discrete element simulation test of Chinese cabbage seeds harvesting process. METHODS: Firstly, the key physical parameters of Chinese cabbage seeds were measured. According to the results, the discrete element simulation model was established and the value range of simulation test parameters was determined. Then, the actual repose angle of Chinese cabbage seeds was obtained by physical accumulation test using bottomless conical cylinder lifting method. Plackett-Burman test, steepest climb test, Box-Behnken test and parameter optimization test were carried out in sequence with the actual angle of repose as the response value. Finally, the obtained parameters are verified. RESULTS: 1. The Plackett-Burman test showed that the seed-seed rolling friction coefficient, the seed-steel rolling friction coefficient, the seed-seed static friction coefficient, and the seed-steel static friction coefficient had significant effects on the repose angle of Chinese cabbage seeds (P<0.05). 2. The optimization test showed that the seed-seed rolling friction coefficient was 0.08, the seed-steel rolling friction coefficient was 0.109, the seed-seed static friction coefficient was 0.496, and the seed-steel static friction coefficient was 0.415. 3. The validation test showed that the repose angle of Chinese cabbage seeds under such parameter was 23.62°, and the error with the repose angle of the physical test was 0.73%. CONCLUSION: The study show that the discrete element simulation parameters of Chinese cabbage seeds model and calibration are reliable, which can provide reference for the discrete element simulation of Chinese cabbage seeds.

Designing an intelligent monitoring system for corn seeding by machine vision and Genetic Algorithm-optimized Back Propagation algorithm under precision positioning.

OBJECTIVE: To realize the regulation of the position of corn seed planting in precision farming, an intelligent monitoring system is designed for corn seeding based on machine vision and the Genetic Algorithm-optimized Back Propagation (GABP) algorithm. METHODS: Based on the research on precision positioning seeding technology, comprehensive application of sensors, Proportional Integral Derivative (PID) controllers, and other technologies, combined with modern optimization algorithms, the online dynamic calibration controls of line spacing and plant spacing are implemented. Based on the machine vision and GABP algorithm, a test platform for the seeding effect detection system is designed to provide a reference for further precision seeding operations. GA can obtain better initial network weights and thresholds and find the optimal individual through selection, crossover, and mutation operations; that is, the optimal initial weight of the Back Propagation (BP) neural network. Field experiments verify the seeding performance of the precision corn planter and the accuracy of the seeding monitoring system. RESULTS: 1. The deviation between the average value of the six precision positioning seeding experiments of corn under the random disturbance signal and the ideal value of the distance is less than or equal to 0.5 cm; the deviation between the average value of the six precision positioning seeding experiments of corn under the sine wave disturbance signal (1 Hz) is less than or equal to 0.4 cm; the qualified rate of grain distance reaches 100%. 2. The precision control index, replay index, and missed index of the designed corn precision seeding intelligent control system have all reached the national standard. During the operation of the seeder, an alarm of the seeder leaking occurred, and the buzzer sounded and the screen displayed 100 times each; therefore, the reliability of the alarm system is 100%. CONCLUSION: The intelligent corn seeder designed based on precision positioning seeding technology can reduce the seeding rate of the seeder and ensure the stability of the seed spacing effectively. Based on the machine vision and GABP algorithm, the seeding effect detection system can provide a reference for the further realization of precision seeding operations.

A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision.

Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method.

IoT-based measurement system for classifying cow behavior from tri-axial accelerometer / Sistema de medição baseado em IoT para classificar o comportamento de vacas através de acelerômetro tri-axial

ABSTRACT: A cow behavior monitoring system based on the Internet of Things (IoT) has been designed and implemented using tri-axial accelerometer, MSP430 microcontroller, wireless radio frequency (RF) module, and a laptop. The implemented system measured cow movement behavior and transmitted acceleration data to the laptop through the wireless RF module. Results were displayed on the laptop in a 2D graph, through which behavior patterns of cows were predicted. The measured data from the system were analyzed using the Multi-Back Propagation-Adaptive Boosting algorithm to determine the specific behavioral state of cows. The developed system can be used to increase classification performance of cow behavior by detecting acceleration data. Accuracy exceeded 90% for all the classified behavior categories, and the specificity of normal walking reached 96.98%. The sensitivity was good for all behavior patterns except standing up and lying down, with a maximum of 87.23% for standing. Overall, the IoT-based measurement system provides accurate and remote measurement of cow behavior, and the ensemble classification algorithm can effectively recognize various behavior patterns in dairy cows. Future research will improve the classification algorithm parameters and increase the number of enrolled cows. Once the functionality and reliability of the system have been confirmed on a large scale, commercialization may become possible.

RESUMO: Um sistema de monitoramento de comportamento de vacas baseado na Internet das Coisas (IoT) foi projetado e implementado através do uso de acelerômetro tri-axial, Microcontrolador MSP430, módulo de rádio, frequência sem fio (RF), e um portátil. O sistema implementado mediu o comportamento do movimento da vaca e transmitiu dados de aceleração ao portátil através do módulo RF sem fio. Os resultados foram exibidos no portátil em um gráfico 2D, através do qual os padrões de comportamento das vacas foram previstos. Os dados medidos do sistema foram analisados usando o Multi-retropropagação-Adaptativa algoritmo de Boosting para determinar o estado comportamental específico das vacas. O sistema desenvolvido pode ser usado para aumentar a classificação de desempenho de vaca comportamento através da detecção de aceleração de dados. A precisão excedeu 90% de todas as categorias de classificação de comportamento e a especificidade do andar normal atingiu 96.98%. A sensibilidade foi boa para todos os padrões de comportamento, exceto em pé e deitada, com um máximo de 87.23% para ficar de pé. No geral, o sistema baseado em IoT fornece medição precisa e remota do comportamento da vaca, e o algoritmo de conjunto de classificação pode efetivamente reconhecer vários padrões de comportamento em vacas leiteiras. Pesquisas futuras irão melhorar os parâmetros do algoritmo de classificação e aumentar a quantidade de vacas matriculadas. Uma vez que a funcionalidade e confiabilidade do sistema foram confirmadas em larga escala, a comercialização pode se tornar possível.