Predicting the excretion of feces, urine and nitrogen using support vector regression: A case study with Holstein dry cows

Fu, Qiang; Shen, Weizheng; Wei, Xiaoli; Yin, Yanling; Zheng, Ping; Zhang, Yonggen; Su, Zhongbin; Zhao, Chunjiang

doi:10.25165/j.ijabe.20201302.4781

Cited by 4 publications

(3 citation statements)

References 35 publications

(44 reference statements)

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“…In recent years, machine learning has become a high-quality solution to various regression problems [22][23][24][25][26] . In this study, principal component analysis and random forest algorithms are employed for current fuel consumption prediction.…”

Section: Fuel Consumption Forecastmentioning

confidence: 99%

Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone

Yang¹,

Tian²,

Zhai³

et al. 2022

International Journal of Agricultural and Biological Engineering

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In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach, this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a smartphone. First, three driving modes were developed for maize sowing scenarios: manual driving assisted driving and unmanned driving. While sowing, smartphone software and CAN (Controller Area Network) storage devices collected both positional data and engine operating conditions.Second, the tractor trajectory points were divided into kinematic sequences, with six driving cycle indicators built in each series based on the time window.Based on the semantic information of the kinematic sequences, the three operations of sowing, seeds filling, and turning round were well recognized.Last, a model for maize sowing fuel consumption forecast was advanced using the principal component analyses and random forest algorithm, regarding three factors: driving cycles, operating behaviors, and driving patterns.When compared to the traditional K-means algorithm, the results demonstrated that the harmonic mean of the precision and recall (F1 score) of sowing behavior recognition, seeds filling behavior recognition, and turning behavior recognition were enhanced by 2.06%, 8.99%, and 21.79%, respectively.In terms of the impacts of driving modes and operating behaviors on fuel consumption, assisted driving mode had the lowest fuel usage for both sowing and turning behavior. Therefore, assisted driving is the most fuel-efficient mode for maize sowing.Combining the three driving modes, the relative error of the fuel consumption prediction model was 0.11 L/h, with the manual driving mode having the lowest relative error at 0.09 L/h. This research method lays the foundation for the optimization of tractor operation behavior, the selection of tractor driving mode, and the fine management of tractor fuel consumption.

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Section: Fuel Consumption Forecastmentioning

confidence: 99%

Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone

Yang¹,

Tian²,

Zhai³

et al. 2022

International Journal of Agricultural and Biological Engineering

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“…For example, a song can be classified as soothing or dynamic according to the rhythm, classical or country or other according to the content, or sad or positive according to the lyrics. Text classification has a wide range of uses today, and the emergence of digital libraries and various search tools has greatly reduced people's workload [7].…”

Section: Current Status Of Researchmentioning

confidence: 99%

A Practical Exploration of Constructive English Learning Platform Informatization Based on RBF Algorithm

Tang

Tsai

2021

Mathematical Problems in Engineering

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The constructive English learning approach provides better technical support and learning concepts for English learners. This paper firstly introduces the theoretical basis of constructive English learning and the status of domestic and international research and analyzes the advantages over the traditional English learning approach. This paper also analyzes the RBF neural network model, especially the locally adjusted RBF algorithm, and considers the training model and construction method of the RBF neural network to simulate the learning pattern of good learners of English in grades 4 and 6 to better realize the effect of the English learning platform and allow the English learning platform to play a supportive role in the learning process of students, and through the evaluation of the design, we can better improve the software design, which can be improved by evaluating the design. In the subsequent research, we should add a more user-friendly interface design, use larger model samples to train the model to make it more universal in accuracy and ensure good stability in operation, and integrate multiple algorithms to improve the training algorithm of RBF neural network to obtain a more general algorithm model. The rich teaching methods can cultivate students’ independent inquiry ability, and the construction of this English learning platform can help improve students’ ability to use information technology. By making students become the main subject of teaching and learning, we can stimulate students’ enthusiasm and a good awareness of English learning. Using the radial basis function (RBF) algorithm and the constructive pod language teaching method, this English learning platform is a good and positive aid for students to learn English, and as a better English education tool, it has certain research value and positive significance for the study of English teaching.

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“…It is estimated that the total amount of livestock and poultry feces produced in China is approximately several billion tons each year. Arbitrary discharging or stacking is still the main treatment method, which has an impact on the atmosphere, water sources, soil, etc., and livestock and poultry feces have become one of the important sources of pollution in the ecological environment [1,2] . Moreover, the mass application of chemical fertilizer affects the quality of agricultural products in traditional farming processes and is prone to a series of problems, such as soil compaction and soil erosion.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the nonmarket value of livestock and poultry feces returning to farmland utilization using CVM in Heilongjiang, China

Shang¹,

Xie²,

Guo³

et al. 2023

International Journal of Agricultural and Biological Engineering

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Aiming at a series of hot issues and topics, such as resource waste, environmental pollution, and Mollisol protection, related to the arbitrary discharge of livestock and poultry feces, livestock and poultry feces are taken as the research object, a questionnaire survey was conducted to investigate residents' cognition and willingness to pay (WTP) for the nonmarket value of livestock and poultry feces returning to farmland in Heilongjiang Province, China. The contingent valuation method (CVM) was used for the correlation analysis of WTP and socioeconomic characteristics, and correlation analysis of payment mode and socioeconomic characteristics. Meanwhile, the factors influencing WTP and payment mode were analyzed. The results show that the majority of the respondents are aware of the harmfulness of the arbitrary discharge of livestock and poultry feces and the importance of its nonmarket regeneration value. Approximately 90.09% of respondents were willing to pay for the return of livestock and poultry feces to fields. In 2020, the total value of WTP of livestock and poultry feces returning to farmland in Heilongjiang Province was 4.65 billion CNY, equivalent to 1.32% of the total agricultural production of Heilongjiang Province in the same year. And the nonmarket value of livestock and poultry feces is estimated to be 1456.69 CNY/t. The annual average WTP of rural and urban households is 286.42 CNY and 422.90 CNY per household, accounting for 0.46% and 0.52% of the average annual income of households, respectively. Therefore, the return of livestock and poultry feces to farmland utilization has a high nonmarket value. The analysis shows that the average annual household income is a significant factor in WTP, and the registered permanent residence type and the average annual household income are significant factors in payment mode. The research results should provide a scientific and theoretical basis for the government's policy and decision-making on the return of livestock and poultry feces to farmland.

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Predicting the excretion of feces, urine and nitrogen using support vector regression: A case study with Holstein dry cows

Cited by 4 publications

References 35 publications

Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone

Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone

A Practical Exploration of Constructive English Learning Platform Informatization Based on RBF Algorithm

Evaluation of the nonmarket value of livestock and poultry feces returning to farmland utilization using CVM in Heilongjiang, China

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