scholarly journals Applying Precision Agriculture to Artificial Waterfowl Hatching, Using the Black Muscovy Duck as an Example

2021 ◽  
Vol 11 (20) ◽  
pp. 9763
Author(s):  
Shun-Chieh Chang ◽  
Chih-Hsiang Cheng ◽  
Yen-Hung Chen
Keyword(s):  
Prediction Model ◽  
Precision Agriculture ◽  
Production Efficiency ◽  
Quantitative Description ◽  
Hatching Rate ◽  
Muscovy Duck ◽  
Conventional Agriculture ◽  
Training Costs ◽  
Environmental Controls ◽  
Universal Prediction

agriculture practices adopt homogenization-farming processes to enhance product characteristics, with lower costs, standardization, mass production, and production efficiency. (2) Problem: conventional agriculture practices eliminate products when these products are slightly different from the expected status in each phase of the lifecycle due to the changing natural environment and climate. However, this elimination of products can be avoided when they receive customized care to the expected developing path via a universal prediction model, for the quantitative description of biomass changing with time and the environment, and the corresponding automatic environmental controls. (3) Methods: in this study, we built a prediction model to quantitatively predict the hatching rate of each egg by observing the biomass development path along the waterfowl-like production lifecycle and the corresponding environment settings. (4) Results: two experiments using black Muscovy duck hatching as a case study were executed. The first experiment involved finding out the key characteristics, out of 25 characteristics, and building a prediction model to quantitatively predict the survivability of the black Muscovy duck egg. The second experiment was adopted to validate the effectiveness of our prediction mode; the hatching rate rose from 47% in the first experiment to 62% in the second experiment without any human interference from experienced farmers. (5) Contributions: this research builds on an AI-based precision agriculture system prototype as the reference for waterfowl research. The results show that our proposed model is capable of decreasing the training costs and enhancing the product qualification rate for individual agricultural products.

scholarly journals Economic, Agronomic, and Environmental Benefits From the Adoption of Precision Agriculture Technologies

2019 ◽  
Vol 10 (1) ◽  
pp. 40-56 ◽  
Author(s):  
Thomas Koutsos ◽  
Georgios Menexes
Keyword(s):  
Precision Agriculture ◽  
Production Efficiency ◽  
Farming System ◽  
Environmental Benefits ◽  
Production Costs ◽  
Modern Agriculture ◽  
Technology Innovations ◽  
Farm Production ◽  
Farming Efficiency ◽  
Lower Production

Precision agriculture (PA) as an integrated information- and production-based farming system is designed to delivery high-end technology solutions to increase farm production efficiency and profitability while minimizing environmental impacts on the ecosystems and the environment. PA technologies are technology innovations that incorporate recent advances in modern agriculture providing evidence for lower production costs, increased farming efficiency and reduced impacts. However, the adoption of the precision agriculture technologies has encountered difficulties such as additional application or management costs and investment on new equipment and trained employees. Some of these PA technologies were proven efficient, providing tangible benefits with lower costs and as a result they quickly gained scientific interest. To investigate further the economic, agronomic, and environmental benefits from the adoption of PA technologies a systematic review was conducted, based on the systematic search and evaluation of related eligible articles.

scholarly journals CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1058 ◽  
Author(s):  
Yang-Yang Zheng ◽  
Jian-Lei Kong ◽  
Xue-Bo Jin ◽  
Xiao-Yi Wang ◽  
Min Zuo
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Production Efficiency ◽  
State Of The Art ◽  
Detection Accuracy ◽  
Similar Species ◽  
Learning Technology ◽  
Well Performance ◽  
Species Classification ◽  
Domain Specific

Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks.

scholarly journals SIGNIFICANCE OF COOPERATION FOR THE DEVELOPMENT OF ORGANIC AGRICULTURE

2020 ◽  
Vol 1 (2) ◽  
pp. 27-32
Author(s):  
Olha Alieksieieva
Keyword(s):  
Organic Farming ◽  
Organic Agriculture ◽  
Production Efficiency ◽  
Negative Impact ◽  
The United States ◽  
Organic Production ◽  
Conventional Agriculture ◽  
Cooperative Movement ◽  
Analysis And Synthesis ◽  
The Impact

The purpose of the article is to study the impact of the basic principles and benefits of the development of cooperative movement on the development of organic agricultural production, as well as to determine the role of cooperation in the development of organic entrepreneurship. Methodology. The author has used the methods of analysis and synthesis, the method of statistical grouping and comparison, induction and deduction, structural and functional approach to solve the problems and achieve the goals. The study is based on the comparative analysis and synthesis of scientific literature. Results. Organic food is increasing in popularity. The growing demand is mainly attributable to consumer concerns about negative implications of conventional agriculture for human health and the environment. Organic agriculture has a great potential to neutralize a negative impact of agricultural intensification on the environment. The article describes the concept and analyzes the current state of cooperation in agriculture. It is found that during the period under the research, the number of registered cooperatives has increased by almost 45%. The concepts of organic agriculture and organic production are clarified. The dynamics of agricultural lands engaged in organic production and the number of certified organic farms in Ukraine are studied. It is indicated that the consumption of organic products in Ukraine is much lower than in the European countries and the United States. The advantages of introducing a cooperative movement in the field of organic agriculture are identified. Being part of a cooperative helps small farms to be able to produce enough to generate profits and ensure their substance, which is an opportunity to compete with larger companies. Risks arising in the process of establishing organic cooperatives are outlined considering the international experience. It is concluded that efficient organic farming cooperatives can support local and export markets, stimulate production efficiency and promote local logistics. Practical implications. The advantages of consolidation of enterprises and entrepreneurs in the field of organic agriculture on the basis of cooperation can be used for the development of state programs to support the cooperative movement in organic farming. Value/originality. Views on the organization of production, processing and logistics activities of organic agricultural producers through the introduction of cooperatives have been expanded.

scholarly journals Optimum size in grid soil sampling for variable rate application in site-specific management

2011 ◽  
Vol 68 (3) ◽  
pp. 386-392 ◽  
Author(s):  
Marcos Rafael Nanni ◽  
Fabrício Pinheiro Povh ◽  
José Alexandre Melo Demattê ◽  
Roney Berti de Oliveira ◽  
Marcelo Luiz Chicati ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Precision Agriculture ◽  
Production Efficiency ◽  
Sampling Interval ◽  
Soil Samples ◽  
Soil Sampling ◽  
Kappa Statistics ◽  
Increase Production Efficiency ◽  
Site Specific ◽  
Specific Management

The importance of understanding spatial variability of soils is connected to crop management planning. This understanding makes it possible to treat soil not as a uniform, but a variable entity, and it enables site-specific management to increase production efficiency, which is the target of precision agriculture. Questions remain as the optimum soil sampling interval needed to make site-specific fertilizer recommendations in Brazil. The objectives of this study were: i) to evaluate the spatial variability of the main attributes that influence fertilization recommendations, using georeferenced soil samples arranged in grid patterns of different resolutions; ii) to compare the spatial maps generated with those obtained with the standard sampling of 1 sample ha-1, in order to verify the appropriateness of the spatial resolution. The attributes evaluated were phosphorus (P), potassium (K), organic matter (OM), base saturation (V%) and clay. Soil samples were collected in a 100 × 100 m georeferenced grid. Thinning was performed in order to create a grid with one sample every 2.07, 2.88, 3.75 and 7.20 ha. Geostatistical techniques, such as semivariogram and interpolation using kriging, were used to analyze the attributes at the different grid resolutions. This analysis was performed with the Vesper software package. The maps created by this method were compared using the kappa statistics. Additionally, correlation graphs were drawn by plotting the observed values against the estimated values using cross-validation. P, K and V%, a finer sampling resolution than the one using 1 sample ha-1 is required, while for OM and clay coarser resolutions of one sample every two and three hectares, respectively, may be acceptable.

Estimation of 24 hour urine protein excretion using urine creatinine/albumin ratio

2020 ◽  
Author(s):  
Xin Liu ◽  
Yonghong Zhao ◽  
Daqing Hong ◽  
Yunlin Feng
Keyword(s):  
General Population ◽  
Prediction Model ◽  
Quantitative Description ◽  
High Accuracy ◽  
Urine Protein ◽  
Albumin Ratio ◽  
Urine Protein Excretion ◽  
Protein Excretion ◽  
Urine Creatinine ◽  
Accurate Quantification

Abstract BackgroundThere is still a lack of quantitative description of the relationship between urine creatinine/albumin ratio (ACR) and 24 hour urine protein excretion (24 h UPE). We aimed to study the correlation between 24 h UPE and urine ACR and develop a prediction model for 24 h UPE.Methods This was a retrospectively observational study. All individuals with paired urine ACR and 24 h UPE tested on the same day in Sichuan Provincial People’s Hospital during September 1st, 2018 to December 31st, 2019 were enrolled. Correlation and agreement between urine ACR and 24 h UPE were evaluated. A prediction model of 24 h UPE was further developed and validated.Results 671 subjects were identified. Urine ACR had a good correlation with 24 h UPE in general population (Spearman’s coefficient = 0.939; p < 0.001) but the agreement between these two measurements was not consistently good (overall ICC = 0.870; 95% CI: 0.849–0.888; p < 0.001). Our multivariable transform model of 24 h UPE had good performance (R2 = 0.829) and validated high accuracy (RMSE = 0.0227, rRSME = 3.1%).Conclusions Urine ACR has a good correlation with 24 h UPE in general population but is not a reliable surrogate for 24 h UPE. Our prediction model is a useful tool for estimating 24 h UPE, however, 24 h UPE is still mandatory in situations when accurate quantification of proteinuria is required.Key messages:1. urine ACR has a good correlation with 24h UPE but is not a reliable surrogate for 24h UPE.2. Our prediction model is helpful to estimate 24h UPE, with good performance (R2=0.829) and validated high accuracy (RMSE=0.0227, rRSME=3.1%); however, 24h UPE is still mandatory in situations when accurate quantification of proteinuria is required.

Intelligent Prediction of Process Parameters for Cold Bending

2010 ◽  
Vol 154-155 ◽  
pp. 74-78
Author(s):  
Sheng Le Ren ◽  
Yi Nan Lai ◽  
Guang Fei Wu ◽  
Jun Tao Gu ◽  
Zeng Lou Li
Keyword(s):  
Prediction Model ◽  
Process Parameters ◽  
Production Efficiency ◽  
Bending Moment ◽  
Production Costs ◽  
Main Process ◽  
Tube Bending ◽  
Forming Process ◽  
Training Samples ◽  
Set Up

The choice of the process parameters in the conventional tube bending forming is often based on experience. The method of constantly testing to adjust has seriously affected the production efficiency and increased production costs. In this paper, an intelligent prediction model of the tube bending forming process parameters for utility boiler was set up based on neural network, which has been used to predict the main process parameters including the bending moment and boost power. In the intelligent prediction model, the analytical calculations, numerical simulation and experimental data are selected as the source of training samples. The test results show that the average relative error between the simulation output and target output of bending moment and boost power is less than 2%, and the predicted process parameters, i.e. bending moment and boost power, can be directly used for actual production.

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