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The "Three Rural Issues", encompass challenges related to agriculture, farmer, and rural area, which hold significant importance in driving comprehensive rural revitalization efforts in China. Farmer entrepreneurship, as a crucial means to enhance productivity, create job opportunities, and increase residents' income, has gradually become a key driving force in promoting rural revitalization in the new stage of development in China. With the rapid development of rural e-commerce, farmer entrepreneurship has encountered new opportunities. This study utilizes the 2020 China Family Panel Studies (CFPS) data and employs a structural equation model (SEM) to analyze the direct impact of rural e-commerce participation on farmer entrepreneurial behavior, considering factors such as human capital, social capital, and network infrastructure. This study further explores the indirect effects and mechanisms of e-commerce participation as a mediating variable and analyzes the impact and mechanisms on agricultural entrepreneurship behavior. The findings are as follows: (1) E-commerce participation significantly promotes farmer entrepreneurial behavior; (2) E-commerce participation as a mediating variable has a positive indirect effect on the relationship between social trust, network infrastructure, human capital, and farmer entrepreneurial behavior; (3) E-commerce participation has a significant positive influence on farmer entrepreneurship in the agricultural sector, and farmers with higher levels of network infrastructure and human capital have a higher probability of choosing agricultural entrepreneurship under the influence of e-commerce participation. Finally, this study provides policy recommendations in terms of infrastructure construction, entrepreneurial policy environment, and education level, aiming to optimize the situation of farmer entrepreneurship and contribute to the comprehensive promotion of rural revitalization.Overall, the research in this paper effectively combines theory and empirical evidence to outline the direct and indirect impact mechanisms of rural e-commerce participation on farmers' entrepreneurial behavior and agriculture-related entrepreneurial behavior and to test the effects of their impacts. First, most of the existing literature deals with farmers in individual sample areas, while the sample selected in this paper is farmers in the whole country, which is relatively more generalizable; second, most of the previous studies explore the level of e-commerce in the inter-provincial or county areas, while this paper expands the empirical study of rural e-commerce on the entrepreneurial behavior of farmers and the micro-period of agricultural entrepreneurial behavior, and focuses on the impacts of the e-commerce activities of farmers on their entrepreneurial behavior. [ABSTRACT FROM AUTHOR]

In this paper, we utilize a machine learning model (the convolutional neural network) to analyze aerial images of winter hard red wheat planted areas and cloud coverage over the planted areas as a proxy for future yield forecasts. We trained our model to forecast the futures price 20 days ahead and provide recommendations for either a long or short position on wheat futures. Our method shows that achieving positive alpha within a short time window is possible if the algorithm and data choice are unique. However, the model's performance can deteriorate quickly if the input data become more easily available and/or the trading strategy becomes crowded, as was the case with the aerial imagery we utilized in this paper. [ABSTRACT FROM AUTHOR]

The vegetable sector is a vital pillar of society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. In this paper, we exploit the average daily price data of six distinct types of vegetables sourced from seven key wholesale markets in Beijing, spanning from 2009 to 2023. Upon training an LSTM model, we discovered that it exhibited exceptional performance on the test dataset. Demonstrating robust predictive performance across various vegetable categories, the LSTM model shows commendable generalization abilities. Moreover, LSTM model has a higher accuracy compared to several machine learning methods, including CNN-based time series forecasting approaches. With R2 score of 0.958 and MAE of 0.143, our LSTM model registers an enhancement of over 5% in forecast accuracy relative to conventional machine learning counterparts. Therefore, by predicting vegetable prices for the upcoming week, we envision this LSTM model application in real-world settings to aid growers, consumers, and policymakers in facilitating informed decision-making. The insights derived from this forecasting research could augment market transparency and optimize supply chain management. Furthermore, it contributes to the market stability and the balance of supply and demand, offering a valuable reference for the sustainable development of the vegetable industry. [ABSTRACT FROM AUTHOR]

Sustainable agricultural economic development is the core task for achieving the objective of rural revitalization strategy in China, which cannot be separated from the support and guidance of fiscal policy, and agricultural industry integration is a key path for the fiscal promotion of sustainable agricultural economic development. This paper systematically examines the interaction mechanism between fiscal agricultural expenditures and sustainable agricultural economic development by using 31 provincial panel data in China from 2008 to 2020 and adopting a two-way fixed effect model, a panel quantile model, and a mediating effect model, respectively. The results show that the impact of fiscal agricultural expenditures on sustainable agricultural economic development is significantly positive, and appears a dynamic increasing trend with the agricultural development stage upgrading. Moreover, heterogeneity analysis shows that the effect of fiscal agricultural expenditures is more obvious for the samples in the central region and with a high share of primary industry. Further, a mediating effect test finds that agricultural industry integration plays a mediating mechanism between fiscal agricultural expenditures and sustainable agricultural economic development. Therefore, this paper proposes constructing a long-term investment mechanism for fiscal agricultural expenditures, formulating differentiated fiscal support policies for agriculture, and prioritizing support for agricultural industry integration, which provides theoretical support and policy inspiration for promoting sustainable agricultural economic development. [ABSTRACT FROM AUTHOR]

The stability of agricultural futures market is of great significance to social economy and agri-cultural development. In view of the complexity of the fluctuation of agricultural futures prices, it is challenging to make up for the shortcomings of the existing data preprocessing technology so as to improve the prediction accuracy of the model. This paper puts forward a new VMDSGMD-LSTM model based on improved quadratic decomposition technology and artificial intelligence model. First of all, in the data preprocessing part, VMD is used to decompose the original futures price data, and SGMD is used to further process the remaining components. Secondly, the LSTM model is used to predict a series of modal components, and the final result is obtained by synthesizing the predicted values of different components. Furthermore, based on the futures trading data of wheat, corn and sugar in China agricultural futures market, this paper makes an empirical study in the 1-step, 2-step and 4-step ahead forecasting scenarios, respectively. The results show that compared with other benchmark models, the VMD-SGMD-LSTM hybrid model proposed in this paper has better forecasting ability and robustness for different agricultural futures, which effectively makes up for the shortcomings of existing research. [ABSTRACT FROM AUTHOR]

To facilitate accurate prediction and empirical research on regional agricultural carbon emissions, this paper uses the LLE-PSO-XGBoost carbon emission model, which combines the Local Linear Embedding (LLE), Particle Swarm Algorithm (PSO) and Extreme Gradient Boosting Algorithm (XGBoost), to forecast regional agricultural carbon emissions in Anhui Province under different scenarios. The results show that the regional agricultural carbon emissions in Anhui Province generally show an upward and then downward trend during 2000–2021, and the regional agricultural carbon emissions in Anhui Province in 2030 are expected to fluctuate between 11,342,100 tones and 14,445,700 tones under five different set scenarios. The projections of regional agricultural carbon emissions can play an important role in supporting the development of local regional agriculture, helping to guide the input and policy guidance of local rural low-carbon agriculture and promoting the development of rural areas towards a resource-saving and environment-friendly society. [ABSTRACT FROM AUTHOR]

This article presents the results regarding a systematic literature review procedure on digital twins applied to precision agriculture. In particular, research and development activities aimed at the use of digital twins, in the context of predictive control, with the purpose of improving soil quality. This study was carried out through an exhaustive search of scientific literature on five different databases. A total of 158 articles were extracted as a result of this search. After a first screening process, only 11 articles were considered to be aligned with the current topic. Subsequently, these articles were categorised to extract all relevant information, using the preferred reporting items for systematic reviews and meta-analyses methods. Based on the obtained results, there are two main conclusions to draw: First, when compared with industrial processes, there is only a very slight rising trend regarding the use of digital twins in agriculture. Second, within the time frame in which this work was carried out, it was not possible to find any published paper on the use of digital twins for soil quality improvement within a model predictive control context. [ABSTRACT FROM AUTHOR]

This paper intends to introduce a novel sugarcane plant disease prediction. There are three stages to the projected method, namely: (i) Preprocessing, (ii) Feature extraction, and (iii) Disease detection phase. Initially, the input image is given to the pre-processing stage in which the grey transformation and bilinear interpolation are carried out. The grey transformation is undergone to improve the clarity of the picture and the bilinear interpolation is used as a re-sampling technique. The feature extraction step follows the pre-processed picture, where both texture and colour features are retrieved. More specifically, the texture features like GLCM and suggested Distance-based LBP (D-LBP) features will be extracted. Subsequently, the extracted features are subjected to the disease detection phase process, where a hybrid classifier is introduced. This hybrid classifier blends the concepts of 'Neural Network (NN) and Support Vector Machine (SVM)' for better prediction results. In order to verify the effectiveness of the work that is suggested, a comparison analysis is conducted comparing the suggested and current approaches. The proposed method achieves highest accuracy in 80% of learning which is 13.97%, 16.12%, 11.82%, and 12.9% better than the other methods such as SVM, DT, RF, and NN, respectively. [ABSTRACT FROM AUTHOR]

The new development pattern has identified two key avenues for the sustained advancement of high-quality agricultural and rural development: digitalisation and low-carbon development. The measurement of the digital economy and the agricultural carbon emission performance, and their spatial and temporal heterogeneity, is a crucial step in promoting the spatial coordination and sustainable development of digitalisation and low-carbon agriculture. This paper employs the entropy value method, SBM model, and coupling coordination degree model to investigate the coupling coordination measurement and spatial-temporal heterogeneity of the performance of the digital economy and agricultural carbon emissions. The data used are provincial panel data from 2013 to 2021. The simulation results demonstrate that, between 2013 and 2021, the digital economy of all provinces exhibited varying degrees of growth, yet the development of the digital economy between provinces exhibited a more pronounced tendency to diverge. Concurrently, the agricultural carbon emission efficiency in China exhibited a fluctuating upward trend. The development of the digital economy and the efficiency of agricultural carbon emission were found to be highly coupled. Their coupling and coordination relationship showed a downward trend followed by an upward trend. In general, it is suggested that we should increase investment in digital economy infrastructure and technology, promote digital agricultural applications, strengthen policy guidance and financial support, establish a coupling coordination mechanism and strengthen farmers' digital literacy and environmental awareness. [ABSTRACT FROM AUTHOR]

The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local XGBoost machine learning system. Additionally, it investigates the ERA5 climate model's viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that, by combining both the expert's pre-season forecasts and the ERA5 climate model with the machine learning model, we can—in most cases—obtain better forecasts that outperform the growers' pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all the plots and an RMSE of 0.0872 with the ERA5 climate data included. [ABSTRACT FROM AUTHOR]

Copyright of Vestnik KazUTB is the property of Kazakh University of Technology & Business and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Real-time acquisition of location information for agricultural robotic systems is a prerequisite for achieving high-precision intelligent navigation. This paper proposes a data filtering and combined positioning method, and establishes an active screening model. The dynamic and static positioning drift points of the carrier are eliminated or replaced, reducing the complexity of the original Global Navigation Satellite System (GNSS) output data in the positioning system. Compared with the traditional Kalman filter combined positioning method, the proposed active filtering–Kalman filter algorithm can reduce the maximum distance deviation of the carrier along a straight line from 0.145 m to 0.055 m and along a curve from 0.184 m to 0.0640 m. This study focuses on agricultural robot positioning technology, which has an important influence on the development of smart agriculture. [ABSTRACT FROM AUTHOR]

Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers' experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field. [ABSTRACT FROM AUTHOR]

Given the 'subsistence' character of smallholder production, agricultural production diversification is often seen as an effective strategy for smallholders to improve their diets' diversity and nutritional status, yet the existing evidence remains inadequate. The study applies bibliometric data from the "Web of Science" database to synthesize 46 papers from developing countries to explore the relationship between production diversity, dietary diversity, and nutrition in smallholder households. The study identifies the most influential journals, authors, organizations, and countries and reveals research themes related to agricultural production and food security. This data analysis can help researchers target potential collaborators and access influential literature in agricultural production diversity and dietary diversity research. In addition, the results showed that agricultural production diversity potentially influences households' dietary diversity, with mixed results: Agricultural production diversification is the primary way to improve food and nutritional security among smallholder families with low socio-economic status, inaccessible transportation, and poverty; market access and trade have more potential to improve dietary diversity among smallholder households with well-developed markets and higher income levels; the significant measures of agricultural production diversity include Crop Counts, FGPD, SI, and SWDI; the significant measures of dietary diversity include HDDS and IDDS. This paper provides a roadmap for agricultural production and food security researchers by conducting a systematic review of the literature, summarizing some research methods and perspectives applicable to local socio-economic development. [ABSTRACT FROM AUTHOR]

Analysis and improvement of crop productivity is one of the most important areas in precision agriculture in the world, including Kazakhstan. In the context of Kazakhstan, agriculture plays a pivotal role in the economy and sustenance of its population. Accurate forecasting of agricultural yields, therefore, becomes paramount in ensuring food security, optimizing resource utilization, and planning for adverse climatic conditions. In-depth analysis and high-quality forecasts can be achieved using machine learning tools. This paper embarks on a critical journey to unravel the intricate relationship between weather conditions and agricultural outputs. Utilizing extensive datasets covering a period from 1990 to 2023, the project aims to deploy advanced data analytics and machine learning techniques to enhance the accuracy and predictability of agricultural yield forecasts. At the heart of this endeavor lies the challenge of integrating and analyzing two distinct types of datasets: historical agricultural yield data and detailed daily weather records of North Kazakhstan for 1990-2023. The intricate task involves not only understanding the patterns within each dataset but also deciphering the complex interactions between them. Our primary objective is to develop models that can accurately predict crop yields based on various weather parameters, a crucial aspect for effective agricultural planning and resource allocation. Using the capabilities of statistical and mathematical analysis in machine learning, a Time series analysis of the main weather factors supposedly affecting crop yields was carried out and a correlation matrix between the factors and crops was demonstrated and analyzed. The study evaluated regression metrics such as Root Mean Squared Error (RMSE) and R2 for Random Forest, Decision Tree, Support Vector Machine (SVM) algorithms. The results indicated that Random Forest generally outperformed the Decision Tree and SVM in terms of predictive accuracy for potato yield forecasting in North Kazakhstan Region. Random Forest Regressor showed the best performance with an R2=0.97865. The RMSE values ranged from 0.25 to 0.46, indicating relatively low error rates, and the R2 values were generally positive, indicating a good fit of the model to the data. This paper seeks to address these needs by providing insights and predictive models that can guide farmers, policymakers, and stakeholders in making informed decisions. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the current context, after a pandemic that has left traces in all socio-economic fields and during major geopolitical problems, Basic research is needed in order to identify directions for rapid and sustainable socio-economic recovery. The agricultural sector has played a particularly important role in speeding economic growth and development in less developed countries. As a result, we may argue that the agriculture sector has aided these countries' integration into global trade. The proposed paper aims to identify the degree of dependence of the Moldovan economy on agriculture and how this dependence can be transformed into a challenge that will help the country's economy. Also, the paper aims to identify the link between agricultural production and gross domestic product at the level of the Moldovan economy, as well as the need to create a stability in the labour market in agriculture. The above mentioned will be achieved in the conditions in which there is already a decrease in the labour force, a decrease due to medical, geo-political and migration factors. To develop a sustainable economy, sustainable agricultural public policies must be enacted, resulting in the attraction of personnel from metropolitan regions (where unemployment is higher) to rural areas. [ABSTRACT FROM AUTHOR]

The main objective of the presented paper was the analysis of the current state of food security in the world, segmentation of 100 selected countries and determination of its main driving factors. The analysis used 27 indicators covering 5 basic areas: agricultural production, poverty, demography, economic development, and environmental indicators. The analysis was based on data from the FAO and World Bank for the most recent available period, which was year 2020. The data dimension was reduced with the application of factor analysis, and the main driving factors of food sufficiency were determined. The result was 6 factors: technological development, economic development, agricultural production, environmental factor, and physical quality of life and environment. To group similar countries in terms of selected indicators, a cluster analysis was performed, whereby countries were grouped by similarity into three clusters. The 1st cluster consisted of the most economically developed countries, where only 2.54% of the population suffers from malnutrition. The countries in this cluster were characterized by high levels of economic development, high caloric intake of the population, and high, life expectancy. On the other hand, they recorded negative development in demographic indicators such as fertility and birth rates. The 2nd cluster included the poorest areas of the African continent, which were most endangered by direct food insufficiency (23.74% of the population). In contrast to the first cluster, these countries were characterized by low levels of economic development, high prices, and low-calorie intakes of the population, as well as low life expectancy, while on the other hand, these countries had high fertility and birth rates. The third largest cluster consisted of countries with a medium threat of food insufficiency, where 6.37% of the population suffers from malnutrition. The countries in the third cluster excelled in terms of crop and livestock production volumes, but in contrast to this, they achieved lower levels of fat, protein, and calorie intake of population. [ABSTRACT FROM AUTHOR]

The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying). [ABSTRACT FROM AUTHOR]

The agriculture sector plays a crucial role in the economy of Pakistan, contributing significantly to the Gross Domestic Product (GDP) and the employment rate. However, this sector faces challenges such as climate change, water scarcity, and low productivity, which have a direct impact on agricultural commodity prices. Accurate forecasting of commodity prices is essential for farmers, traders, and policymakers to make informed decisions and improve economic outcomes. This paper explores the use of a big data framework for agricultural commodity price forecasting in Pakistan, using a historical dataset on commodity prices in various Pakistani cities from 2007 to 2022 and Apache Spark to preprocess and clean the data. Based on historical spinach prices in Vehari City, the machine learning models Auto-Regressive Moving Average (ARIMA), Random Forest, and Long-Short-Term Memory (LSTM) were applied to price trends, and their performance was compared using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and squared correlation coefficient (R²). LSTM outperformed ARIMA and Random Forest with a higher R² value of 0.8 and the lowest MAE of 125.29. Such predictions can help farmers to effectively plan crop cultivation and traders to make well-informed decisions. [ABSTRACT FROM AUTHOR]

The state of India was situated on fertile land and river deltas with appropriate agricultural land. In 2019, agricultural fields, primarily cropland, occupy more than 40% of the country's total surface area. Moreover, agricultural industry revenues less than 3% of the country's Gross Provincial Product (GPP). While manufacturing has become the country's major financial activity, accounting countries represent half of GPP's revenues. The objective of the study is to find a way to improve the financial profitability and efficiency of farming supply chain networks as follows: (1) Fixation of national-level targets for zonal-level groups information after assessment and forecasts that affect production and distribution of agriculture. (2) Producers risk can be reduced by directing people to multiple factory and industrialization options based on market assessment. (3) Insurance costs and reduction of bank borrowing by standardizing the connection between bankers and producers using the structure to centralise land information. (4), Stabilize the agricultural sector by looking at nearby potential destinations for production and regulation of the Public Distribution System (PDS) flow for the security stock. In this paper, the novel ML target prediction algorithm to inform the farmers about the market target product and improve the relationships between the farmer and bankers for centralizing the information about recent government plans. The crop prediction ML algorithm proposed to improve the revenue of agriculture field. [ABSTRACT FROM AUTHOR]

This paper examines the effectiveness of grouping agricultural enterprises according to the wheat harvested area and assesses their profitability. We have developed linear and non-linear regression equations to predict the income for said groups of enterprises. The methodology is designed for cases when future market prices are probabilistic in nature. With the help of the developed methodology, it is possible to calculate the necessary production volumes in the conditions of price fluctuations. We have used the Goldfeld–Quandt parametric test to test the model for heteroscedasticity. Calculations show that agricultural holdings are indeed inefficient, and preference should be given to enterprises with medium crop areas. Application of the Lagrange multipliers method when solving the problem of agricultural enterprise optimization makes it possible to increase profitability. The case of price risk, when future market prices are not deterministic, is considered. Therefore, it is necessary to be guided by two criteria when making managerial decisions: to maximize the expected total net income and to minimize the variance of the total net income. [ABSTRACT FROM AUTHOR]

Prefabricated dishes are an effective carrier for the foodization of agricultural products, which is an industry that is gradually moving towards a trillion-level market scale, playing an important role in ensuring food security. The prefabricated dishes industry develops modern agriculture with the concept of industrialization, promotes the further agglomeration and development of upstream and downstream supporting enterprises in the agricultural industry chain, which is the link connecting the primary, secondary and tertiary industries and the endogenous driving force to promote their innovation and development, and is an important bridge connecting the integrated development of multiple industrial chains such as production and sales between rural and urban areas. This paper introduced the development status of the prefabricated dishes industry, analyzed the problems facing by the prefabricated dishes, predicted the future development trend, and made a suggestion for the future development of the prefabricated dishes industry, so as to provide reference for the healthy and sustainable development of the prefabricated dishes industry. [ABSTRACT FROM AUTHOR]

In order to address the significant prediction errors resulting from the substantial fluctuations in agricultural product prices and the non-linear features, this paper proposes a hybrid forecasting model based on variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), and long short-term memory networks (LSTM). This combined model is referred to as the VMD–EEMD–LSTM model. Initially, the original time series of agricultural product prices undergoes decomposition using VMD to obtain a series of variational mode functions (VMFs) and a residual component with higher complexity. Subsequently, the residual component undergoes a secondary decomposition using EEMD. All components are then fed into an LSTM model for training to obtain predictions for each component. Finally, the predictions for each component are linearly combined to generate the ultimate price forecast. To validate the effectiveness of the VMD–EEMD–LSTM model, empirical analyses were conducted for one-step and multi-step forecasts using weekly price data for pork, Chinese chives, shiitake mushrooms, and cauliflower from China's wholesale agricultural markets. The results indicate that the composite model developed in this study provides enhanced forecasting accuracy. [ABSTRACT FROM AUTHOR]

The article describes the current state, retrospective, and perspective of the development of the export potential of the agricultural sector of the Ukrainian economy. The share of agricultural exports in the structure of the total export of goods from Ukraine was analyzed. The geographical structure of the export of goods from Ukraine, in particular the agro-industrial complex, was studied. Special attention was paid to the issue of the change in the structure of the export of goods in 2022. The structure of the export of products from Ukraine, in particular agricultural products, was studied in terms of partner countries. Special attention was focused on considering changes in the structure of exports in 2022 in Ukraine. In 2022, the logistics of exporting agricultural products became significantly more complicated. Neighbouring European countries felt a significant burden during the restructuring of the logistics channels for the export of Ukrainian agricultural products before the opening of the «grain corridor». Customs crossings, railway infrastructure and the infrastructure of sea and river ports of the European Union countries were fully involved. In the article, we substantiated that in order to unblock the export of agricultural products from Ukraine through the domestic Black Sea seaports, the significant burden of building export logistics flows was taken on by European neighboring countries, which fully utilized all their logistics infrastructure. In 2022, there was a reduction in the export of agricultural products, and in 2023, its further decline is predicted. Using the example of grain crops, polynomial approximation was used to forecast their gross harvest, as well as calculate domestic consumption and export potential in 2023. The study indicates that in 2023, export revenue from the sale of grain crops should be expected to be more than twice as much as in 2021. [ABSTRACT FROM AUTHOR]

For market players and policy officials, commodity price forecasts are crucial problems that are challenging to address due to the complexity of price time series. Given its strategic importance, corn crops are hardly an exception. The current paper evaluates the forecasting issue for China's weekly wholesale price index for yellow corn from January 1, 2010 to January 10, 2020. We develop a Gaussian process regression model using cross validation and Bayesian optimizations over various kernels and basis functions that could effectively handle this sophisticated commodity price forecast problem. The model provides precise out-of-sample forecasts from January 4, 2019 to January 10, 2020, with a relative root mean square error, root mean square error, and mean absolute error of 1.245%, 1.605, and 0.936, respectively. The models developed here might be used by market players for market evaluations and decision-making as well as by policymakers for policy creation and execution. [ABSTRACT FROM AUTHOR]

Agricultural carbon emission reduction is inseparable from discussions about government systems, especially in countries with distinctive institutional arrangements such as China. Environmental regulation may influence the impact of environmental decentralisation on agricultural carbon intensity. Using panel data from 30 provinces in mainland China from 2000 to 2015, this paper depicts the spatiotemporal pattern and dynamic evolution of environmental decentralisation, environmental regulation, and agricultural carbon intensity. It uses the spatial Durbin model to empirically study the internal connections between the three. The empirical results show that the enhancement of environmental decentralisation, environmental administrative decentralisation, environmental supervision decentralisation, and environmental monitoring decentralisation is not conducive to reducing agricultural carbon intensity. The results also show that the enhancement of the intensity of environmental regulation of "terminal governance type" and "front-end governance type" is conducive to reducing agricultural carbon intensity, but this effect has a negative "space spillover" phenomenon. Other findings are as follows: (1) the enhancement of the two types of environmental regulation intensity is conducive to changing the impact of environmental decentralisation on agricultural carbon intensity from a "grabbing-hand" to a "helping-hand"; (2) compared with balanced grain-producing and marketing areas and major grain-marketing areas, environmental decentralisation and two types of environmental regulations have a greater negative impact on the agricultural carbon intensity of major grain-producing areas. Therefore, the central government should change how it evaluates political performance; accelerate the construction of a diversified performance evaluation system; increase the weight of low-carbon, green, and other indicators that reflect the friendly development of agriculture in the system; and encourage local governments to adjust the direction of agricultural production. [ABSTRACT FROM AUTHOR]

Access to an agricultural market information system (AMIS) is essential to reduce farmers' risk from market speculation related to information asymmetry. However, AMIS's sustainability depends on the users' needs and willingness to pay (WTP) for market information. This paper explores the WTP for market information among market‐oriented farmers, as well as its determinants. Data were collected through a structured survey with 479 farmers. A contingent dichotomous choice dual boundary estimation is used to measure WTP and a logistic regression is used to ascertain the main determinants of the stated WTP. Experienced losses, access to market information, use of non‐virtual means of information, and degree of farm specialization are the main determinants of the WTP. These findings are useful for establishing a feasible AMIS, and designing its services according to the characteristics, needs, capacities and preferences of the identified potential users. [ABSTRACT FROM AUTHOR]

Scientific measurement of Agricultural Carbon Emissions (ACE) and its formation mechanism is of great significance for the effective formulation of high-quality agricultural development strategies. Therefore, the objectives were to study the characteristics of ACE, its formation mechanism, and its economic relevance in Chongqing by adopting the IPCC and 1997-2019 data, and with LMDI index and Decoupling index. The empirical analysis shows that the total ACE presents an M-shaped trend of "rising-steady fluctuation-falling". Energy structure, energy intensity, and population size have a negative driving effect on ACE, but economic activity is the main factor. The decoupling elasticity characteristics of ACE and economic growth are mainly weak decoupling and strong decoupling, of which the relationship is significantly coordinate. Therefore, to consolidate and enhance the goal of ACE reduction, it is necessary to strengthen the research and development of agricultural low-carbon production technology and promotion. We will continue to deepen supply-side structural reform in agriculture. We will improve the ecological environment in agriculture and rural areas. [ABSTRACT FROM AUTHOR]

Reducing regional inequality is one of the seventeen Sustainable Development Goals (SDGs) established by the United Nations. However, a persistent regional disparity known as the "Mezzogiorno Trap" presents a significant challenge. The underdeveloped regions that fall into the "Mezzogiorno Trap", even though they can narrow the gap with other regions through substantial support, see the disparity widen again when the level of assistance starts to decline. This paper proposes a methodology for identifying the "Mezzogiorno Trap". By employing this approach and combining panel data on Chinese agriculture from 2015 to 2021, it is discovered that despite the overall development of the Chinese agricultural economy during this period, the "Mezzogiorno Trap" still exists. The paper analyzes the reasons behind the "Mezzogiorno Trap" in the Chinese agricultural economy and presents constructive recommendations based on the research findings. The research process demonstrates that this methodology is better suited for studying regional disparities in specific economic sectors, and the obtained results are more stable and reliable. [ABSTRACT FROM AUTHOR]

Different forms of social learning have been explored to act as complements to conventional extension services. This paper examines the possibility of using vocational training to high school students who in turn transfer information to their parents. We conduct a randomized control trial in nine communities in rural Nicaragua and evaluate changes in the knowledge of agricultural technologies, access to credit markets, and technology adoption for parents and students using a difference-in-difference approach. Our results show improvements in knowledge-based outcomes for students and parents, and increased access to credit markets and adoption of agricultural technologies by parents. Given the increase in schooling across developing countries, our results suggest that programs designed around within-family information diffusion can complement more conventional forms of agricultural extension. Little explored channels of information exchange can act as complements or substitutes of conventional extension models, where those are weak or non-existent. This paper is one of the first to explore and evaluate the teacher-student-parent of information exchange as an alternative for agricultural technology diffusion. [ABSTRACT FROM AUTHOR]

The South Asian Association for Regional Cooperation (SAARC) region is an important player in the world agriculture trade. They have vast potential to strengthen their position in global agricultural trade due to theregion's opportunities to increase agricultural production combined with growing global demand. To discover the SAARC potential of agricultural trade patterns, the present paper examines the determinants of bilateral agricultural exports from 2000 to 2019. The gravity model was estimated by employing the Poisson Pseudo Maximum Likelihood (PMML) technique, including zero trade flows for panel data. The results confirm the positive and significant impact of exporter gross domestic product (GDP), importer GDP, Broder, common language, South Asian Free Trade Area (SAFTA), and India-Sri Lanka Free Trade Agreement (ISFTA) on bilateral agricultural trade inthe SAARC region. On the other hand, distance and development levels significantly negatively impact bilateral agricultural trade. Lastly, the study showed an insignificant impact of the bilateral exchange rate. [ABSTRACT FROM AUTHOR]

Plant-soil sensing devices coupled with Artificial Intelligence autonomously collect and process in situ plant phenotypic data. A challenge of this approach is the limited incorporation of phenotype data into decision support systems designed to harness agricultural practices and forecast plant behavior within the intricate context of genotype, environment, and management interactions (G × E × M). To enhance the role of digital phenotyping in supporting Precision Agriculture, this paper proposes a sensing network based on the Internet of Things. The developed system comprises three modules: data collection, communication, and a cloud server. Several processes co-occur in the server, namely data visualization to confirm the correct sensors and data stream functioning. In addition, a crop growth model (CGM) runs on the server, which is powered by the collected data. The simulations generated by the model will support agricultural decisions, obtaining, in advance, insights about plant behavior considering several G × E × M scenarios. To assess the performance of the proposed network to provide reliable data to the model, a greenhouse was equipped with several sensors that collect plant, environment, and soil data (e.g., leaf numbers, air temperature, soil moisture). The proposed network can provide real-time causal support for advanced agricultural practices, evolving from a data-driven approach to an integrative framework where context (G × E × M) drives decision making. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. In this paper, we present a robust framework for the application of Internet of Things (IoT) technology in the agricultural sector in Bangladesh. The framework encompasses the integration of IoT, data mining techniques, and cloud monitoring systems to enhance productivity, improve water management, and provide real-time crop forecasting. We conducted rigorous experimentation on the framework. We achieve an accuracy of 87.38% for the proposed model in predicting data harvest. Our findings highlight the effectiveness and transparency of the framework, underscoring the significant potential of the IoT in transforming agriculture and empowering farmers with data-driven decision-making capabilities. The proposed framework might be very impactful in real-life agriculture, especially for monsoon agriculture-based countries like Bangladesh. [ABSTRACT FROM AUTHOR]

The widening supply-demand gap of poultry products in Nigeria calls for urgent attention toward improving the production and productivity of poultry with a special interest in underutilized poultry species. This study was carried out to examine the level of production of underutilized poultry species and the factors influencing it in the Iwo ADP Zone of Osun State. A multistage sampling procedure was adopted to select 150 poultry farmers for the study. Data were obtained through physical interview of the farmers, then analysed using descriptive statistics and ordinary least square (OLS) regression. Results showed higher percentage of males in poultry production and mean age of the poultry farmers was 49.19±12.40 years with a mean length of experience in poultry keeping of 11.0±7.4 years and mean flock size of 68± 17. It was deduced from the study that majority (77.3%) of the poultry farmers had no or limited access to extension services. As determinants of the rearing of underutilized poultry species in the study area, results of multiple linear regressions showed the coefficients of the age of poultry farmers, and length of poultry farming experience was positive and statistically significant (P< 0.01). Household size was also positive and statistically significant (P< 0.05). Based on the findings, the study, therefore recommends the need for an enlightenment campaign and awareness on the importance of rearing underutilized poultry species and strengthening the livestock extension services for effective and result-oriented service delivery to the poultry farmers. [ABSTRACT FROM AUTHOR]

Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep learning (DL) models, with advanced feature extraction and predictive abilities, offer a promising solution. However, their application to agricultural price data ignored the exogenous factors. Hence, the study explored advanced versions of the well-known univariate models, NBEATSX and TransformerX. The research employed price data for essential crops like Tomato, Onion, and Potato (TOP) from major Indian markets and complemented it with corresponding weather data (precipitation and temperature). To provide a comprehensive analysis, the study also evaluated traditional statistical methods (ARIMAX and MLR) and a suite of ML algorithms (ANN, SVR, RFR, and XGBoost). The performance of these models was rigorously evaluated using error metrics like RMSE, MAE, sMAPE, MASE and QL. The findings were significant indicating DL models, particularly when augmented with exogenous variables, consistently outshone other methods with NBEATSX and TransformerX showing an average RMSE of 110.33 and 135.33, MAE of 60.08 and 74.92, sMAPE of 22.14 and 24.00, MASE of 1.02 and 1.32 and QL of 30.04 and 34.07, respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in agricultural crop price forecasting. This study not only bridged a crucial research gap but also highlighted the robust potential of DL models in enhancing the accuracy of agricultural commodity price predictions in India. [ABSTRACT FROM AUTHOR]

Pollinators are critical for agricultural production and food security, leading to many ongoing surveys of pollinators (especially bees) in crop and adjacent landscapes. These surveys have become increasingly important to better understand the community of potential pollinators, quantify relative insect abundance, and secure crop ecosystem services. However, as some bee populations are declining, there is a need to align and improve bee survey efforts, so that they can best meet research and conservation goals, particularly in light of the logistical and financial constraints of conducting such studies. Here, we mined the existing literature on bee surveys in or around agricultural lands to better understand how sampling methods can be optimized to maximize estimates of 2 key measures of bee communities (abundance and richness). After reviewing 72 papers spanning 20 yr of publication, we found that study duration, number of sites, sampling time, and sampling method most significantly influenced abundance, while the number of trips per year and collection method significantly influenced richness. Our analysis helps to derive thresholds, priorities, and recommendations that can be applied to future studies describing bee communities in agroecosystems. [ABSTRACT FROM AUTHOR]

Navigating the complex landscape of 21st-century agriculture involves overcoming numerous obstacles, such as changing dietary trends, food safety issues, and health concerns due to soil inconsistencies, climatic fluctuations, and varied agricultural practices. The global population surge, climate change, and resource depletion compound these issues. For various stakeholders, including farmers and policymakers, precise crop yield predictions at diverse spatial levels can be immensely advantageous. The value of these forecasts heightens when they are available at multiple spatial resolutions. However, accurately predicting the complex interplay between many data sources and regional yields presents a significant challenge. Conventional methods often fall short, delivering inconsistent results that are difficult to generalize due to their limited ability to incorporate spatial and temporal features, insufficient understanding of market trends, and challenges with scalability and nonlinearity. This paper proposes an innovative Deep Learning approach that adeptly captures and integrates spatial and temporal features, marking a substantial enhancement over traditional methodologies that often grapple with these aspects. This approach forecasts crop yields with a minimal error rate, leveraging the robustness of a unique WaveNet and LSTM hybrid architecture, introducing a fresh perspective to agricultural yield predictions. The novelty of our methodology lies in its two-tier design: the preliminary phase involves pre-processing similar to existing models, and the second phase harnesses the combined power of WaveNet and LSTM for Feature Extraction and regression, enabling precise predictions. The model has been tested on four crops in the Netherlands using varied data splitting criteria, demonstrating stellar performance by offering predictions with minimal error rates. When compared against prevailing machine learning methodologies using an identical Netherlands dataset, our approach outperforms them, highlighting its efficacy and potential practical application in real-world agricultural settings. [ABSTRACT FROM AUTHOR]

Few studies provide direct evidences that agricultural fiscal affects agricultural carbon intensity. This study tries to fill this gap. Using panel data of 30 provinces in China from 2005 to 2019, we conclude that agricultural fiscal expenditures significantly reduce agricultural carbon intensity. The result is still robust after employing the provincial agricultural leaders' birthplace information as an instrumental variable. Further study shows that the negative effect of agricultural fiscal expenditures on agricultural carbon intensity is more pronounced in regions with less corruption and is also more visible in central, western, and inland regions than other areas. For this effect, agricultural technological improvement and structure optimization are possible channels, but not operation scale expansion. Interestingly, although agricultural fiscal expenditures reduce the local agricultural carbon intensity, neighbor regions' carbon intensities are increased due to fiscal rivalry. [ABSTRACT FROM AUTHOR]

Experiments have proven that traditional prediction research methods have limitations in practice. Proposing countermeasures for environmental changes is the key to optimal control of the cold chain environment and reducing the lag of control effects. In this paper, a cold chain transportation environment prediction method, combining k-means++ and a long short-term memory (LSTM) neural network, is proposed according to the characteristics of the cold chain transportation environment of agricultural products. The proposed prediction model can predict the trend of cold chain environment changes in the next ten minutes, which allows cold chain vehicle managers to issue control instructions to the environmental control equipment in advance. The fusion process for temperature and humidity data measured by multiple data sensors is performed with the k-means++ algorithm, and then the fused data are fed into an LSTM neural network for prediction based on time series. The prediction error of the prediction model proposed in this paper is very satisfactory, with a root-mean-square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) and R-squared of 0.5707, 0.2484, 0.3258, 0.0312 and 0.9660, respectively, for temperature prediction, and with an RMSE, MAE, MSE, mean absolute percentage error and R-squared of 1.6015, 1.1770, 2.5648, 0.2736 and 0.9702, respectively, for humidity prediction. Finally, the LSTM neural network and back propagation (BP) neural network are compared in order to enhance the reliability of the results. In terms of the prediction effect of the temperature and humidity in cold chain vehicles transporting agricultural products, the proposed model has a higher prediction accuracy than that of existing models and can provide strategic support for the fine management and regulation of the cold chain transportation environment. [ABSTRACT FROM AUTHOR]

The aim of this paper is to highlight the role of computer based information systems in managing the resources of agricultural companies in order to forecast agricultural production and to study, from several points of view, the impact of climate change, respectively the changes brought by them. The Internet of Things involves the use of the Internet to connect different devices, services, and automated systems, thus forming a network of objects. Internet of Things devices will be integrated, connected to computer based information systems to increase the efficiency of agricultural production, by remotely monitoring sensors that can detect soil moisture and crop growth, ensuring (for example) remote management of smart combine harvesters and equipment of irrigation. The data provided by different sensors will be collected and the operational data obtained will be analyzed. The analysis will be performed using artificial intelligence methods that, through specific algorithms, allow the learning of new processing procedures, using data and previous knowledge collected, correlated with information obtained from meteorological services, to improve the making process. of decisions. Climate change is affecting agriculture by decreasing agricultural areas and reducing crops in the affected areas. These changes ultimately lead to a reduction in the amount of food available to humans and animals, to starvation, a phenomenon that still affects millions of people today. [ABSTRACT FROM AUTHOR]

The key trend for future agricultural growth is efficient, green and sustainable high-quality development, and it is crucial to sort out the factors influencing high-quality agricultural development. Objectives: The purpose of this study is to dissect whether environmental regulation has a catalytic effect on quality rural development, through which paths it is driven and whether there is a threshold effect to be further clarified. Method: In this study, the panel data of 34 provinces in China from 2012 to 2018 are used, and 17 variables are used to construct an evaluation index system, covering four dimensions: agricultural endowment, agricultural output level, agricultural green degree and social sustainability. The high-quality development of agriculture is measured by entropy method. And further, using a baseline regression model and a mediating effects model, we empirically investigated the impact of environmental regulation on high-quality agricultural development and its mechanism of action, and empirically assessed the nonlinear effects of environmental regulation using a threshold regression model. Results: Environmental control, as noted in the study, considerably assists in the establishment of high-quality agriculture; at the same time, large-scale land management plays a role in mitigating the influence of both. There is a single income threshold impact on rural households between high-quality agricultural growth and environmental regulation, and once that threshold is reached, the influence of high-quality agricultural growth grows. Contribution: According to the research findings, recommendations are made for the design of scientific environmental regulation policies, the establishment of a sound service system for large-scale rural land management, and the establishment of a stable mechanism for rural residents to sustain their income, in order to strengthen the effect of environmental regulation and realize high-quality agricultural growth in China. The marginal contribution of this paper is to enrich the study of the relationship between environmental regulation and high-quality agricultural development, which has theoretical and practical implications for promoting sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Precision agriculture in the realm of the Internet of Things is characterized by the collection of data from multiple sensors deployed on the farm. These data present a spatial, temporal, and semantic characterization, which further complicates the performance in the management and implementation of models and repositories. In turn, the lack of standards is reflected in insufficient interoperability between management solutions and other non-native services in the framework. In this paper, an innovative system for spatio-temporal semantic data management is proposed. It includes a data query system that allows farmers and users to solve queries daily, as well as feed decision-making, monitoring, and task automation solutions. In the proposal, a solution is provided to ensure service interoperability and is validated against two European smart farming platforms, namely AFarCloud and DEMETER. For the evaluation and validation of the proposed framework, a neural network is implemented, fed through STSDaMaS for training and validation, to provide accurate forecasts for the harvest and baling of forage legume crops for livestock feeding. As a result of the evaluation for the training and execution of neural networks, high performance on complex spatio-temporal semantic queries is exposed. The paper concludes with a distributed framework for managing complex spatio-temporal semantic data by offering service interoperability through data integration to external agricultural data models. [ABSTRACT FROM AUTHOR]

Despite its immense natural resources, Angola struggles to significantly improve its economy to reduce poverty. Carbon emissions have been increasing over the years, even though the country plans to reduce them by 35% by 2030. This paper attempts to assess the carbon emissions of several sectors (industries, transport, services, and residences) on economic growth, intending to find a balance between environmental protection that requires carbon emissions reduction and economic development that may add to environmental degradation. The study employed time series data on GDP, CO2, CH4, and N2O covering 1971 to 2021 and ARDL and ECM models. This is the first study at the state level in Angola on the relationship between economic development and environmental sustainability considering methane and nitrous oxide emissions. Additionally, the paper assesses the responses of GDP to deviation shock of GDP, CO2, CH4, and N2O by 2032. Phillip Perron and Augmented Dickey-Fuller tests showed that all the data are stationary at the first difference, favoring the application of the ARDL model to explore the short and long-run relationships. The result reveals that methane from agricultural activities and carbon emissions from the building sector and public services contribute to economic growth, whereas carbon emissions from industrial heat systems, non-renewable electricity production, and manufacturing industries harm economic growth. However, no relationship exists between nitrous oxide emissions and economic development. In addition, impulse response function estimates show that appropriate investments can sustain economic development over the years. Therefore, the country should diversify its economy and avoid polluting fuel sources, such as coal. Raising renewable energy's proportion in the total energy mix can support growth while considering the environmental quality. Investments in skills training, academic projects in renewable energy technologies development, agriculture mechanization, and sustainable job creation are recommended. Additionally, investing in quality seeds adapted to climate realities might help lessen climate change's adverse effects and promote growth. Manure manufacturing processes must be improved to reduce agriculture and livestock's methane and nitrous oxide emissions. The country's leaders are encouraged to promote raw material processing industries while insisting on reducing carbon emissions. [ABSTRACT FROM AUTHOR]

Yarn quality prediction is vital in cotton spinning mills for stable production, quality assurance, and cost control. This paper built a machine-learning-based yarn quality prediction method, which contains two core parts: a novel data characterization measurement and a one-dimensional convolutional neural network (YQP-1D-CNN). Firstly, apply a statistical measurement to extract the variable-size feature indicators from the spinning technical parameters, including raw cotton blending scheme, yarn structure parameters, and production process parameters. Then a 1D-CNN prediction model is established based on the prior structural relationships between the feature indicators. Compared with the traditional ANNs, YQP-1D-CNN possesses fewer parameters, requires fewer training samples, and represents better generalization performance. In the experiment, 200 samples are collected from a spinning mill. The prediction performance of the proposed method is compared with six models in existing research. The YQP-1D-CNN shows the highest prediction R2 and the lowest MSE on the overall performance. The better performance of YQP-1D-CNN on poor sample quality is verified better as well. The ablation experiment proves the advantages of the data characterization measurement and the 1D-CNN structure on yarn quality prediction. Finally, the YQP-1D-CNN has been tested in the spinning mill and met practical production requirements. [ABSTRACT FROM AUTHOR]

Utilizing idle space for agricultural planting can generate production profits while greening the urban environment. How can the space available for agricultural planting in cities be inventoried and quantified, and what is the production potential? With the help of GIS, spatial analysis was carried out, and more objective conclusions were drawn through calculation. This paper takes the roof space in Shibei District of Qingdao as an example to study: First, we analyzed the influencing factors and inventory standards of roof agricultural planting; second, we selected inventory standards for idle roof space inventory and conducted statistical analysis of the data obtained from the inventory. Furthermore, combining the annual yield of vegetables per unit area and the annual consumption standards of vegetables by citizens, we calculated the yield and supply and demand; next, we summarized the research results, analyzed their reliability and the advantages and disadvantages of this inventory, and derived formulas for calculating the agricultural production potential of rooftops and urban spaces. Finally, we concluded that the idle roof area in cities and the potential for agricultural production are significant. The research results indicate that the roof area of urban buildings in the Shibei District of Qingdao is 383.3 hectares, with an annual vegetable production potential of 25,800 tons, which can supply the urban population of 184,300. Based on the above research, it is proposed that rooftop agriculture should be given priority in the future development of urban agriculture, and it is recommended that other urban spaces should be inventoried to promote the development of urban agriculture. [ABSTRACT FROM AUTHOR]