Analyzing Agricultural Economy Data with Computational Intelligence Method

Agricultural big data has become a key resource for the development of modern agriculture, and has occupied an important role in the field of agricultural scientific and technological innovation. With the development of deep learning in recent years, we can effectively improve agricultural production efficiency by existing agricultural data sets to build deep learning algorithm models. There are new ideas to explore the standard architecture of agricultural big data. By formulating standard and standardizing agricultural data process, we established a standard architecture of agricultural big data for deep learning. The result can promote the implementation of deep learning application, and support standardization of technological achievements transformation in agricultural big data.

The low uptake of modern agricultural technologies in sub‐Saharan African countries has encouraged researchers to revisit the returns to (or profitability of) these agricultural inputs. A related strand of literature is exploring the allocative efficiency of these factors of production in African agriculture. However, all these studies rely on self‐reported agricultural data, which are prone to nonclassical measurement errors, the errors in these data are correlated with the true values of variables of interest. In this article we investigate the implication of measurement errors in self‐reported agricultural input and production data on marginal returns to these modern agricultural inputs. We consider a generic two‐sided measurement error problem where both production and inputs can be measured with error, and these errors can be correlated. We employ both self‐reported and objective measures of production and plot size to compute output elasticities under these alternative measurement scenarios. We find that using self‐reported production and plot size overestimates output elasticities and hence marginal returns to modern agricultural inputs (including chemical fertilizer and improved seed). These results are noteworthy in terms of informing conventional technology diffusion strategies as well as in view of revisiting existing presumptions about the profitability of modern agricultural inputs.

With the continuous advancement of digital informationization, the agricultural field has naturally entered the era of big data. At present, big data technology has achieved remarkable results in the Internet industry, but it is still in its infancy in the agricultural field. Due to the complexity of the agricultural field itself and the obvious diversity and heterogeneity of data, the agricultural Internet of Things in the big data environment is facing many difficulties and challenges, especially the collection and storage of massive data. The purpose of this article is to study the application of agricultural big data. This article briefly introduces the background of the development of agricultural big data and the significance of developing agricultural big data, and summarizes the meaning of big data. At the same time, this article summarizes and analyzes the major applications of agricultural big data in production process management, agricultural resource management, and agricultural ecology environmental management, agricultural products and food safety management. In the experimental part, this paper designed and implemented an agricultural big data system platform based on Hadoop. This article simulated the query of 10, 100 and 1000 records respectively for 10,000, 100,000, 1 million and 10 million records, and the average response time was 56ms. The experimental results show that the performance of the agricultural big data platform designed in this paper can meet the needs.

The agricultural products' production link, the production antecedents data is the foundation of the traceability system. In the process of agricultural products' production antecedents acquisition, according to the fact that the single acquisition method, the low collection efficiency, and the antecedents data was easy to tamper. This paper designs an agricultural products' production antecedents acquisition terminal based on Hi3511 and 3G technology. The terminal equipment can be realized agricultural products' production multimedia production antecedents data Acquisition of text, picture and video format. The result of using it in practice indicates that the equipment can meet the requirement of real-time and multi-source production antecedents acquisition.

The past decade has witnessed an increasing interest in the application of Computational Intelligence methods to problems derived from the field of cryptography and cryptanalysis. This phenomenon can be attributed both to the effectiveness of these methods to handle hard problems, and to the major importance of automated techniques in the design and cryptanalysis of cryptosystems. This chapter begins with a brief introduction to cryptography and Computational Intelligence methods. A short survey of the applications of Computational Intelligence to cryptographic problems follows, and our contribution in this field is presented. Specifically, some cryptographic problems are viewed as discrete optimization tasks and Evolutionary Computation methods are utilized to address them. Furthermore, the effectiveness of Artificial Neural Networks to approximate some cryptographic functions is studied. Finally, theoretical issues of Ridge Polynomial Networks and cryptography are presented. The experimental results reported suggest that problem formulation and representation are critical determinants of the performance of Computational Intelligence methods in cryptography. Moreover, since strong cryptosystems should not reveal any patterns of the encrypted messages or their inner structure, it appears that Computational Intelligence methods can constitute a first measure of the cryptosystems’ security.

Agriculture is one of the most important sectors in the world. Agricultural productivity is important for a country’s economy. Big Data technology has been successfully used to solve problems from several sectors such as health, finance, and energy for mention a few. In agriculture, Big data is being used for making better decisions and improving productivity. The increasing interest of Big Data technology in agriculture calls for a clear review. The objective of this review is to collect all relevant research on Big Data technology in agriculture to detect current research topics, benefits of Big Data in Agriculture, Big Data sources, algorithms, approaches, and techniques used. We have extracted 18 primary studies from scientific repositories published between 2017 and 2019. The results show that 67% of the studies are dominated by Indian and China research community. The results also show that half of the studies are focused on crop quality and productivity.

Continued population growth and land intensification put increasing pressure on agricultural production and point to a need for a ‘step change’ in agriculture to meet the demand. Advances in digital technology—often encapsulated in the term ‘big data’—are increasingly assumed to be the way this challenge will be met. For this to be achieved, it is necessary to understand the ways that farmers and other industry stakeholders perceive big data and how big data might change the industry. It is also necessary to address emerging moral and ethical questions about access, cost, scale and support, which will determine whether farms will be able to be ‘big data enabled’. We conducted a discourse analysis of 26 interviews with stakeholders in the grains industry in Australia. Two main discourses were identified: (1) big data as a technology that will significantly benefit a few larger farms or businesses—Big Data is for Big Farming—and conversely (2) big data as a way for every farmer to benefit—Big Data is for Everyone. We relate these findings and the literature on adoption of technology and social studies in agriculture to the potential of farmers to embrace big data, from basic concerns about network infrastructure through to more complex issues of data collection and storage. The study highlights that there are key questions and issues that need to be addressed in further development of digital technology and big data in agriculture, specifically around trust, equity, distribution of benefits and access. This is the first study of big data in agriculture that takes a discourse analysis approach and thus interrogates the status quo and the prevailing norms and values driving decisions with impacts on both farmers and wider society.

In order to improve the effect of precision intelligent agricultural decision support system, this paper combines big data technology to carry out precision mining of agricultural data, and combines decision tree algorithm to carry out data classification processing. Moreover, this paper obtains the most effective agricultural decision reference data through data mining, combines the agricultural decision support process to set the functional modules of the decision system, and analyzes the implementation process of each functional module. In addition, this paper studies the theoretical basis and key technologies of the agricultural production structure optimisation decision support system, and builds a precision and intelligent agricultural decision support system based on big data analysis. The system mainly performs accurate processing of agricultural data and makes effective predictions, and then makes scientific decision results. Finally, this paper verifies the structure of the model in this paper combined with experimental analysis. From the comparison of experiments, it can be seen that the precision and intelligent agricultural decision support system constructed in this paper has significant effects.

Solve the problem of agricultural product big data mining based on e-commerce platform, meet the needs of e-commerce development to agricultural products, meet the diversified needs of e-commerce platforms, and improve people’s living standards and convenience. According to 1000 online questionnaires, 866 people believe that e-commerce can bring them convenience, and 134 people believe that the convenience is insufficient. Even agricultural products, as a traditional primary industry, have begun to be “involved” in the sales mode of e-commerce platforms. In the face of the increasingly huge online consumer demand market, the agricultural product economy has redisplayed a strong market vitality. Of course, the huge market base also makes the e-commerce model of agricultural products pay attention to big data mining and analysis. This paper focuses on how to carry out big data mining and analysis of agricultural products more efficiently from the technical level. Therefore, the agricultural product user data mining technology of e-commerce platform based on Hadoop is proposed. Through the intervention of association rule analysis and algorithm, the improvement of relevant algorithms and agricultural products user behavior analysis system under e-commerce platform based on Hadoop is proposed. The results show that the system can realize the analysis of commodity association degree under various agricultural products user behavior modes and can better help the e-commerce platform of agricultural products realize precision marketing.

The solution of the modal equation of a planar optical waveguide is a cumbersome job and usually incident angle of successful modes is determined by a graphical solution. In this research work, we applied two computational intelligence methods: Particle Swarm Optimization (PSO) and Genetic algorithm (GA) in a segment-wise approach to solving the modal equation of the tangent function. The motivation for employing Computational Intelligence (CI) lies in its ability to optimize functions without requiring high-level mathematics or complex statistical models, as opposed to traditional analytical methods. This strategic use of computational intelligence significantly reduces the overall computational cost, more nature inspired and probabilistic, providing an efficient alternative. Particularly for functions with complex solutions, the utilization of computational intelligence or soft computing methods becomes imperative to obtain an approximate solution compared to classical numerical optimization methods like Newton-Raphson, bisection etc. that generally deterministic and aim to find the exact optimal solution. In terms of using probability (a core component of chosen algorithm’s searching mechanism) we can incorporate distributions that will enhance the performance. Therefore, while classical root-finding methods are computationally simpler for isolated cases, the use of PSO and GA is motivated by their global search capability, robustness to initialization, and ease of automation, which are advantageous in generalized or large-scale modal solution frameworks. The outcomes derived from both methods (PSO and GA) are meticulously compared with the results obtained through the traditional graphical solution. We have found accuracy of 99.95% for PSO and 99.87% for GA. Notably, the findings reveal a close correlation between the computational intelligence approaches and the graphical method offering a promising avenue for advancing the field with a more computationally feasible approach.

This paper designs a cloud platform for agricultural products e-commerce information service based on big data, relying on the Internet of Things, big data, cloud computing and other technologies. The data acquisition layer mainly uses IoT perception to collect data. The system completes data conversion, processing, and analysis in the big data center, and realizes the application through the visual display of the application layer. The development system uses ASP.Net technology and uses C# as the programming language. This article uses Silverer database as the background database to build an agricultural product e-commerce platform. The research shows that this paper applies big data technology to the field of agricultural product information service to realize real-time sharing of agricultural product big data. The system is conducive to the effective and full utilization of agricultural product data resources.

How to design an effective experiment system to teach computational intelligence methods is big challenge nowadays. For the computational intelligence course, we design an aquaponics system and use the project-based teaching method. The aquaponics experiment system includes PLC, LabVIEW and OPC technology. The computational intelligence teaching project has data acquisition, sensors, and model subsystems. This project-based teaching method not only allows students to easily learn the computational intelligence methods and soft measurement, but also enhances their practical skills. Finally, we provide the teaching evaluation of the proposed teaching method.

Big Data, in the era of digital transformation, plays an important role in the global economy, including agriculture. This chapter discusses the applicability of big data in modern farm management and presents some of its key elements. Based on the literature, the advantages and some disadvantages of applying big data in agriculture are listed. The most important benefits were economic benefits such as increased production, cost reduction, increased profitability, and reduced management risk. Ethical, legal, and technical issues were mentioned among the problems related to Big Data. It has been confirmed that Big Data has great potential to transform agricultural production methods and management methods. The use of Big Data in agriculture is a relatively new issue, especially in animal production. In this chapter, it was possible to identify several gaps in knowledge and propose research directions.

During the First World War, farmers in both European and overseas countries benefited from high prices, which persisted for a few years after the war owing to the shortage of food supplies. But the reduction in soil fertility, the loss of livestock and of capital in the countries of continental Europe were gradually made up, and agricultural production recovered. Overseas production had expanded greatly during the war and afterwards continued to rise. In 1921 the increase in food supplies, combined with the effects of general economic depression, caused a sharp fall in prices. In subsequent years prices remained at a low level, and European farmers found themselves in difficulties. In many cases they had incurred debts during the period of prosperity, and repayments now constituted a heavy burden. Further, the costs of agricultural production — wage costs in particular — were rising, and a price “scissors” began to operate to the detriment of agriculture. To increase their revenue many farmers sought to expand their output, but this only aggravated the fall in prices.

The existing quality supervision system of agricultural products is facing challenges such as quality inspection data fraud, data privacy disclosure, and low credibility. In order to solve these problems, this paper designs and implements a Secure Edge-Cloud-BlockChain Integrated Agricultural Product Safety Supervision System based on blockchain and cryptography technology (SECBCAPSS), which enables secure, reliable, and efficient storage of detection data as well as trustworthy traceability. First, quality inspection entities and regulatory entities are deployed as blockchain nodes to achieve transparent supervision and reliable traceability of agricultural product data. Then, a two-stage encryption algorithm is designed and implemented: the SM4 algorithm is used to protect the key data of the inspection record, and the encryption scheme based on the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is used to protect the SM4 key and the detection result certificate. Then, a reliable agricultural product quality detection data storage mechanism is designed to ensure the consistency between cloud storage data and blockchain storage data. The experimental results show that the system function and performance meet the actual needs, can resist malicious data access attacks, and effectively reduce the data storage burden of the blockchain.