Agricultural Information Technology Research Articles

Information and communication technology in smart agriculture: A scientometric review

SummaryAgriculture has been affected by several global trends raising the concern for food security. Agri‐food demands are amplifying due to the ever‐escalating population numbers. Thus, the notion of extending the use of smart innovative technology in managing agricultural practices has emerged rapidly over the last decade. Technological innovations have contributed significantly to shape modern agriculture as smart agriculture. Smart agriculture unfolds various benefits such as increased production, real time data and production insights and remote monitoring. The rising advancements in Information and Communication Technologies (ICT) have paved the way for researchers to use these technologies in managing agricultural practices, leading to greater benefits for farmers and society. These innovations are the key to establishing agriculture as a research discipline. The purpose of this article is to conduct a scientometric analysis to study the structure and evolution of research activities in the field of smart agriculture. The scientometric analysis aims to empirically map the scientific knowledge and identify any possible challenges in the field. This study performs elementary analysis to study publication growth over the years, impact analysis to assess the leading journals, authors, and countries, and articles analysis for findings patterns among the citations over the years and among the keyword‐based clusters. There has been a considerable increase of more than 200% in the number of publications from 2011 to 2022. However, around 60% of authors have contributed with a single publication. The findings of the study reveal the research trends and hot topics for future research fronts. Deep learning, digital agriculture, object detection, blockchain, and semantic segmentation have been identified as trending topics in smart agriculture. Comprehensively, an intellectual view of the agriculture domain is presented as a scientific field in this article.

A Redactable Blockchain-Based Data Management Scheme for Agricultural Product Traceability.

With the development of agricultural information technology, the Internet of Things and blockchain have become important in the traceability of agricultural products. Sensors collect real-time data in agricultural production and a blockchain provides a secure and transparent storage medium for these data, which improves the transparency and credibility of agricultural product traceability. However, existing agricultural product traceability solutions are limited by the immutability of the blockchain, making it difficult to delete erroneous data and modify the scope of data sharing. This damages the credibility of traceability data and is not conducive to the exchange and sharing of information among enterprises. In this article, we propose an agricultural product traceability data management scheme based on a redactable blockchain. This scheme allows agricultural enterprises to encrypt data to protect privacy. In order to facilitate the maintenance and sharing of data, we introduce a chameleon hash function to provide data modification capabilities. Enterprises can fix erroneous data and update the access permissions of the data. To improve the efficiency of block editing, our scheme adopts a distributed block editing method. This method supports threshold editing operations, avoiding single-point-of-failure issues. We save records of data modifications on the blockchain and establish accountability mechanisms to identify malicious entities. Finally, in this paper we provide a security analysis of our proposed solution and verify its effectiveness through experiments. Compared with the existing scheme, the block generating speed is improved by 42% and the block editing speed is improved by 29.3% at 125 nodes.

Investigating the impact of agricultural informatization on the carbon shadow price

The mitigation of climate change requires that agricultural development would proceed in accordance with green and sustainable practices. This implies that economic performance should be improved while minimizing the impact on the environment. One way to assess the underlying costs of sustainable agricultural development, and to model the relationship between the environment and the economy in general, is to use the carbon shadow price (CSP). In this paper, we utilize a by-production model within a non-parametric framework to estimate the agricultural CSP from 1997 to 2020 in 31 provinces of China. The patterns of the agricultural CSP are unveiled through kernel density analysis and convergence tests. Additionally, this paper constructs a comprehensive agricultural informatization indicator using the entropy method. We then empirically investigate its influence on agricultural CSP. The results reveal that China's agricultural CSP has demonstrated consistent growth over the past two decades which suggests serious improvement in environmental performance. Furthermore, the CSP show cross-province variation, albeit with conditional β convergence. Our empirical findings indicate that agricultural information technology contributes to an increase in CSP, thereby indicating its positive role on the green growth of agriculture in China.

Evaluating the Efficacy of Segment Anything Model for Delineating Agriculture and Urban Green Spaces in Multiresolution Aerial and Spaceborne Remote Sensing Images

Segmentation of Agricultural Remote Sensing Images (ARSIs) stands as a pivotal component within the intelligent development path of agricultural information technology. Similarly, quick and effective delineation of urban green spaces (UGSs) in high-resolution images is also increasingly needed as input in various urban simulation models. Numerous segmentation algorithms exist for ARSIs and UGSs; however, a model with exceptional generalization capabilities and accuracy remains elusive. Notably, the newly released Segment Anything Model (SAM) by META AI is gaining significant recognition in various domains for segmenting conventional images, yielding commendable results. Nevertheless, SAM’s application in ARSI and UGS segmentation has been relatively limited. ARSIs and UGSs exhibit distinct image characteristics, such as prominent boundaries, larger frame sizes, and extensive data types and volumes. Presently, there is a dearth of research on how SAM can effectively handle various ARSI and UGS image types and deliver superior segmentation outcomes. Thus, as a novel attempt in this paper, we aim to evaluate SAM’s compatibility with a wide array of ARSI and UGS image types. The data acquisition platform comprises both aerial and spaceborne sensors, and the study sites encompass most regions of the United States, with images of varying resolutions and frame sizes. It is noteworthy that the segmentation effect of SAM is significantly influenced by the content of the image, as well as the stability and accuracy across images of different resolutions and sizes. However, in general, our findings indicate that resolution has a minimal impact on the effectiveness of conditional SAM-based segmentation, maintaining an overall segmentation accuracy above 90%. In contrast, the unsupervised segmentation approach, SAM, exhibits performance issues, with around 55% of images (3 m and coarser resolutions) experiencing lower accuracy on low-resolution images. Whereas frame size exerts a more substantial influence, as the image size increases, the accuracy of unsupervised segmentation methods decreases extremely fast, and conditional segmentation methods also show some degree of degradation. Additionally, SAM’s segmentation efficacy diminishes considerably in the case of images featuring unclear edges and minimal color distinctions. Consequently, we propose enhancing SAM’s capabilities by augmenting the training dataset and fine-tuning hyperparameters to align with the demands of ARSI and UGS image segmentation. Leveraging the multispectral nature and extensive data volumes of remote sensing images, the secondary development of SAM can harness its formidable segmentation potential to elevate the overall standard of ARSI and UGS image segmentation.

ОСОБЛИВОСТІ ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНИХ ТЕХНОЛОГІЙ СІЛЬСЬКОГОСПОДАРСЬКИХ ПІДПРИЄМСТВ КИТАЮ

The article analyses the peculiarities of information and communication technologies of agricultural enterprises in China. The relevance of the work is due to the increasing digitalisation of agriculture at the global level, as well as the digitalisation of the Chinese economy. In addition, the role of modern technologies is becoming the key to the development of agriculture in general. The study is based on the works of Ukrainian and Chinese scholars on the selected topic. The purpose of the study is to analyse the current features of the use of information and communication technologies in the agricultural sector on the example of Chinese agricultural enterprises. The article analyses the main features of the use of information and communication technologies in agriculture. The peculiarities of digital and electronic agriculture are identified. The features of the introduction of information and communication technologies in agriculture in China are determined. The historical patterns of the use of modern technologies in agriculture of the country are determined. The main directions and manifestations of the use of information and communication technologies by agricultural enterprises in China are considered. Special attention is paid to the importance of social networks for Chinese agricultural enterprises. The main advantages of the use of information and communication technologies by Chinese agricultural enterprises are also identified, and the role of ICT in ensuring food security is considered. The article highlights the shortcomings and problems of using modern technologies in Chinese agriculture, among which are insufficient Internet coverage and insufficient information education of employees. The results of the study demonstrate the importance of information and communication technologies for Chinese agricultural enterprises and prove their positive impact on the development of agriculture in the country as a whole. The results obtained can be used to further improve information and communication technologies in Chinese agricultural production and to improve the digitalisation of agriculture in the country.

Information and communication technologies in agriculture: a systematic literature review

Information and communication technologies in agriculture: a systematic literature review

Consumption Pattern of Information and Communication Technology in Agriculture: A Study of Jaipur District

The study was conducted in Girdharipura and Mehla villages of Jaipur district of Rajasthan. The study analyzes how ICT is used for agricultural information in rural regions and evaluates the information and relationship between various ICT tools and farmers. In this research, we used a sample size of 100 farmers with a random selection of 50 farmers from Mehla village and 50 farmers from Girdharipura village, all between the age group of 20 and 55 from June 2021 to December 2021. ICT holds huge potential in the dissemination of agriculture-related information to rural masses for their capacity building. Thus, ICT is rightly hailed as a potentially useful tool for agricultural reforms. Farmers often engage in numerous forms of media consumption and do not limit themselves to a single one. This study suggests that farmers engage in various media consumption such as electronic media, folk media & print media, and ICT tools for agriculture purposes, and weather information. Mehla village was using more electronic media than Girdharipura.

Research on Information Technology Innovation and Rural Revitalization Strategies of Agricultural Complex Teaching and Research Bases in Colleges and Universities

Abstract The construction of agricultural composite teaching and research bases plays an important role in promoting rural revitalization and strengthening agricultural education. This paper emphasizes the role of agricultural information technology innovation in rural revitalization and proposes the impact of agricultural technology innovation on the development of rural revitalization. The variables of agricultural technology innovation as well as rural revitalization are selected, the benchmark regression model is set, the local impact model is set for the seven secondary indicators under agricultural technology innovation, and the evaluation index model is constructed. Through the empirical analysis method, the model is sequentially subjected to preliminary analysis and nonlinear characterization. Benchmark regression test using the fixed-effects model, model (7) shows that agricultural technological innovation at the 1% significant level of β =2.6548, P < 0.01, indicating that agricultural technological innovation has a positive and positive impact on rural revitalization. The threshold test shows that when the level of financial activity is lower than the threshold effect of 0.0965, the coefficient of the impact of agricultural technological innovation on rural revitalization is 0.0458. The effect of agricultural technological innovation on rural revitalization is affected by the intensity of financial activity, and there is a moderating effect of economic activity on both.

E-Agricultural for Better Yield

Abstract: The advent of newer technologies had led scientists and researchers to monitor and control agricultural activities remotely by the incorporation of information and communication technology (ICT). Modern information technologies have the impact to potentially increase the agricultural productivity by transferring knowledge and information to agricultural practitioners from the rural communities. Since the present generation have knowledge and easy access to smart mobile phones so the setup of control system installed in field can be monitored and controlled by new generation giving new dimensions to agriculture also known as smart agriculture. The agriculture is playing a major role in the economy of nation developing; therefore the need for upliftment in Agriculture is very important. Smart agriculture or e-Agriculture is a one of such concepts that is playing an important role in the enhancement of processes involved in Agriculture. The major role of ICT in Agriculture is its potentiality to aid a wide access to information that will support knowledge sharing and decision making. The usage of Information and Communication Technology (ICT) in Agriculture is growing day by day and more youth are attracted towards e-agriculture for their better earnings.

TRIBAL FARMING DYNAMICS: INVESTIGATING THE INFLUENCE OF SOCIO ECONOMIC AND PSYCHOLOGICAL ATTRIBUTES ON AGRICULTURAL INFORMATION MANAGEMENT PRACTICES

Agriculture is the cornerstone of many rural communities worldwide. Tribal farmers play a vital role in preserving agricultural customs and ensuring regional food security. The efficient management of agricultural information is critical in modern farming operations, as it allows farmers to enhance production, make informed decisions, and adapt to changing environmental conditions. Information and Communication Technologies (ICTs) in agriculture include networks, mobile devices, services, and applications that aid in the processing, management, and exchange of data, information, or knowledge within the farming ecosystem. ICTs assist farmers through educational procedures in improving farming methods and techniques, increasing production efficiency and income, bettering their levels of living, and lifting the social and educational standards of rural life. The study was conducted in the Keonjhar district of Odisha. An ex-post facto research design was followed for carrying out the study. The research study was followed by both purposive and non-probability random sampling methods for the selection of district, block, gram panchayat, village, and respondents. 120 respondents were selected through disproportionate random sampling. The study revealed the relationship between some socio—economic and psychological variables and Agricultural information management behavior. From this correlation, it was observed that age is negatively correlated and other variables such as gender, education, income, family size, outward orientation, training program, and social participation are positively correlated with information-seeking sources. These variables are directly proportional to the information-seeking sources.

APPLICATION OF ADVANCED TECHNOLOGIES IN AGRICULTURAL SECTOR FOR INCREASING GROSS PRODUCTION AND EXPORTING AGRICULTURAL PRODUCTS

This scientific article examines the influence of information technologies on the agro-industrial complex (AIC) during armed conflicts, specifically using the example of the war in Ukraine. Military actions have a negative impact on agricultural activities, leading to the destruction of crops, railways, transportation blockades, and infrastructure. However, information technologies can play a crucial role in ensuring food security and supporting the AIC in the context of armed conflict.The article analyzes the utilization of information technologies such as remote sensing of Earth, geographic information systems, drones, and agricultural sensors, which can provide essential information about the state of crops, water resources, and land areas. These technologies assist farmers in planning and making informed decisions regarding optimal resource utilization and ensuring food security even during times of armed conflict. Furthermore, the article examines the effectiveness of mobile applications, electronic document management, and farm management systems in ensuring efficient production and organizing agricultural processes to maintain the sustainable functioning of the AIC during armed conflict. These technologies enable farmers and agricultural enterprise managers to conduct effective monitoring and resource management, plan farm operations, and coordinate personnel actions even in the face of military threats. Additionally, the article analyzes the ethical aspects of using information technologies in the AIC during armed conflicts. This includes issues of data confidentiality protection for farmers, ethical use of data from mobile applications, and the safeguarding of worker privacy. The impact of these technologies on society and rural development during conflicts is also explored. Overall, the article provides a comprehensive view of the use of information technologies in agriculture during armed conflict and emphasizes the necessity of developing and implementing specialized solutions that address the unique challenges and needs of the AIC in such exceptional circumstances. Research in this field can contribute to ensuring the sustainable development of agriculture and enhancing the resilience of the AIC to armed conflicts and other hazards.

Factors Influencing Intention and Behaviour of Agricultural Extension Staff Concerting Post-Covid-19 Digital Technical Guidance: A Study in Yogyakarta, Indonesia

Agriculture extension staffs are encouraged to attempt more efficient methods to enhance their competency through digital webinars or technical guidance, in Bahasa Bimbingan Teknis (Bimtek) programs. The research aims to explore factors influencing the intention and behavior of agricultural extension staff towards the Technical guidance online Ministry of Agriculture in Special Region Yogyakarta, Indonesia. This research included the Behavior Intention (BI) and Use Behavior (UB) models with significant construct. The research used the quantitative method and analyzed PLS-SEM. One hundred eighty extension staffs participated, chiefly 34-42 years old. All of the indicators passed the outer model assessment. The analysis results conclude that significant effects are Attitude to BI, Control Behavior to BI, Perceived Usefulness to BI, and Behavior Intention to UB. The perception of extension staffs about Technical guidance online can enhance their performance to continue using it in obtaining agricultural technology information, extension materials, information on agricultural policies, and the latest agricultural development programs. The insignificant constructs are Subjective Norm, Motivation, and Perceived Ease to Use. The validated research model explains 61% of the variance (R2 = 0.61) in Attitude, Perceived Usefulness, and Control Behaviour towards Behavior Intention (BI). Moreover, BI explains 8.9% of the variance (R2 = 0.089) in UB of Technical guidance online. This research found that BI and UB have Q2 values of 0.538 and 0.077. The practical implication could be applied as preliminary development planning studies to enhance the efficiency of Technical guidance online as Bank Data.

Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review

The potential benefits of applying information and communication technology (ICT) in precision agriculture to enhance sustainable agricultural growth were discussed in this review article. The current technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), as well as their applications, must be integrated into the agricultural sector to ensure long-term agricultural productivity. These technologies have the potential to improve global food security by reducing crop output gaps, decreasing food waste, and minimizing resource use inefficiencies. The importance of collecting and analyzing big data from multiple sources, particularly in situ and on-the-go sensors, is also highlighted as an important component of achieving predictive decision making capabilities in precision agriculture and forecasting yields using advanced yield prediction models developed through machine learning. Finally, we cover the replacement of wired-based, complicated systems in infield monitoring with wireless sensor networks (WSN), particularly in the agricultural sector, and emphasize the necessity of knowing the radio frequency (RF) contributing aspects that influence signal intensity, interference, system model, bandwidth, and transmission range when creating a successful Agricultural Internet of Thing Ag-IoT system. The relevance of communication protocols and interfaces for presenting agricultural data acquired from sensors in various formats is also emphasized in the paper, as is the function of 4G, 3G, and 5G technologies in IoT-based smart farming. Overall, these research sheds light on the significance of wireless sensor networks and big data in the future of precision crop production

Are vulnerable farmers more easily influenced? Heterogeneous effects of Internet use on the adoption of integrated pest management

The Internet is believed to bring more technological dividends to vulnerable farmers during the green agriculture transformation. However, this is different from the theory of skill-biased technological change, which emphasizes that individuals with higher levels of human capital and more technological endowments benefit more. This study investigates the effects of Internet use on farmers’ adoption of integrated pest management (IPM), theoretically and empirically, based on a dataset containing 1 015 farmers in China's Shandong Province. By exploring the perspective of rational inattention, the reasons for the heterogeneity of the effects across farmers with different endowments, i.e., education and land size, are analyzed. The potential endogeneity issues are addressed using the endogenous switching probit model. The results reveal that: (1) although Internet use significantly positively affects farmers’ adoption of IPM, vulnerable farmers do not benefit more from it. Considerable selection bias leads to an overestimation of technological dividends for vulnerable farmers; (2) different sources of technology information lead to the difference in the degree of farmers’ rational inattention toward Internet information, which plays a crucial role in the heterogeneous effect of Internet use; and (3) excessive dependence on strong-tie social network information sources entraps vulnerable farmers in information cocoons, hindering their ability to reap the benefits of Internet use fully. Therefore, it is essential to promote services geared towards elderly-oriented Internet agricultural technology information and encourage farmers with strong Internet utilization skills to share technology information with other farmers actively.

Soil Classification using Machine Learning and Crop Suggestions

Abstract: India is a predominantly agricultural nation. Agriculture is currently the most significant emerging sector in the actual world and the key industry and economic pillar of our nation. The discipline of agricultural information technology has recently undergone significant changes that have made crop yield prediction an intriguing study topic. Crop yield prediction is a technique for estimating crop production using many characteristics, including temperature, rainfall, fertilisers, pesticides, and other climatic variables and parameters. The use of data mining tools is quite common in agriculture. Agriculture uses data mining tools to forecast agricultural production for next years and evaluates these strategies. This method provides a succinct study of K-Nearest Neighbour (KNN) and Support Vector Machine-based agricultural yield prediction.

Effectiveness of WhatsApp Messages on Knowledge Gain of Sugarcane Cultivation Practices among the Farmers

Information and communication technology in agriculture provides solutions to agricultural challenges. The present study was conducted in Athani taluk of Belagavi District in Karnataka state. ‘Before-After’ without control group experimental design was used to determine the effectiveness of WhatsApp messages on knowledge gain of sugarcane cultivation practices among farmers. WhatsApp messages on “Sugarcane cultivation practices” was the subject matter selected for the study and tested for their effectiveness. WhatsApp messages acted as treatment for the study. 50 Sugarcane growers were selected for the treatment on the basis of random sampling. The effectiveness was studied in terms of knowledge gain for WhatsApp messages. The results revealed that highest mean differences 2.50 was observed in case of integrated disease management followed by weed management (2.38), micronutrients and inter cultivation (2.10). The mean knowledge score of WhatsApp messages of the respondents before exposure was 20.52 and after exposure it was 50.52, thus there was mean knowledge gain of 30.00. Farming experience, land holding, extension contact, Innovative proneness achievement motivation and Management orientation were found to be positive and significant relationship with knowledge gain. Hence, WhatsApp messages are effective to disseminate the information among the farmers.

Kinerja Penyuluh Pertanian Lapangan di Kecamatan Kotabunan Kabupaten Bolaang Mongondow Timur

This research aims to: 1). Knowing the internal factors and external factors agricultural field extension, 2). Knowing the performance of field agricultural extension in Kotabunan District, East Bolaang Mongondow Regency. This research was conducted from October to November 2022. The sampling method was purposive sampling. This research used primary data obtained through interviews and observation and secondary data obtained from books, previous research and BPS of Kotabunan District. The data obtained is presented in tabular form and then analyzed descriptively. Category determination is obtained based an assessment or score with the measurement method used is the Likert scale. The results of the study show that the internal factors that support are the extension workers are still in the productive age, some extension workers have a formal undergraduate education indicating that the human resources of the extension workers are quite good, most of the extension workers have enough years of work/work experience and all extension workers have attended training. The external factors that support the availability of sufficient facilities, infrastructure and information to make it easier for extension agents to carry out extension activities, good extension intensity can increase cooperative relations between farmers and a supportive reward system can increase enthusiasm and motivation for extension workers to work. The performance of field agricultural extension in Kotabunan District, East Bolaang Mongondow Regency is in the very good category. Performance indicators that show very good results include the preparation of extension programs, the availability of regional map data according to commodity zoning, the preparation of agricultural extension work plans in each work area, the dissemination of agricultural technology information evenly, and the growth and development of farmer empowerment and independence. The performance of extension agents that showed poor results was the realization of business partnerships between farmers and entrepreneurs and the realization of farmers' access to financial institutions.

IoT Based Agriculture Monitoring System using Arduino Uno and Node MCU

Indian agriculture is diverse ranging from impoverished farm villages to developed farms utilizing modern agricultural technologies. Promoting application of modern information technology in agriculture will solve a series of problems facing by farmers. This system provides an intelligent monitoring platform framework and system structure to facilitate agriculture’s ecosystem based on IOT. This will be a Catalyst for the Transition from Traditional Farming to Modern Farming. This also provides opportunity for creating new technology and service development in IOT (Internet of things) farming application. In INDIA, the population has tripled, but food grain production had more than quadrupled, thus as there has been a substantial increase in available food grain per capita. Modern agriculture practices have a great promise for the economic development of a nation. So we have brought-in this innovative project for the welfare of Farmers and also for the Best Quality of Crops. There are no such restrictions of Day and Night in our project. Using this Project, Farming can be done at any time.

Design and Test of an Automatic Navigation Fruit-Picking Platform

With the development in agricultural mechanization and information technology, orchard agricultural machinery is also constantly pursuing the goal of intelligence and efficiency. Fruit picking is the most labor-intensive part of the orchard harvesting process. In order to resolve the problems of high labor intensity, low picking efficiency, and labor shortage when harvesting dwarf high-density orchards, an automatic navigation fruit-picking platform with voice control was developed in the present study. First, the platform utilized a voice-controlled high-level extendable working platform and a fruit-box-lifting device to adapt to varying orchard planting row spacing and enable convenient fruit box loading and unloading. Second, an automatic navigation system, which employed China’s Beidou navigation satellite system to acquire positional data and the Stanley algorithm for path-tracking control, was implemented. Third, the fruit-picking platform with automatic navigation system was fabricated and assembled, its outer wheel steering was measured to have a maximum angle of 30.3° and an average minimum turning radius of 4.5 m, meeting the turning radius requirements under orchard conditions. Finally, automatic navigation tests of the developed platform were performed in the orchard conditions. The results indicated that the platform could maintain a straight-line path with a maximum lateral deviation of 101.5 mm and a maximum absolute average deviation of 44.1 mm at 0.4 m/s. Under the U-shaped paths, the measured maximum lateral deviation was 148.6 mm and the maximum absolute average deviation was 57.2 mm. The navigation accuracy was sufficient to meet the requirements for the harvesting platform’s routine operation in the orchards.

Research on Path Tracking of Articulated Steering Tractor Based on Modified Model Predictive Control

With the development of agricultural mechanization and information technology, automatic navigation tractors are becoming a more common piece of farm equipment. The accuracy of automatic navigation tractor path tracking has become critical for maximizing efficiency and crop yield. Aiming at improving path tracking control accuracy and the real-time performance of the traditional model predictive control (MPC) algorithm, the study proposed an adaptive time-domain parameter with MPC in the path tracking control of the articulated steering tractor. Firstly, the kinematics model of the articulated steering tractor was established, as well as the multi-body dynamics model by RecurDyn. Secondly, the genetic algorithm was combined with MPC. The genetic algorithm was used to calculate the optimal time domain parameters under real-time vehicle speed, vehicle posture and road conditions, and the adaptive MPC was realized. Then, path tracking simulations were conducted by combining RecurDyn and Simulink under different path types. Compared with the traditional MPC algorithm under the three paths of U-shaped, figure-eight-shaped and complex curves, the maximum lateral deviations of the modified MPC algorithm were reduced by 59.0%, 24.9% and 13.2%, respectively. At the same time, the average lateral deviation was reduced by 72%, 43.5% and 20.3%, respectively. Finally, the real path tracking tests of the articulated steering tractor were performed. The test results indicated that under the three path tracking conditions of straight line, front wheel steering and articulated steering, the maximum lateral deviation of the modified MPC algorithm was reduced by 67.8%, 44.7% and 45.1% compared with the traditional MPC. The simulation analysis and real tractor tests verified the proposed MPC algorithm, considering the adaptive time-domain parameter has a smaller deviation and can quickly eliminate the deviation and maintain tracking stability.