Development Of Modern Agriculture Research Articles

Training of New Professional Farmers in Rural Revitalization: A Reexamination based on Supply and Demand

The rural revitalization strategy is a crucial initiative aimed at promoting the modernization of rural areas, with the cultivation of new professional farmers emerging as a key component in its implementation. This paper conducts an in-depth analysis of the training of new professional farmers from the perspectives of both supply and demand. Addressing issues related to cultivation policies, key players, and training content during the cultivation process, the paper proposes a series of strategies in areas such as top-level design, classification management, cultivation models, content innovation, and supervision mechanisms. In terms of top-level design, the paper emphasizes the importance of defining strategic goals and pathways for cultivation to ensure alignment with the rural revitalization strategy. The strategy combining classification management with precision cultivation aims to implement differentiated training plans that better cater to the specific needs of different regions and rural areas. Reforms in cultivation models and content are advocated to align with the trends in modern agricultural development. Additionally, the establishment of a comprehensive supervision mechanism is recommended to ensure the fairness and transparency of cultivation efforts.

Spatial distribution, influencing factors and innovative development countermeasures of farmer cooperatives in Hunan Province, China

China’s rural reform is reliant on farmers' cooperatives as a key organization vehicle. It plays an important role in promoting rural revitalization. Such as, realizing the organic connection between small farmers and agricultural modernization. This study used the nearest neighbor index and kernel density estimation to analyze the spatial distribution and structural characteristics of farmer cooperatives in Hunan Province. It revealed the spatial differentiation law of cooperatives. Also using geographical detectors to figure out the main factors that affect the spatial distribution. The results show that: ① Hunan Farmers Cooperatives show significant spatial agglomeration. The overall spatial distribution shows the spatial characteristics of "one core, one circle, and multiple points". And the spatial distribution pattern of "large agglomeration, finger-shaped radiation distribution". Among them, the distribution of provincial demonstration cooperatives is relatively balanced. ② Cooperatives in six types of industries, such as planting, forestry, and feeding, showed agglomeration distribution. Different industrial cooperatives spread out in different ways, such as in an anti-"L" shape, a dual-core shape, or a one-center area. ③ The 24 influence factors selected from the five dimensions of the natural environment, social economic basis, production basis, output capacity, and facility basis have high, general, and weak influence on cooperatives' spatial distribution. The development and distribution of cooperatives mainly depend on resource endowment, social and economic development level, and market dependence. The spatial distribution heterogeneity of different professional cooperatives is mainly affected by slope, per capita disposable income of rural residents, road density, and other factors. ④ The progression of farmer cooperatives in Hunan Province should prioritize high-quality development, emphasizing the need for innovative approaches and transformative strategies within rural industrial organizations. It is imperative to optimize the spatial distribution of cooperatives, strategically contributing to the establishment of a novel framework for modern agricultural development in Hunan Province. Additionally, there is a critical emphasis on spearheading collaborative initiatives among cooperatives of varied industrial types, aimed at fostering the integrated development of rural primary, secondary, and tertiary industries.

Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters.

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the "trilateral" parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0-2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the "trilateral" parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0-2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.

Multiple signal-enhanced electrochemiluminescence aptamer sensors based on carboxylated ruthenium (II) complexes for acetamiprid detection

BackgroundRapid and sensitive detection for acetamiprid, a kind of widely used neonicotinoid insecticide, is very meaningful for the development of modern agriculture and the protection of human health. Highly stable electrochemiluminescence (ECL) materials are one of the key factors in ECL sensing technology. ECL materials prepared by porous materials (e.g., MOFs) coated with chromophores have been used for ECL sensing detection, but these materials have poor stability because the chromophores escape when they are in aqueous solution. Therefore, the development of highly stable ECL materials is of great significance to improve the sensitivity of ECL sensing technology. ResultsIn this work, by combining etched metal-organic frameworks (E-UIO-66-NH2) as carrier with Tris(4,4′-dicarboxylic acid-2,2′-bipyridine)Ru(II) chloride (Ru(dcbpy)32+) as signal probe via amide bonds, highly stable nanocomposites (E-UIO-66-NH2-Ru) with excellent ECL performance were firstly prepared. Then, using MoS2 loaded with AuNPs as substrate material and co-reactant promoter, a signal off-on-off ECL aptamer sensor was prepared for sensitive detection of acetamiprid. Due to the excellent catalytic activity of E-UIO-66-NH2-Ru and MoS2@Au towards K2S2O8, the ECL signals can be enhanced by multiple signal enhancement pathways, the prepared ECL aptamer sensor could achieve sensitive detection of acetamiprid in the linear range of 10−13 to10−7 mol L−1, with the limit of detection (LOD) of 2.78ⅹ10−15 mol L−1 (S/N = 3). After the evaluation of actual sample testing, this sensing platform was proven to be an effective method for the detection of acetamiprid in food and agricultural products. Significance and noveltyThe E-UIO-66-NH2-Ru prepared by linking Ru(dcbpy)32+ to E-UIO-66-NH2 via amide bonding has very high stability. The synergistic catalytic effect of MoS2 and AuNPs enhanced the ECL signal. By exploring the sensing mechanism and evaluating the actual sample tests, the proposed signal "on-off" ECL sensing strategy was proved to be an effective and excellent ECL sensing method for sensitive and stable detection of acetamiprid.

Soil extractivism: Political ontology of soil erasure in the European Union's agricultural politics

This article examines how specific (non-relational) political ontological understandings of soils are incorporated into industrial agricultural politics in the European Union and introduces a complementary contribution to existing literature with the concept of soil extractivism. The concept is used to describe current institutionalized ways of relating to soils and their dynamics of power. Through an integrative literature review, the article links existing research on soils with the development of modern agriculture in Europe. The theoretical contribution of soil extractivism provides a conceptual tool for understanding currently dominant ways of treating soils as a key part of modernity and industrial capitalism as well as part of wider destructions of multispecies relations.

Measurement and analysis of regional water-energy-food nexus resilience with an improved hybrid kernel extreme learning machine model based on a dung beetle optimization algorithm

CONTEXTIn traditional water-energy-food nexus (WEFN) system analysis, the impact of resilience is insufficiently considered, and the accuracy of resilience assessment is low, which can easily lead to system imbalance. Modeling measures can yield optimal evaluation results and provide decision makers with a scientific and efficient basis for management. OBJECTIVEThe objective of this study was to provide a new research paradigm for exploring the sustainable development of WEFN systems by quantitatively and accurately assessing WEFN resilience and identifying its key driving factors. METHODSThe connotation of WEFN resilience was established, and a human-centered conceptual framework for WEFN resilience was constructed. A WEFN resilience evaluation system was scientifically and completely constructed using qualitative and quantitative index screening methods. The hybrid kernel extreme learning machine (HKELM) model was improved based on the dung beetle optimization (DBO) algorithm to measure and analyze the WEFN resilience of the China Beidahuang Group. The CRiteria Importance Through Intercriteria Correlation (CRITIC) method was adopted to calculate the evaluation index weight of the WEFN system of the Beidahuang Group. Model performance verification methods, such as application of the theory of serial number summation, were used to evaluate the performance of the DBO-HKELM model. RESULTS AND CONCLUSIONSThe results showed that over time, the Beidahuang Group's WEFN resilience exhibited a fluctuating upward trend. From 2006 to 2008, WEFN resilience declined to level II because of predatory agricultural production and management practices. From 2008 to 2018, the WEFN resilience increased to level IV due to the protection of uncultivated land, the optimization of irrigation methods, and the increasing maturity of the agricultural modernization development model. Likewise, from 2018 to 2020, the WEFN resilience stabilized at level IV because of the optimization of agricultural planting structures and the reduction in coal consumption. Spatially, the WEFN resilience of various branches of the Beidahuang Group exhibited a trend of “gradually decreasing from north to south.” Differences in certain variables, such as the proportion of area irrigated with electromechanical wells, were the most important factors related to changes in WEFN resilience over time. The DBO-HKELM model has notable advantages over the FA-HKELM and NGO-HKELM models in terms of fitting accuracy, optimization rate, reliability, rationality, and robustness. SIGNIFICANCEThese research results could enrich quantitative assessment methods for determining WEFN system recovery strength and provide scientific support for the safe operation and sustainable development of agricultural economies and societies.

Automatic grading evaluation of winter wheat lodging based on deep learning.

Lodging is a crucial factor that limits wheat yield and quality in wheat breeding. Therefore, accurate and timely determination of winter wheat lodging grading is of great practical importance for agricultural insurance companies to assess agricultural losses and good seed selection. However, using artificial fields to investigate the inclination angle and lodging area of winter wheat lodging in actual production is time-consuming, laborious, subjective, and unreliable in measuring results. This study addresses these issues by designing a classification-semantic segmentation multitasking neural network model MLP_U-Net, which can accurately estimate the inclination angle and lodging area of winter wheat lodging. This model can also comprehensively, qualitatively, and quantitatively evaluate the grading of winter wheat lodging. The model is based on U-Net architecture and improves the shift MLP module structure to achieve network refinement and segmentation for complex tasks. The model utilizes a common encoder to enhance its robustness, improve classification accuracy, and strengthen the segmentation network, considering the correlation between lodging degree and lodging area parameters. This study used 82 winter wheat varieties sourced from the regional experiment of national winter wheat in the Huang-Huai-Hai southern area of the water land group at the Henan Modern Agriculture Research and Development Base. The base is located in Xinxiang City, Henan Province. Winter wheat lodging images were collected using the unmanned aerial vehicle (UAV) remote sensing platform. Based on these images, winter wheat lodging datasets were created using different time sequences and different UAV flight heights. These datasets aid in segmenting and classifying winter wheat lodging degrees and areas. The results show that MLP_U-Net has demonstrated superior detection performance in a small sample dataset. The accuracies of winter wheat lodging degree and lodging area grading were 96.1% and 92.2%, respectively, when the UAV flight height was 30m. For a UAV flight height of 50m, the accuracies of winter wheat lodging degree and lodging area grading were 84.1% and 84.7%, respectively. These findings indicate that MLP_U-Net is highly robust and efficient in accurately completing the winter wheat lodging-grading task. This valuable insight provides technical references for UAV remote sensing of winter wheat disaster severity and the assessment of losses.

Risks in AGRIGULF's Agricultural Export Settlement Business and Its Management

This paper is primarily a case-based study. It takes Gulf Modern Agriculture Development Co., Ltd (hereinafter referred to as AGRIGULF) as the object of research, and adopts the case study method and field interview method to sort out the company's agricultural products export business and common settlement methods and so on. Through field visits and investigation, data collection, and collation, identify and analyze the risks of the company's agricultural export settlement business. Finally, corresponding risk management suggestions are put forward at four levels, namely, country risk, foreign exchange risk, product quality change risk, and credit risk, which are expected to provide certain references for the sustainable development of China's agricultural export enterprises

Research on Tax Policies to Promote Agricultural Modernization Development

Agriculture constitutes a fundamental sector of national economic development, and the steady growth of agriculture-related industries has contributed significantly to job creation and increased value output in the Chinese economy. The central government has consistently prioritized the healthy and sustainable development of agriculture, implementing various policies to support agricultural enterprises in enhancing their market competitiveness. This includes a wide range of tax support measures. On the other hand, the effectiveness of policy incentives in encouraging enterprises to improve their operations is unclear, given that most agricultural enterprises in the country face challenges such as small size, outdated information, weak management capabilities and poor resistance to market risks and uncertainties. This paper aims to examine the role of tax policy in the industrialised management of the agricultural sector. This paper seeks to provide new insights into governance in this area by exploring the moderating role of tax policies on the relationship between Chinese agribusinesses and their management. The findings will provide policy makers with responsible recommendations for enhancing the effectiveness of tax policies and other agriculture-related policies.

Improving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices.

Timely and accurate estimation of cotton seedling emergence rate is of great significance to cotton production. This study explored the feasibility of drone-based remote sensing in monitoring cotton seedling emergence. The visible and multispectral images of cotton seedlings with 2 - 4 leaves in 30 plots were synchronously obtained by drones. The acquired images included cotton seedlings, bare soil, mulching films, and PE drip tapes. After constructing 17 visible VIs and 14 multispectral VIs, three strategies were used to separate cotton seedlings from the images: (1) Otsu's thresholding was performed on each vegetation index (VI); (2) Key VIs were extracted based on results of (1), and the Otsu-intersection method and three machine learning methods were used to classify cotton seedlings, bare soil, mulching films, and PE drip tapes in the images; (3) Machine learning models were constructed using all VIs and validated. Finally, the models constructed based on two modeling strategies [Otsu-intersection (OI) and machine learning (Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)] showed a higher accuracy. Therefore, these models were selected to estimate cotton seedling emergence rate, and the estimates were compared with the manually measured emergence rate. The results showed that multispectral VIs, especially NDVI, RVI, SAVI, EVI2, OSAVI, and MCARI, had higher crop seedling extraction accuracy than visible VIs. After fusing all VIs or key VIs extracted based on Otsu's thresholding, the binary image purity was greatly improved. Among the fusion methods, the Key VIs-OI and All VIs-KNN methods yielded less noises and small errors, with a RMSE (root mean squared error) as low as 2.69% and a MAE (mean absolute error) as low as 2.15%. Therefore, fusing multiple VIs can increase crop image segmentation accuracy. This study provides a new method for rapidly monitoring crop seedling emergence rate in the field, which is of great significance for the development of modern agriculture.

Diversity and Antibiotic Resistance of Triticale Seed-Borne Bacteria on the Tibetan Plateau.

The Tibetan Plateau is located in southwestern China. It has many important ecological functions, such as biodiversity protection, and is an important grassland agroecosystem in China. With the development of modern agriculture and animal husbandry, antibiotics are widely used to treat humans and livestock, and antibiotics cannot be fully metabolised by both. Antibiotics eventually find their way into the environment, affecting other parts of grassland agroecosystems. Triticale (Triticosecale wittmack) is an artificial hybrid forage that can be used for both grain and forage. This study revealed the diversity of seedborne bacteria in triticale on the Tibetan Plateau and the resistance of the bacteria to nine antibiotics. It identified 37 representative strains and successfully obtained the spliced sequences of 36 strains of the bacteria, which were clustered into 5 phyla and 16 genera. Among them, 18 strains showed resistance to at least one of the 9 antibiotics, and the colony-forming unit (CFU) abundance of antibiotic-resistant bacteria (ARB) accounted for 45.38% of the total samples. Finally, the bacterial motility and biofilm formation ability were measured, and their correlation with bacterial resistance was analysed. The results showed that the bacterial resistance did not have an absolute positive correlation with the motility or biofilm formation ability.

Industrial technological progress, technology spillover, and the environment in a dual agricultural economy

AbstractDeveloping nations typically prioritize economic growth. Thus, industrial technological progress frequently leads to production expansion and subsequent environmental degradation. This paper, however, proposes that in a dual agricultural economy, the progress in industrial production technology does not necessarily degrade the environment when considering technology spillover. Utilizing a general equilibrium model, we conclude that, at the beginning of modern agricultural development, increasing technology spillover rate adversely affects the environment and social welfare. Yet, after the popularization of modern agriculture, increasing this rate can enhance environmental and welfare conditions. Under specific conditions, industrial technological progress might promote environmental and welfare benefits.

Study on Accelerating the Development of Agricultural Modernization by Fiscal Policies in the Context of "14th Five-Year Plan"

This paper analyzes the background of the study of agricultural modernization in Sichuan Province from the aspects of national policy background, and conducts research on agricultural modernization in Sichuan Province from the connotation and characteristics of agricultural modernization, analysis of the current situation, analysis of the influencing factors, and the choice of path. Through the study, in order to hope can provide help to promote the process of agricultural modernization in Sichuan Province. Under the background of the national vigorously promote the modernization of agriculture, in-depth study of the background and path selection of agricultural modernization in Sichuan Province, for improving the efficiency of agricultural production and the quality of agricultural products in Sichuan Province, to protect the national food security and to further promote the perfect solution of the three rural issues and economic development has its practical significance.

Measurement of Agricultural Eco-Efficiency and Analysis of Its Influencing Factors: Insights from 44 Agricultural Counties in Liaoning Province

Agricultural eco-efficiency (AEE) considers economic and environmental benefits and is a key indicator of green agricultural development. To achieve the multiple goals of improving agricultural production efficiency, reducing agricultural environmental damage, and reducing the input of agricultural resources, this study enriches the case study of agricultural production performance evaluation at the county level by measuring the AEE of 44 agricultural counties in Liaoning Province based on panel data and a super-efficient slacks-based measure model including undesired outputs. A two-way fixed-effects model was used to analyze the impact of agricultural development, macro-environment, and policy support on AEE. We found that the average AEE of the counties in Liaoning Province in 2014, 2016, 2018, and 2020 was 0.716, 0.735, 0.749, and 0.813, respectively, indicating a cumulative improvement rate of 13.55%. The average AEE levels gradually improved during the study period. Notably, the development of AEE among the counties was uneven. AEE was distributed in a “block-like” manner, and its local correlation presents a phenomenon of “small agglomeration and large dispersion”. In addition, the level of the agricultural economy, industrialization, and urbanization significantly promoted the improvement of AEE, and the promoting effects varied between different income levels and regions. Therefore, Liaoning Province needs to improve the AEE of each county according to local conditions and narrow the differences in AEE between counties. To continuously improve the level of rural economic development, lead the development of agricultural modernization with new urbanization, and comprehensively improve the overall AEE of counties. The research results are of guiding significance for deepening the study of AEE and can provide decision-making support for optimizing the mode of agricultural production and promoting the green development of regional agriculture.

Assessment of the Coupling Degree between Agricultural Modernization and the Coordinated Development of Black Soil Protection and Utilization: A Case Study of Heilongjiang Province

Agricultural management encompasses various processes, including agricultural modernization and land protection and utilization; however, these aspects are seldom considered simultaneously. This study focuses on Heilongjiang Province, a major grain-producing region in China, in efforts to address this gap. The objective is to facilitate the mutual sustainable development of agricultural modernization as well as the protection and utilization of black soil through research and coordination. Statistical data from a case study conducted in Heilongjiang Province serve as the basis for this investigation, aiming to identify contradictions in the coordinated development of agricultural modernization and black soil protection and utilization in the province, design mechanisms to sustain this development, and ensure the mutually supportive progress of both aspects. This research delves into an infrequently explored dimension of the current policies surrounding black soil imposed by the Chinese authorities, offering significant insights into agricultural modernization.

Automatic fruit picking technology: a comprehensive review of research advances

In recent years, the fruit industry has become an important part of agricultural development, and fruit harvesting is a key stage in the production process. However, picking fruits during the harvest season is always a major challenge. In order to solve the challenges of time-consuming, costly, and inefficient fruit picking, researchers have conducted a lot of studies on automatic fruit picking equipment. Existing picking technologies still require further research and development to improve efficiency and reduce fruit damage. Aiming at the efficient and non-destructive picking of fruits, this paper reviews machine vision and mechanical fruit picking technology and the current research status, including the current application status, equipment structure, working principle, picking process, and experimental results. As a promising tool, machine vision technology has been widely researched and applied due to its low hardware cost and rich visual information. With the development of science and technology, automated fruit picking technology integrates information technology, integrates automatic perception, transmission, control, and operation, etc., saves manpower costs, and continuously promotes the development of modern agriculture in the direction of refinement of equipment technology, automation, and intelligence. Finally, the challenges faced by automated fruit picking are discussed, and future development is looked forward to with a view to contributing to its sustainable development.

Security analysis of agricultural energy internet considering electricity load control for dragon fruit cultivation

With the increasing emphasis on sustainable energy and the advancements in modern agriculture, flexible agricultural power loads present challenges to the reliable operation of the agricultural energy internet. However, research on the coupling of energy system security with agricultural security is insufficient and fails to consider the impacts of meteorological elements on agricultural power loads. To address these gaps, this paper establishes load models for irrigation and light supplementation based on the actual cultivation demands of winter dragon fruit in Guangxi province. A static security index system is developed to analyze the security, considering the unique features of agricultural power demands. The condition of the distribution network is assessed by comparing the indexes with predefined limits, using a China 41-bus distribution network. Finally, the optimal scheme for nocturnal supplemental lighting treatment and irrigation is determined based on the method for maintaining secure operation of the distribution network. This study serves as a guide for simulating current farming power loads and demonstrates how security analysis of the agricultural energy internet contributes to the large-scale and sophisticated development of modern agriculture.

Professional standard «Agronomy» in the context of employer's requests

Introduction. In the article, the authors drew attention to the fact that the development of modern agriculture is aimed at finding new ways and methods to increase land productivity and raise farm animals. In the current situation, the interaction between real production and science comes to the fore. Science should provide domestic enterprises with high technologies. However, there is a large gap between universities and the real sector of the economy.Purpose setting. Compliance of the competencies of graduates with the requirements of employers is becoming a modern trend in the development of agricultural education. In the conditions of innovative economic development, the labor market needs specialists who can work in various professional environments and have a high motivation for retraining.Methodology and methods of the study. The secondary analysis of sociological research data on the compliance of the level of professional training of graduates of agricultural universities with the production requirements of today is compared with the results of a sociological study of students of Nizhny Novgorod Agrotechnological University.Results. The researchers not only identified a set of problems inherent in modern agricultural education, but also described specific competencies (according to the federal state educational standard in the area of training «Agronomy» (FGOS 3++), which do not meet the needs of the leading stakeholders of agricultural production, revealed the inconsistency of the subjects of the educational process in understanding and broadcasting professional qualifications.Conclusion. The authors believe that the results obtained determine the relevance of the rights of agricultural universities to make their own adjustments to the formation of the qualifications of graduates.

Evolutionary game analysis of stakeholders’ decision-making behavior in agricultural data supply chain

The significance of agricultural information sharing in fostering agricultural development cannot be overstated. This practice plays a pivotal role in disseminating cutting-edge agricultural technologies, cultivation methods, and pest control strategies, empowering farmers with valuable knowledge to enhance crop yield and quality. Moreover, it aligns with government objectives of resource sharing and addressing gaps, contributing to the advancement of agricultural modernization and the development of the industry chain. Despite its inherent benefits, the practical implementation of agricultural information sharing faces challenges. Stakeholders engaged in information sharing often prioritize individual benefits, potentially leading to a decline in agricultural information quality and the inefficient use of experimental resources. To confront this issue, the present research establishes a three-party evolutionary game model comprising an agricultural product data sharing platform, agricultural data providers, and agricultural data consumers. Leveraging dynamic system theory, the model analyzes the evolutionary stable strategies of stakeholders and investigates the critical factors influencing the strategic choices of these three parties. Experimental findings underscore the pivotal role of participants’ initial strategies, regulatory intensity, reward and punishment mechanisms, and information feedback in shaping stakeholder decision-making behavior. Implementation of measures such as heightened scrutiny of information on the sharing platform and fostering consumer trust in data emerges as imperative for enhancing system stability. These actions are essential for constructing an efficient and reliable information-sharing ecosystem, thereby facilitating the sustainable development of modern agriculture.

Estimation of Soil Moisture Content Based on Fractional Differential and Optimal Spectral Index

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy’s original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model’s accuracy follows the order: 1.5 order > 2.0 order > 1.0 order > 0.5 order > original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF > SVM > BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models.