Agricultural Management Systems Research Articles

Application of Precision Farming in Horticulture: A Comprehensive Review

With the ever-increasing global population and diverse challenges caused by climate change, horticultural productivity must increase in an ecofriendly and sustainable way. Precision farming, also known as precision agriculture may play an important role to improve this situation by a data-driven farm management system that aims to identify, analyze, and manage variability within fields for optimal profitability, sustainability, and land resource protection. It involves applying the right treatments at the right time and location within a field. In horticulture, precision farming plays a crucial role in resource-efficient management as well as reducing significant labour requirement, including techniques like fertigation, greenhouse cultivation, soil and leaf nutrient-based fertilizer management, and more. This comprehensive review paper explores the application of precision farming in horticulture, emphasizing its potential impact on crop management, environmental health, and economic sustainability.

Mechanisms and Impact Effects of Digital Agriculture Development on Agricultural Eco-Efficiency in China

The green development of agriculture is an essential way to achieve high-quality agricultural development, and the development of digitalization has given new momentum to the green development of agriculture. In this study, based on the panel data of 30 provinces in China from 2011 to 2022, we measure the agricultural eco-efficiency and the level of digital agriculture development in China using the Super-SBM model with global reference and the entropy value method, respectively. The impact of the level of digital agriculture development on agricultural eco-efficiency is explored with the help of a regression model, and the mediating role of pesticide and fertilizer inputs in this impact pathway is explored using a mediating effects model. The study found that: (1) the level of digital agriculture development positively and significantly affects agricultural eco-efficiency to a relatively large extent; (2) the effect of digital agriculture development on the improvement of agriculture eco-efficiency is significantly heterogeneous in different regions; (3) pesticide and fertilizer inputs have a mediating role in this impact pathway. Therefore, the application and promotion of digital agriculture technology should be strengthened to build a green agricultural production and management system, so as to promote high-quality and sustainable development of Chinese agriculture.

A comprehensive analytical and computational assessment of soil water characteristics curves in Atlantic Canada: Application of a novel SelectKbestbased GEP model

For sustainable agriculture, irrigation is one of the most important management practices in a semi-arid climate. Determining the site-specific soil type-based irrigation water requirements requires the development of a soil water characteristics curve (SWCC). This study focuses on exploring the suitability of analytical SWCC models based on the observed data of SWCC developed using the pressure membrane apparatus. A novel SelectKbest gene expression programming (GEP) model is used to create the generalized machine learning SWCC model for Atlantic Canadian soils. The composite soil samples with representative soil textures of loamy sand, loam, and sandy loam are collected from the selected locations in Prince Edward Island and New Brunswick provinces of Canada. The suitability of models at three regions shows that at all sites, including PEI-1 (loam soil) 1, PEI-2 (loamy sand), and NB-3 (sandy loam) if SWCC data has four measured suction potential levels, then McBee & Bomb and Tani exponential models may be selected. Van Genuchten, double exponential, Ruso, Kosuki, and Gardner's four parameters models should be a good choice for five-section potential. Similarly, for six measured suction potential values, the Fredlund & Xing, Omuto, and Van Genuchten models having parameters should be selected for determining the optimum soil hydraulic parameters. The HydroMe program with Brooks and Corey, four, and five parameters Van Genuchten models performed well. The HydroMe program is calibrated and validated with standard RETC for selected regions of Atlantic Canada, which suggests that the HydroMe program is flexible in developing efficient agricultural water management algorithms. Various performance metrics like R2 and RMSE are employed to assess the robustness of the model. A generalized SWCC model (R2=0.976 and RMSE=0.04) is developed using a novel SelectKbest-based GEP modeling approach for Atlantic Canadian soils. These novel approaches can significantly improve site-specific agricultural water management and decision support systems.

Smart Agriculture Management System: Integration of IoT Technology and Mobile Application for Enhanced Farming Practices

Abstract: This paper presents a collaborative effort between Computer Science and Electronics and Communication Engineering students aimed at developing a comprehensive smart agriculture management system. Traditional farming practices face challenges related to climate variability and resource optimization, highlighting the need for innovative solutions. Leveraging IoT technology and mobile application development, this project proposes a holistic approach to address these challenges. The system integrates wireless sensors, microcontrollers, cloud computing, and a user-friendly mobile application to enable real-time monitoring, control, and optimization of agricultural processes. The paper discusses the architecture, design, implementation, and performance evaluation of the Smart Agriculture Management System (SAMS), emphasizing its potential to revolutionize farming practices and enhance productivity.

AGRICULTURAL FIELD MANAGEMENT PROBLEMS: FROM CLASSIC AGRO-TECHNOLOGIES TO ARTIFICIAL INTELLIGENCE

The text is straightforward and precise. The agricultural sector has been significantly impacted by artificial intelligence (AI) technologies such as expert systems, natural language processing, speech recognition, and machine vision. This is due to factors such as the rising global population, increasing demand for food, changing weather conditions, and water availability. AI has not only increased the quantity of work in agriculture but also improved its quality. Researchers and scientists are now adopting new IoT technologies in smart farming to enable farmers to use AI technology for the advancement of seeds, crop protection, and fertilizers. This would enhance the financial viability of farmers and contribute to the general economic growth of the nation. AI is being used in three primary areas in agriculture: soil and crop monitoring, predictive analytics, and agricultural robots. Farmers are increasingly using sensors and soil sampling to collect data for farm management systems, which will be utilized for future studies and analysis. This paper enhances the area by doing a study of artificial intelligence applications in the agriculture industry. The text provides an introduction to AI, covering several AI techniques used in the agricultural sector, such as machine learning, the Internet of Things (IoT), expert systems, image processing, and computer vision.

Methodology for forming a digital farming management system

The paper presents methodological approaches for the creation of a digital farming management system (DFMS). A convergent approach, based on cognitive (conceptual) analysis methods, is employed in the research and applied to the formation of adaptive landscape farming systems. The fundamental principles of organizing DFMS include crop and environmental monitoring (in situ, remote sensing); the formation of farming system archetypes based on the analysis of long-term field experiments; spatial object modeling and land typology using GIS; planning and support for agrotechnologies to adapt to natural and economic conditions; modeling ecosystem services and biodiversity; assessing the impact on the sustainability and economics of crop production. The system is implemented using geoinformation models in a specific geographic coordinate. DFMS involves conducting a "inventory" of natural and production resources, as well as identifying limits of climatic, soil, and agrolandscape parameters at different levels of land use intensity. At each stage of organizing system blocks, methods of intelligent data analysis and machine learning are used, with the core of the system relying on the use of knowledge bases and logical rules of the subject area. A key element of the system is the scaling of the results of long-term field experiments and accumulated knowledge in different management areas based on the parameterization of the multi-level variability of farming systems and the formation of their archetypes. The practical implementation of the main provisions of DFMS allows approaching the solution of key issues related to reducing the level of uncertainty and associated risks in agriculture. This is achieved through scientifically justified organization of rational land use, increasing the resilience of crop production in different land use conditions, and providing information support to rural producers.

Smart Agricultural Management System for Small-Scale Farmers in India

Agriculture can be defined as the science or practice of farming, including the growing of crops and the rearing of animals. Agriculture is one of the most important sectors in India 's economy and could benefit tremendously with the application of information communication technology. Due to tremendous advances during the last decade, information technology is today affecting all the spheres of human life. We can exploit these advances to design a cost-effective system to provide expert advice to farmers and the general public. This project focuses on how information communication technology can be used to better improve agriculture in India. This will be done by implementing an online electronic Agro-commerce system, a livestock and vegetable farming information repository, buying and customer overview implementation , showing crop distribution in different farming areas of India and an electronic records management system for local cooperatives and associations. Online marketplaces connect farmers directly with buyers, enabling them to sell their products without intermediaries. Keywords: Agriculture, Farmers, Agri Products, Online Marketplace etc.

An effective object detector via diffused graphic large selective kernel with one-to-few labelling strategy for small-scaled crop diseases detection

Amid the swift evolution of advanced technologies such as autonomous robotics, edge computing, and the Internet of Things, Intelligent Agriculture Management Systems (IAMS) have become increasingly pivotal in contemporary smart agriculture. One vital task, crop disease monitoring, significantly contributes to ensuring agricultural production quality and food supply security. However, IAMS encounters a particular challenge: due to the limited computational capacity of agricultural equipment and the scarcity of available training datasets, general detection models struggle to achieve anticipated performance in agricultural environments, especially when dealing with small-scaled targets. In this paper, we propose a diffusion learning detector named CropDetdiff, designed for disease detection in agricultural environments where training samples are limited. We have developed a backbone network based on dynamic convolutional kernels, which also embeds a graph attention network in its final stage, enhancing feature extraction capabilities for small targets. Additionally, we employ a fuzzy label assignment strategy to balance positive and negative samples and devise a new loss function, HIoU, to supervise the detection of small targets. A series of experiments on two crop disease datasets validate that CropDetDiff achieves an optimal balance between detection performance and operational efficiency. Specifically, the proposed model has achieved a mean Average Precision (mAP) of 73.2% and 40.1% on the two datasets, improving by at least 3.7% and 2.9% over other detectors, respectively, and there is a significant improvement in small targets particularly, thus achieving optimal performance. Additionally, the proposed model exhibits commendable performance in terms of parameter quantity and execution velocity. These results substantiate the efficacy and stability of CropDetDiff, underscoring its suitability for real-world applications within IAMS.

Deep nutrients and soil fungal communities support tomato fruit yield and quality in dry farm management systems

Changing climates are causing agricultural water shortages at unprecedented scales and magnitudes, especially in regions historically reliant on irrigation. Identifying and understanding systems of farming that allow continuity in agricultural operations in times of water scarcity are increasingly urgent needs. Vegetable dry farming relies on winter rains stored in soils to reduce irrigation to 0–2 events per season and has become prevalent on California’s Central Coast in recent decades. Until now, this system has been unexplored in scientific literature beyond extension publications, despite its promise as a model for low-water agriculture in arid regions. Dry farm management presents a unique challenge given that low water content restricts nutrient access in surface soils, which farmers typically target for fertility management. Managing soil nutrients at depth, as well as microorganisms that help plants access nutrients and alleviate water stress (e.g. arbuscular mycorrhizal fungi, or AMF) could be crucial to dry farm success. We engaged in a collaborative research design process with farmers managing seven commercial dry farm tomato fields to identify and answer three key management questions: 1. What are the depths at which nutrients influence harvest outcomes given low water content in surface soils?, 2. Are commercially available AMF inoculants effective at improving harvest outcomes?, and 3. How does the broader fungal community change in dry farm soils, and are those changes associated with harvest outcomes? Only soil nutrients below 60 cm depth were correlated with tomato yield and fruit quality. We identified a fungal class, Sordariomycetes, as a ‘signature’ fungal group in dry farm soils that distinguished them from irrigated management and correlated with positive fruit quality, while commercial AMF inoculation showed little benefit. These findings can inform management practices that optimize fruit yield and quality, and can guide farmers and policymakers alike in efforts to minimize agricultural water use.

Waste Utilization in Horticulture: An Overview

Waste management in horticulture plays a pivotal role in achieving environmental sustainability and optimizing resource utilization. One of the primary methods for waste utilization is composting, which involves the decomposition of organic waste materials to produce nutrient-rich compost. Compost serves as an excellent soil amendment, enhancing soil fertility and promoting plant growth. Additionally, anaerobic digestion offers a valuable approach for converting organic waste into biogas, a renewable energy source that can be used for heating, electricity generation, and vehicle fuel. Furthermore, fermentation processes can be employed to convert fruit and vegetable waste into valuable products such as animal feed and biofuels. The by-products of fermentation, including yeast and enzymes, have applications in various industries, contributing to the circular economy model. Advancements in precision agriculture and smart waste management systems have revolutionized waste utilization in horticulture. Technologies such as GPS, GIS mapping, wireless sensor networks and big data analytics enable data-driven, decision-making and optimize resource allocation. Precision planting, targeted spraying, automated pruning/harvesting and variable rate irrigation are among the innovative practices facilitated by these technologies, leading to increased productivity and efficiency in horticultural operations. Moreover, waste utilization in horticulture contributes to mitigating environmental pollution and reducing greenhouse gas emissions. By diverting organic waste from landfills and incineration, the emission of methane, a potent greenhouse gas, is minimized. Instead, organic waste is repurposed into valuable resources, thereby closing the loop on waste streams and promoting a circular economy.

Coupled DSSAT and HYDRUS-1D Simulation of the Farmland–Crop Water Cycling Process in the Dengkouyangshui Irrigation District

(1) Background: Effective water management in agricultural systems poses a significant challenge, particularly in the Dengkouyangshui irrigation district. Inefficiencies and insufficient detail in water usage across crop growth stages have resulted in suboptimal water cycling. Recent infrastructure improvements and technological interventions necessitate a reevaluation of water usage, especially concerning changes in irrigation and seepage dynamics. (2) Methods: This study addresses these concerns by employing an integrated modeling approach that combines the DSSAT with the HYDRUS-1D soil hydrology model to simulate complex interactions among soil, crop growth, and irrigation practices within the district. Observational data were used to calibrate and validate the integrated model, including soil moisture, LAI, and crop yields from the 2022 and 2023 agricultural seasons. (3) Results: The simulation results strongly align with the empirical data, highlighting the ability of the model to capture the intricate dynamics of soil–water–atmosphere–plant interactions. (4) Conclusions: The soil’s retention and moisture-holding characteristics exhibited resilience during periods without water supplementation, with measurable declines in soil moisture at various depths, indicating the soil’s capacity to support crops in water-limited conditions. This study delineates water consumption by maize crops throughout their growth cycle, providing insights into evapotranspiration partitioning and quantifying seepage losses. An in-depth analysis of water balances at different growth stages informs irrigation strategies, suggesting optimal volumes to enhance efficiency during critical crop development phases. This integrative modeling approach is valuable for providing actionable data to optimize the water cycling process and improve agricultural sustainability in the Dengkouyangshui irrigation district.

Long-Term Cropping Management Practices Affect the Biochemical Properties of an Alabama Ultisol

Interest in improving the long-term sustainability of agricultural production systems has focused on identifying management practices that promote soil health. No tillage, cover cropping, and amending soils with broiler (Gallus gallus domesticus L.) litter are commonly adopted conservation practices that have been shown to improve soil fertility and crop yield. However, the overall influence of these conservation practices on soil health in the southeastern US are not well understood. Thus, a study was conducted to evaluate the influence of tillage, broiler litter (BL) applications, and cropping systems on soil biochemical properties. Soils were collected from field research plots under long-term management (>than 25 years of tillage, 15 years of broiler litter application, and 15 years of cropping system). Soil microbial biomass, C, N, and P, amidohydrolases, and dissolved organic matter (DOM) were evaluated as indicators of soil health. Adopting tillage and BL into the agricultural management system modified the biochemical parameters of the soils evaluated. Most of these modifications occurred in the 0–5 cm depth. Higher microbial biomass carbon (MBC; 85%) and nitrogen (MBN; 10%) and enzyme activities of asparaginase (65%) and glutaminase (70%) were observed in the 0–5 cm depth under no tillage (NT) compared to conventional tillage (CT), indicating greater biological activities were established in these soil ecosystems. Broiler litter applications increased microbial biomass N and activities of asparaginase and glutaminase in both soil depths. In addition, microbial biomass phosphorus (MBP) was increased following BL application in the 0–5 cm depth. The results suggest that long-term management of NT and BL additions can improve the health of eroded southeastern US soils by altering the soil biochemical parameters.

Soil structure and solute transport pathways in biogas digestate-amended soils

Here, we investigated the effects of biogas digestate application on flow pathways and soil properties in a sandy soil aiming at unraveling phosphorus accumulation and depletion processes in fertilized soils. Dye tracer experiments were conducted to visualize and distinguish between various flow pathways, while X-ray tomography was utilized to characterize the soil structure. The dye tracer experiments revealed differences between the control and the digestate-treated group of soil profiles in the top-soils although no statistical verification was possible amid a single plot per treatment. The stained soil profiles in both treatments indicate extensive areas of homogeneous and heterogeneous matrix flow. However, the digestate-treated group showed a decrease in homogeneous matrix flow and a notable presence of preferential flow with low interaction. The application of biogas digestate impacted various soil properties, including organic carbon content, water drop penetration time, and pH levels. Accordingly, the topsoil experienced an increase in soil organic carbon, water repellency, and a decrease in pH levels. The fraction of macropores also increased in the topsoil of the digestate-treated group, accompanied by an increase in tortuosity.Double lactate-extractable phosphorus (DL-P) distribution varied across the different flow pathways, with higher concentrations detected in matrix flow and a depletion observed in preferential flow regions in both treatments. These findings confirm the close relationship between the type of flow pathway and phosphorus distribution in soils. Additionally, a significant correlation was observed between pH levels and DL-P in the digestate-treated group, indicating potential implications for phosphorus availability. Overall, the results of this study suggest that the long-term application of biogas digestate is likely to improve soil properties as related to soil organic matter content, particularly in the topsoil of sandy soils. The study highlights the significance of the prevailing flow and transport pattern on nutrient distribution, with potential implications for nutrient management in agricultural systems. While the study design presented prevents the unequivocal assignment of differences to treatment effects, our data likely illustrate the impacts of biogas digestate amendment on the investigated sandy soil.Further research should focus on assessing the long-term effects of digestate application in loamy and clayey soils.

Relationships between soil compaction and soil physical-chemical-biological characteristics: A case study from volcanic agricultural soils of Entisol and Ultisol in North Maluku Province of Indonesia

Soil compaction has been recognized as a form of soil degradation that mostly deteriorates agricultural soil health worldwide from various climatic conditions and soil management. Heavy compaction can occur in agricultural soils due to intensive tillage, where its effects on soil properties have been the focus of many studies. The present study was undertaken to assess whether soil compaction occurs in agricultural soils adopting no-tillage systems and how the link between soil compaction and chemical-biological characteristics could be. A field investigation was carried out in two different locations that have different soil types, Entisol in Maitara Island and Ultisol in Halmahera Island, North Maluku Province. Sixteen undisturbed and disturbed soil samples were collected from each location grown with cassava and taken to the laboratory for analysis. Soil components observed included soil texture, bulk density, particle density, total porosity, soil permeability, soil organic matter (SOM), total nitrogen (N), phosphorous (P) availability, and phosphate-solubilizing bacteria (PSB). The findings revealed linear relationships between soil compaction and sand fraction, SOM, PSB, and available P, but none were found in soil total N. These results confirmed the importance of soil compaction management in food crop agricultural systems, particularly when soil carbon is low, to promote soil health. As the inconsistent correlation of soil compaction and chemical-biological properties was found in these two soil types, further investigation is necessary.

Analysis of the Promotion Effect of Agricultural Economic Management on Rural Economic Development Based on VAR Model

Under the background of my country’s new rural construction and the implementation of the rural revitalization strategy, the rural economy has ushered in an unprecedented opportunity for development. Agricultural economic management plays a catalytic role in providing direction guidance for rural economic development, promoting sustainable rural economic development, and providing a good environment for rural economic development. However, there are some drawbacks in agricultural economic management, which are mainly reflected in the imperfect agricultural economic management system and the lag in information infrastructure construction. In view of this, the author puts forward corresponding the advanced VAR model from the aspects of improving the agricultural economic management system, improving the application level of information technology, and improving the quality of the agricultural economic management team. Research shows that: through effective management of agricultural economy, the obstacles to agricultural economic development can be reduced from the source, thereby promoting the healthy and sustainable development of rural economy.

A Review on Precision Agriculture: An Evolution and Prospect for the Future

A farm management system that uses information and technology to identify, analyze, and control the temporal and spatial variability within a field is known as precision farming or precision agriculture. Its goals are to maximize productivity and profitability, preserve the land resource, and minimize production costs. The public's growing environmental consciousness is forcing us to alter agricultural management techniques in order to maintain economic profitability while preserving natural resources like water, air, and soil quality. The application of inputs (such as chemical pesticides and fertilizers) in accordance with the proper amount, timing, and location. "Site-Specific Management" is the term used to describe this kind of management. With over a third of the world's food now requiring irrigation for production, the productivity increase in the global food supply has depended more and more on the expansion of irrigation schemes in recent decades. The overall economic viability of traditional agricultural systems is being challenged by market-based global competition in agricultural products, which calls for the creation of new, flexible production systems.

A novel interval estimation framework for wind power forecasting using multi-objective gradient descent optimization

Effective wind power forecasting enables reliable integration of renewable energy into the electric grid and enhances wind farm management and power system dispatch. To mitigate uncertainty in wind power forecasting, interval prediction is commonly used to provide information on confidence levels. The lower upper bound estimation (LUBE) method is a quality-driven solution that constructs prediction intervals (PIs) based on an ensemble loss function comprising two PI quality metrics without assuming any specific distribution function. However, the conventional LUBE method suffers from three major drawbacks: 1) it is incompatible with gradient descent (GD) due to its non-differentiable objective function; 2) its objective function is formulated based on qualitative assessment rather than on a statistical basis; and 3) the ensemble loss function cannot fully represent the PI quality information measured by two conflicting metrics, leading to potential performance loss. In this study, we propose a novel multi-objective gradient descent (MOGD)-based LUBE model to address these drawbacks. The loss function is derived based on the maximum likelihood estimation and is approximated by a sigmoid function to ensure its statistical basis and differentiable form. In the training process, MOGD is applied to search for the direction of GD subject to both PI metrics. The proposed model is compared with several benchmarks using four wind power datasets from different spatial locations. The numerical results indicate the superiority of the proposed MOGD-LUBE model over its counterparts in terms of the reliability and sharpness for efficient wind power forecasting.

Potato Soil Core Microbiomes Are Regionally Variable Across the Continental United States

Soil microbiomes play crucial roles in pathogen suppression, nutrient mobilization, and maintenance of plant health. Their complexity and variability across spatial and temporal scales provide challenges for identifying common targets—microbial taxa or assemblages—for management in agricultural systems. To understand how microbiomes in potato production soils vary across growing regions and identify commonly distributed taxa among them, we compiled a continental-scale bacterial and eukaryotic amplicon dataset of over 1,300 communities with corresponding edaphic measurements from nine U.S. field sites. Field site explained most of the variance across bacterial and eukaryotic (predominantly fungal) communities, while pH and organic matter as well as nitrate, phosphate, and potassium concentrations also varied with community structure. Bacterial and eukaryotic potato soil microbiomes showed consistent phylum-level composition across locations at the continental scale, with regional-scale differences evident among genera and amplicon sequence variants (ASVs). Core community analysis identified 606 bacterial and 74 eukaryotic ASVs, which were present, but unequally distributed, across all nine field sites. Many of these core ASVs belonged to common soil genera, such as Bacillus and Mortierella, which may reveal the functional potential involved in maintaining soil health across regionally variable soil systems.

Deep learning technique for plant disease detection

A nation's economy is primarily reliant on agricultural growth. However, several plant diseases seriously impair crop growth, both in terms of quantity and quality. Due to a lack of subject matter specialists and low contrast data, accurate diagnosis of many diseases by hand is highly difficult and time-consuming. The farm management system is therefore looking for a method for automatically detecting early illnesses. To overcome these challenges and correctly classify the different diseases, an efficient and small deep learning-based framework (E-GreenNet) is proposed. A MobileNetV3Small model is used as the foundation of our end-to-end architecture to produce finely tuned, discriminative, and noticeable features. Furthermore, the new plant composite (PC), plantvillage (PV), and data repository of leaf images (DRLI) datasets are used to independently train our proposed model, and test samples are used to evaluate its actual performance. The suggested model achieved accuracy rates of 1.00 percent, 0.96 percent, and 0.99 percent on the given datasets after a rigorous experimental study. Additionally, a comparative investigation of our proposed technique against the state-of-the-art (SOTA) reveals extremely high discriminative scores.

Deep learning technique for plant disease detection

A nation's economy is primarily reliant on agricultural growth. However, several plant diseases seriously impair crop growth, both in terms of quantity and quality. Due to a lack of subject matter specialists and low contrast data, accurate diagnosis of many diseases by hand is highly difficult and time-consuming. The farm management system is therefore looking for a method for automatically detecting early illnesses. To overcome these challenges and correctly classify the different diseases, an efficient and small deep learning-based framework (E-GreenNet) is proposed. A MobileNetV3Small model is used as the foundation of our end-to-end architecture to produce finely tuned, discriminative, and noticeable features. Furthermore, the new plant composite (PC), plantvillage (PV), and data repository of leaf images (DRLI) datasets are used to independently train our proposed model, and test samples are used to evaluate its actual performance. The suggested model achieved accuracy rates of 1.00 percent, 0.96 percent, and 0.99 percent on the given datasets after a rigorous experimental study. Additionally, a comparative investigation of our proposed technique against the state-of-the-art (SOTA) reveals extremely high discriminative scores.