Management Production Research Articles

Microorganisms in beet sugar production – perspective on technological opportunities and risks

The sugar industry has long faced challenges from microorganisms during production. Preventing sugar losses and microbial-related issues such as the formation of organic acids, gases like CO2 or H2, or slimy substances continues to be key concerns. Fortunately, process parameters such as high temperatures, low pH values and limited oxygen availability create unfriendly conditions for microorganisms, and antimicrobial agents have been developed to control microbial growth. However, a proactive approach would utilize the advantageous properties of microorganisms to break down undesirable substances and promote desirable ones. For example, acidic by-products from bacterial metabolism lower the pH and thus stabilize sugar beet cossettes during pressing. Additionally, beneficial metabolic processes have been recognized, such as preferentially utilizing monosaccharides instead of sucrose or selectively degrading undesired substances (e.g., converting nitrite into elemental nitrogen). Modern sequencing techniques continue to identify unknown bacteria with the potential to improve the process. Exploring these bacteria could offer valuable insights for a better microorganism management in beet sugar production. This perspective article discusses the microbial diversity in beet sugar factories and strategies for better utilization of microbial benefits, aiming for sustainable improvements in sucrose extraction. However, extensive research is necessary to identify and characterize suitable bacterial species and how best to exploit them.

Drainage lysimeter based measurement of water requirement and crop coefficient of bread wheat under semi-arid climate of Melkassa, Ethiopia

In view of the changing climate and growing global food demand, efficient water management is crucial for sustainable agriculture. Accurate measurement of evapotranspiration is essential for determining crop water demand and consequently for designing and managing irrigation systems. This study, conducted at Melkassa Agricultural Research Center in Ethiopia, utilized a drainage lysimeter to investigate the water requirements and crop coefficients of the Kingbird wheat variety during the December to March cropping season in 2021/22 and 2022/23. The experiment involved planting Kingbird wheat both inside and outside the lysimeter and irrigating using a watering can. Neutron probe measurement monitored the water balance in the soil. The study determined an average crop evapotranspiration of 427.28 mm and a reference evapotranspiration of 471.30 mm indicating a water requirement of 4273 m³ to fully grow wheat on a hectare of land. The derived average crop coefficient values were 0.43, 0.93, 1.15, and 0.30 for the initial, mid-season, and end growth stages, respectively. Furthermore, a fifth-order polynomial function was developed to predict crop coefficient values based on days after sowing. The findings provide valuable insights for enhancing the design and management of irrigated wheat production in the region. The specific crop coefficient values determined for different growth stages are crucial for optimizing irrigation scheduling and improving water-use efficiency, contributing to sustainable wheat production in semi-arid environment.

In-depth exploration of nanoparticles for enhanced nutrient use efficiency and abiotic stresses management: Present insights and future horizons

Nanotechnology is an innovative method of elevating agricultural output without sacrificing quality due to nanoparticles (NPs) unique characteristics and numerous potential uses. It is also nature-friendly, advantageous to living organisms, and cost-effective. Sustainable agricultural practices are gaining attention on NPs and nanofertilizers (NFs) as practical substitutes for traditional fertilizers and pesticides that nanotechnology could surpass some of the issues with traditional farming methods. There should be an emphasis on cutting-edge studies of NPs applications in agriculture. This article presents a positive perspective on the mechanisms leading to the formation of NPs and their application as NFs for managing nutrients in agriculture. We also share up-to-date findings on NPs interactions with plants, the fate of NPs and potential risks associated with them in plants. The as well as on the role of NPs nanomaterials in decreasing abiotic and heavy metal toxicity stress. NFs help reduce the environmental damage caused by traditional, inorganic fertilizers. Due to their enhanced responsiveness and ability to pierce the epidermis, NFs can decrease nutrient surplus while increasing nutrient usage efficiency. It was also established that NPs are essential for protecting against abiotic stress. However, some studies have shown that NPs are harmful to higher plants because the NPs they are deposited upon the surface of cells or in the cell organelles, leading to oxidative stress symptoms. In this review article, we explore the utilization of NPs for nutrient and abiotic stress management for crop production and protection during the climate change era.

Reuse of straw in the form of hydrochar: Balancing the carbon budget and rice production under different irrigation management

Hydrochar is proposed as a climate-friendly organic fertilizer, but its potential impact on greenhouse gas (GHG) emissions in paddy cultivation is not fully understood. This two-year study compared the impact of exogenous organic carbon (EOC) application (rice straw and hydrochar) on GHG emissions, the net ecosystem carbon budget (NECB), net global warming potential (net GWP), and GHG emission intensity (GHGI) in a rice pot experiment using either flooding irrigation (FI) or controlled irrigation (CI). Compared with FI, CI increased ecosystem respiration by 23 – 44 % and N2O emissions by 85 – 137 % but decreased CH4 emissions by 30 – 58 % (p < 0.05). Since CH4 contributed more to net GWP than N2O, CI reduced net GWP by 16 – 220 %. EOC amendment increased crop yield by 5 – 9 % (p < 0.05). Compared with CK, hydrochar application increased initial GHG emission, net GWP and GHGI in the first year, while in the second year, there was no significant difference in net GWP and GHGI between CI–hydrochar and CK. Compared with straw addition, hydrochar amendment reduced net GWP and GHGI by 20 – 66 % and 21 – 66 %; and exhibited a lower net CO2 emission when considering the energy input during the hydrochar production. These findings suggest that integrated CI-hydrochar practices would be a sustainable and eco-friendly way for organic waste management in rice production as it holds potential to enhance the NECB and SOC sequestration of rice production, while also offsetting the extra carbon emissions from organic inputs.

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics.

Crop Nutrition and Soil Fertility Management in Organic Potato Production Systems

Organic farming is a comprehensive production management system that fosters and improves the health of agroecosystems, encompassing biodiversity, biological cycles, and soil biological activity. The potato (Solanum tuberosum L.) is a crucial crop in organic farming systems, standing out as one of the most highly demanded organic products on the market. Among all crops, with potatoes, there is a very large yield gap between organic and conventional systems, attributable mainly to its intensive nutrient demands. The present review, considering the most relevant scientific literature worldwide, discusses the contemporary state of knowledge on crop nutrition and soil fertility management in organic potato crop production, analyzing the effects of animal manures, green manures, organic amendments, and biostimulants on organic potato tuber yield and quality. Overall, the main findings show a particular combination is needed to effectively maintain good soil fertility, satisfy the nutritional needs of the crop, and overcome the difference in potato yield between organic and conventional farming methods while meeting consumer demand. This combination entails using an animal manure or leguminous green manure with an organic soil amendment, and even better with a biofertilizer, such as a mycorrhizae-fungus-based one. It also emerged that more targeted studies are needed to select appropriate cultivars for organic potato farming systems to optimize this environmentally friendly production method.

Modeling climatic, terrain and soil factors using AHP in GIS for grapevines suitability assessment

AbstractThe study carried out in Matera, Italy, used multi‐criteria decision‐making techniques and geographic information systems to identify optimal area management for sustainable grape production. Terrain parameters such as temperature, pH, humidity, soil texture, slope, altitude, nutrients and precipitation were considered. ArcGIS maps were created, and the northwest part of the field was identified as a favorable area. Fuzzy maps were generated, and measurements were taken in each area to determine optimal land management. The results revealed that 51% of the area was very highly suitable for agricultural activities, and 49% was considered high suitable. Receiver operating characteristic (ROC) analysis of the AHP results demonstrated a high level of accuracy, as indicated by the area under the curve (AUC). The produced maps indicated a similar trend of increasing zone management priorities for physico‐chemicals as depth fluctuate. Additionally, results showed that remote sensing indices were the most important variables to predict physico‐chemicals zone management. The study also highlighted that the majority of the area supported plant growth due to favorable temperature and humidity conditions, with only a small portion in the northwest showing less favorable results. By identifying management zones, the study aimed to protect crops, better use of irrigation water and improve yields. This study highlights the potential of integrating satellite remote sensing, GIS technology and AHP as a valuable tool for agricultural land use planners and policy makers in identifying optimal locations for managing grape production.

Agro Active Potential of Bacillus subtilis PE7 against Didymella bryoniae (Auersw.), the Causal Agent of Gummy Stem Blight of Cucumis melo.

Microbial agents such as the Bacillus species are recognized for their role as biocontrol agents against various phytopathogens through the production of diverse bioactive compounds. This study evaluates the effectiveness of Bacillus subtilis PE7 in inhibiting the growth of Didymella bryoniae, the pathogen responsible for gummy stem blight (GSB) in cucurbits. Dual culture assays demonstrate significant antifungal activity of strain PE7 against D. bryoniae. Volatile organic compounds (VOCs) produced by strain PE7 effectively impede mycelial formation in D. bryoniae, resulting in a high inhibition rate. Light microscopy revealed that D. bryoniae hyphae exposed to VOCs exhibited abnormal morphology, including swelling and excessive branching. Supplementing a potato dextrose agar (PDA) medium with a 30% B. subtilis PE7 culture filtrate significantly decreased mycelial growth. Moreover, combining a 30% culture filtrate with half the recommended concentration of a chemical fungicide yielded a more potent antifungal effect than using the full fungicide concentration alone, inducing dense mycelial formation and irregular hyphal morphology in D. bryoniae. Strain PE7 was highly resilient and was able to survive in fungicide solutions. Additionally, B. subtilis PE7 enhanced the nutrient content, growth, and development of melon plants while mitigating the severity of GSB compared to fungicide and fertilizer treatments. These findings highlight B. subtilis PE7 as a promising biocontrol candidate for integrated disease management in crop production.

Research on raw material inventory management of liquor production enterprises based on EOQ model

With the recovery of the economy, China's liquor industry has shown strong recovery force, the scale of the liquor industry has been rising since 2018, but the output and sales volume have declined, and the competition in the liquor industry has become increasingly fierce, which has put forward higher operating requirements for various liquor companies. This paper analyzes the raw material (wheat) demand and inventory cost data of "Hengkang" liquor enterprises in the past three years, and uses the EOQ model to calculate its economic order quantity. Compared with the fixed order quantity method currently adopted by "Hengkang", it is found that the order quantity obtained by the EOQ model can reduce the inventory management cost of the enterprise. On this basis, some suggestions for inventory management of liquor enterprises are proposed.

Implementation of Large Language Models and Agricultural Knowledge Graphs for Efficient Plant Disease Detection

This study addresses the challenges of elaeagnus angustifolia disease detection in smart agriculture by developing a detection system that integrates advanced deep learning technologies, including Large Language Models (LLMs), Agricultural Knowledge Graphs (KGs), Graph Neural Networks (GNNs), representation learning, and neural-symbolic reasoning techniques. The system significantly enhances the accuracy and efficiency of disease detection through an innovative graph attention mechanism and optimized loss functions. Experimental results demonstrate that this system significantly outperforms traditional methods across key metrics such as precision, recall, and accuracy, with the graph attention mechanism excelling in all aspects, particularly achieving a precision of 0.94, a recall of 0.92, and an accuracy of 0.93. Furthermore, comparative experiments with various loss functions further validate the effectiveness of the graph attention loss mechanism in enhancing model performance. This research not only advances the application of deep learning in agricultural disease detection theoretically but also provides robust technological tools for disease management and decision support in actual agricultural production, showcasing broad application prospects and profound practical value.

Identifying the toxic mechanisms of emerging electronic contaminations liquid crystal monomers and the construction of a priority control list for graded control

Liquid crystal monomers (LCMs) are identified as emerging organic contaminations with largely unexplored health impacts. To elucidate their toxic mechanisms, support the establishment of environmental discharge and management standards, and promote effective LCMs control, this study constructs a database covering 20,545 potential targets of 1431 LCMs, highlighting 9 key toxic target proteins that disrupt the nervous system and metabolic functions. GO and KEGG pathway analysis suggests LCMs severely affect nervous system, linked to neurodegenerative diseases and mental health disorders, with toxicity variations driven by electronegativity and structural complexity of LCM terminal groups. To achieve tiered control of LCMs, construct toxicity risk control lists for 9 key toxic target proteins, suitable for the graded control of LCMs, management recommendations are provided based on toxicity levels. These lists were validated for reliability and offer reliable toxicity predictions for LCMs. SHAP analysis points to electronic properties, molecular shape, and structural characteristics of LCMs as primary health impact factors. As the first study integrating machine learning with computational toxicology to outline LCMs health impacts, it aims to enhance public understanding of LCM toxicity risks and support the development of environmental standards, effective management of LCM production and emissions, and reduction of public exposure risks.

A situational ethics and harmonization challenges in the case of EU biodiversity data governance

ABSTRACT The article discusses challenges in harmonizing biodiversity data by considering situational ethics in the valuation of data practices. Data harmonization involves technical processes for managing and governing data by linking, aligning, and binding datasets. Our study views data harmonization as a socio-technical practice encompassing both technical management and cultural contexts of data production and valuation. Using qualitative interviews, we aim to understand data harmonization through Reflexive Thematic Analysis, identifying patterns within empirical data. Our research shows data harmonization as aligning datasets and settings involving human and non-human agents within EU biodiversity data politics. Through qualitative inquiry, we explore EU biodiversity data politics as a model of biodiversity datafication, highlighting challenges and values such as learning processes for all actors, simplicity in data management, broad adoption of observation schemes, and promotion of the Western model of engaged citizenship as a good observer.

Organic management improved the multifunctionality in recolonization soil by increasing microbial diversity and function

Abstract Organic management enhances the formation of distinct and stable soil microbial communities, however, its influence on the temporal recovery of microbiome and multifunctionality of sterilized soil remains poorly understood. We used amplicon sequencing and metagenomic sequencing to investigate the effects of microbial communities in long‐term organic and conventional managed soils on restoring soil microbiome and functionality. We calculated multifunctionality of soils at days 30 and 90 of recolonization using the averaging approach. Results showed that organic management (O) significantly increased alpha diversity, niche width and network complexity of microbial community compared to conventional management (C). The alpha diversity, niche width and network complexity of microbial community in soils with organic soil suspension were significantly increased compared to conventional management at days 30 and 90 of recolonization. Soil multifunctionality of sterilized organic managed soil inoculated with organic soil suspension (OO) was 14.6% to 70.6% higher than that of the rest treatments. Macrogenomic analysis revealed that O significantly enriched functional pathways of ABC transporters, carbon metabolism, biosynthesis of amino acids, two‐component and nitrogen metabolism as well as most of the functional genes for carbon degradation, carbon fixation, nitrogen cycling and phosphorus cycles compared to C. These functional pathways and genes were also significantly enriched in soils with organic soil suspension at day 30 and 90 of recolonization. Furthermore, alpha diversity, niche width, network complexity, functional pathways and functional genes of microbiome correlated positively with soil multifunctionality. Synthesis and applications. Our results emphasize the importance of organic management induced changes in diversity, network complexity, and functionality of microbial communities for promoting recovery to soil microbial and functional losses, providing the theoretical basis for sustainable impact of organic management in agronomic production on soil microbiome and function. Read the free Plain Language Summary for this article on the Journal blog.

Advancing sustainable agriculture: the role of integrated soil-crop management in maize production

ObjectiveThis study aims to evaluate the effectiveness of Integrated Soil-Crop System Management (ISSM) and provide technical support for sustainable high yield and efficiency in regional agriculture.MethodsThe study compared the effects of no fertilization (Control), conventional farmer practices (FP), high-yield management (HY), and ISSM on maize yield and plant nutrient uptake. Measurements included grain yield, plant biomass, plant nutrient absorption, and soil nutrient content across different management strategies.ResultsOver the 12-year experimental period, a significant decline in grain yield was observed under the Control treatment, with a slight decrease in the FP treatment. In contrast, consistent yield increases were noted for the HY and ISSM treatments. The ISSM approach significantly enhanced the average yield and plant uptake of P and K by 26%, 24%, and 32%, respectively, approaching 98%, 91%, and 85% of the levels achieved in the HY treatment. Furthermore, the average use efficiency of P and K fertilizers in the ISSM treatment exceeded those in the FP treatment by 18.7% and 1.2%, respectively, and those in the HY treatment by 17.4% and 24.8%, respectively. The adoption of ISSM led to a significant increase in total and available P and K content within the 0–20 cm and 20–40 cm soil layers and enhanced the available P and K content across all aggregate size fractions within the 0–20 cm soil layer.ConclusionISSM is capable of achieving long-term high and stable yields for spring maize, enhancing the uptake and utilization of P and K in plants, and bolstering the soil’s capacity to supply these nutrients, thereby fostering the sustainable development of the entire soil-crop system.

Bio-control efficacy of selected indigenous nematophagous fungi against Meloidogyne enterolobii in vitro and on dry bean (Phaseolus vulgaris L.).

Dry bean (Phaseolus vulgaris L.) is an important commercialized field crop in South Africa for aiding in food security as a cheap protein source. However, it is highly susceptible to root-knot nematodes (RKN), Meloidogyne species. Use of indigenous nematophagous fungi as bio-control agents (BCA) of Meloidogyne nematodes is a promising research focus area. This is because indigenous fungal species are naturally part of the ecosystem and therefore compatible with other biological processes unlike most synthetic chemicals. The objective of the study was to identify indigenous nematophagous fungal BCA and establish their potential efficacy in reducing M. enterolobii population densities on dry bean with and without incorporation of compost. Screened indigenous fungal species included Aspergillus terreus, Talaromyces minioluteus, T. sayulitensis, Trichoderma ghanense, and T. viride. There were observed significant parasitism differences (P ≤ 0.05) among the BCA, with T. ghanense showing the highest egg parasitism (86%), followed by T. minioluteus (72%) and T. sayulitensis (70%). On the other hand, the highest J2 parasitism was observed on T. minioluteus (95%), followed by A. terreus and T. viride (63%). A similar trend was observed under in vivo conditions, with higher efficacy with compost incorporation. This provides a highly encouraging alternative and ecologically complementary Meloidogyne management in dry bean production.

Development of A Man-Machine Interface for Managing Photovoltaic Energy: Applied Study

Objective: Develop and evaluate a human-machine interface (HMI) that integrates advanced monitoring, forecasting, and management functionalities for photovoltaic solar energy systems, aiming to optimize energy production and operational efficiency. Theoretical Framework: This study is based on concepts of modeling and simulation, solar energy management, and problem-solving methodologies such as Soft System Methodology (SSM). Method: An applied approach was adopted using modeling, simulation, and statistical analysis techniques. The research included a bibliographic review in scientific databases, a case study, and SSM to organize and solve complex problems. 121 digital solar energy platforms in Brazil were analyzed to define the interface requirements. The interface was developed with React JS, Axios, Bootstrap v5, Apache Echarts, HTML, CSS, JavaScript, and Python libraries for forecasting models. Results and Discussion: The interface, named "Solar Smart Manager," enables efficient monitoring and management of energy production using critical data such as temperature, time of day, and solar irradiation. Tests in a real operational environment demonstrated improvements in energy management, incident response, and preventive maintenance. The functionality of validating solar radiation incidence data represents a significant contribution to the energy sector, promoting sustainability and innovation. Research Implications: The practical and theoretical implications of this research provide insights into the efficient and optimized management of photovoltaic solar energy systems, contributing to a better understanding and optimization of available solar resources. Originality/Value: This study contributes to the literature by developing an innovative interface that improves operational efficiency and solar energy management. The relevance and value of this research are evidenced by its positive impact on the energy sector, promoting sustainability and innovation.

Estimation and Management of Wind Energy in Ikorodu Lagos Lagoon, Using Weibull Parametric Measurement to Electricity Production in Nigeria

This paper is an approach on the estimation and approximation management of wind energy production around the lagoon axis of Ikorodu, Lagos state, Nigeria. The article concentrates on the availability of renewable resource such as wind to generate electrical energy in the greater Ikorodu metropolis of Lagos state Nigeria. Here, probability distribution function is used to generate the wind data. In this paper, three distinct methods are presented; Data time series analysis, Weibull probability function, and theoretical comparison with analytical concept. This research uses two important parameters for analyzing wind data: shape factor “k” and scale factor “c” from Weibull distribution function. The theoretical uses mathematical equations of popular methods such as: (a) Moment method, (b) Empirical Formula, or statistical standard deviation, (c) Peak likelihood, (d) modified peak likelihood, (e) double modified peak likelihood (f) graphical method or smallest mean square, and (g) energy sequence factor. The results obtained are tested to optimize the value from the Weibull parameters by adopting five techniques: (i) root average square error methodology, X2, power of agreement , MAPE, and RRMSE. The results expatiated on the practical and theoretical techniques design to confront the outcome of wind energy harnessed per 1.5 km2. Here, a differential optimization technique is used to determine the precision report. This serve as the basis of error litmus check existing between the wind energy determined by theory of statistical and mathematical Weibull Parametric function and the practical time-series data analysis in LSTM. Again, the wind data (speed and energy/power) were measured and recorded between January 2020 to December 2023 in the Ijede-Ikorodu Lagoon area of Lagos State. The optimized value for the shape factor k and scale factor c parametric measurement and management for maximizing the output electrical energy are obtained by using a well robust Weibull distribution function techniques and by absolutely determining and selecting the best position and location for installing the wind/wave alternators/generator. These generators come with turbines as a single unit. The measurement of the yearly average wind speed and average wind power are 10.09 ms-1 and 10.1 KWm-2, concurrently.

AI-based automatic identification and processing techniques for agricultural safety information

In the context of globalization, agricultural safety is directly linked to food security and human health. Modern agriculture’s challenge is effectively monitoring and processing vast agricultural safety information in the digital era. This study aims to achieve the automatic identification and intelligent processing of agricultural safety information within a digital media environment by applying artificial intelligence (AI) technologies. Initially, the background of AI applications in agriculture is explored, followed by an analysis of the urgent need for automated processing of agricultural safety information and an overview of current research in this field. It is demonstrated that, despite progress, existing methods still lack in-depth feature extraction and real-time capabilities of the data processing systems. A method based on multi-level feature fusion for identifying agricultural product safety is proposed to address these limitations alongside a decentralized AI Internet of Things (IoT) system for processing agricultural safety information. The multi-level feature fusion method can extract and integrate essential details on agricultural products from different dimensions and levels, thus enhancing the identification accuracy. Meanwhile, the decentralized AIoT system strengthens the efficiency and reliability of data processing, ensuring timely responses to agricultural safety information. Through deep neural networks, efficient recognition and classification of agricultural product images were achieved, enabling the automatic identification of agricultural safety information in a digital media environment. Utilizing deep learning algorithms, the system could learn and understand the characteristics of different agricultural products and accurately identify potential safety issues, such as pests, diseases, or spoilage, providing timely monitoring and management for agricultural production. The innovation of this research lies in the comprehensive utilization of multi-level data features and advanced Internet of Things (IoT) technology, significantly improving the level of intelligence in agricultural safety supervision.

Nutritional Aspects for Modulation of Poultry Immune Stimulation

The immune system's defense mechanism has been affected by the management and nutrition conditions in poultry production. In biological aspects, Bursa Fabricius and Thymus are primary lymphoid organs that defend the body against diseases. The spleen, cecal tonsils, immunoglobulins, and various forms of mucosal immunity, BALT (bronchus-associated lymphoid tissue), MALT (mucosa-associated lymphoid tissue), CALT (conjunctiva-associated lymphoid tissue), and GALT (gut-associated lymphoid tissue), are also vital to their overall health. The management program (environmental conditions, heating, lighting practices) and nutritional supplementation (feed ingredients, composition, and feed additives) significantly affect birds' immunity and performance. Dietary supplementation enhances immune system activation by improving the lymphoid tissues and beneficial immune modulators and responses. During the past fifteen years, alternative feed additives (amino acids, minerals, vitamins, probiotics, prebiotics) have impacted animal production performance and immunity. Besides, feed additives, such as probiotics, prebiotics, vitamins, minerals, and organic acids, are essential for sustaining hormonal, physiological, and immunological processes. These additives, probiotics, vitamins (A, C, D, E), mineral (selenium) supplementations, and organic acids boost the immune system's activity against microbial pathogens and physiological stress mechanisms. At the last point, this paper aims to review the current improvement in the relationship between nutrition and immunity in poultry.

Refinement of Cropland Data Layer with Effective Confidence Layer Interval and Image Filtering

Various systems have been developed to process agricultural land data for better management of crop production. One such system is Cropland Data Layer (CDL), produced by the National Agricultural Statistics Service of the United States Department of Agriculture (USDA). The CDL has been widely used for training deep learning (DL) segmentation models. However, it contains various errors, such as salt-and-pepper noise, and must be refined before being used in DL training. In this study, we used two approaches to refine the CDL for DL segmentation of major crops from a time series of Sentinel-2 monthly composite images. Firstly, different confidence intervals of the confidence layer were used to refine the CDL. Secondly, several image filters were employed to improve data quality. The refined CDLs were then used as the ground-truth in DL segmentation training and evaluation. The results demonstrate that the CDL with +45% and +55% confidence intervals produced the best results, improving the accuracy of DL segmentation by approximately 1% compared to non-refined data. Additionally, filtering the CDL using the majority and expand–shrink filters yielded the best performance, enhancing the evaluation metrics by about 1.5%. The findings suggest that pre-filtering the CDL and selecting an effective confidence interval can significantly improve DL segmentation performance, contributing to more accurate and reliable agricultural monitoring.