Agricultural Production Management Research Articles

Сучасні аналітичні інструменти відповідального ведення бізнесу та впровадження стандартів єс для сталого розвитку в агропродовольчому секторі

Directions for ensuring the agricultural sector’s functioning in full-scale war conditions require modern analytical tools. They should be aimed at preserving the chain’s efficiency: “production – processing – storage – supply of food products to the population”. In the conditions of post-war recovery, by EU requirements, it is necessary to provide a digital platform for business analytics and subsequently diversify the development of agricultural production based on sustainable development, environmental friendliness, and circularity, increase the capitalization and investment attractiveness of enterprises, the formation of competitive market institutions, which will involve increasing the efficiency of agricultural production management of resources, strengthening of food security of the state, development of multi-organization, increase of export of goods with higher added value, provision of comfortable living conditions in the countryside. The significance of implementing the EU directive on the proper verification of the sustainable development of agricultural and food enterprises, considering environmental and social standards, cannot be overstated. This directive provides a framework for due Diligence tools, which are crucial for checking the economic activity of a food company and stages of Due Diligence diagnostics, measures for assessing the business reputation of a food company. In the work, a thorough analysis and assessment of the risks that arose during the martial law in Ukraine was carried out, an example of a Due Diligence B2B case is given – the merger of agro-food companies in the agricultural sector. The components of technical, commercial, operational and ESG verification, risk management components, business reputation assessment and predictive modeling of possible future scenarios and financial support and their reflection in the annual sustainable development report are covered. The introduction of the EU Directive on due diligence of the sustainable development of enterprises (Corporate Sustainability Due Diligence Directive) creates new challenges and opportunities for Ukrainian agri-food companies. To maintain access to the European market and strengthen business reputation, enterprises must implement sustainable development strategies that must meet environmental standards and respect human rights. This will contribute to strengthening their positions in the international arena and increasing competitiveness. Keywords: agro-food sector, sustainable development reporting, European standards, Due Diligence, post-war recovery, environmental standards, social standards, investment attractiveness, risks, business reputation, business analytics.

Using the MSFNet Model to Explore the Temporal and Spatial Evolution of Crop Planting Area and Increase Its Contribution to the Application of UAV Remote Sensing

Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in real time to help precision agriculture management. It is widely used in crop monitoring, yield prediction, and irrigation management. However, the application of remote sensing technology faces challenges such as a high imbalance of land cover types, scarcity of labeled samples, and complex and changeable coverage types of long-term remote sensing images, which have brought great limitations to the monitoring of cultivated land cover changes. In order to solve the abovementioned problems, this paper proposed a multi-scale fusion network (MSFNet) model based on multi-scale input and feature fusion based on cultivated land time series images, and further combined MSFNet and Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) to optimize the parameters of the neural network. The proposed method is applied to remote sensing of crops and tomatoes. The experimental results showed that the average accuracy, F1-score, and average IoU of the MSFNet model optimized by PSO + MAML (PSML) were 94.902%, 91.901%, and 90.557%, respectively. Compared with other schemes such as U-Net, PSPNet, and DeepLabv3+, this method has a better effect in solving the problem of complex ground objects and the scarcity of remote sensing image samples and provides technical support for the application of subsequent agricultural UAV remote sensing technology. The study found that the change in different crop planting areas was closely related to different climatic conditions and regional policies, which helps to guide the management of cultivated land use and provides technical support for the realization of regional carbon neutrality.

Comparison of Nonlinear Functions to Define the Growth in Intensive Feedlot System with XGBoost Algorithm

The aim of this study was to define the growth by using nonlinear functions in intensive feedlot system with XGBoost algorithm. To achieve this aim, five nonlinear functions were implemented. To implementation of the study, Brown Swiss (n=41) and Simental (n=95) breed were used. Each nonlinear functions were examined for each breed. According to the results of the nonlinear functions, logistic model was the best prediction model for defining the growth of each breed. In this study, the parameters in the best prediction model were calculated individually and the relationship of these parameters with body weight was evaluated with the XGBoost algorithm. Model comparison criteria such as standard deviation ratio (SDratio), Pearson’s correlation coefficient (PC), determination of coefficient (R2) and Akaike’s information criteria (AIC) were used to evaluate the XGBoost algorithm. In conclusion, the XGBoost algorithm can be an effective and optional approach that allows breeders to estimate live weight from growth parameters. This algorithm can operate on large data sets with high accuracy and speed, leading to significant improvements in agricultural productivity and animal health management. XGBoost enables more accurate predictions by analyzing the effects of various characteristics (e.g., nutritional level, breed, age). Therefore, this method can be used to determine critical parameters such as body weight in animal breeding practices, serving as a powerful support tool for operational decisions.

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.

Semantic segmentation for plant leaf disease classification and damage detection: A deep learning approach

Agriculture sustains the livelihoods of a significant portion of India's rural population, yet challenges persist in manual practices and disease management. To address these issues, this paper presents an automated plant leaf damage detection and disease identification system leveraging advanced deep learning techniques. The proposed method consists of six stages: first, utilizing YOLOv8 for region of interest identification from drone images; second, employing DeepLabV3+ for background removal and facilitating disease classification; third, implementing a CNN model for accurate disease classification achieving high training and validation accuracies (96.97 % and 92.89 %, respectively); fourth, utilizing UNet semantic segmentation for precise damage detection at a pixel level with an evaluation accuracy of 99 %; fifth, evaluating disease severity; and sixth, suggesting tailored remedies based on disease type and damage state. Experimental analysis using the Plant Village dataset demonstrates the effectiveness of the proposed method in detecting various defects in plants such as apple, tomato, and corn. This automated approach holds promise for enhancing agricultural productivity and disease management in India and beyond.

Downscaling soil moisture in regions with high soil heterogeneity: the solution based on ensemble learning with sequential and parallel learner

Soil moisture plays an important role in the water and heat exchanges between the land surface and atmosphere, and it has great importance for agricultural production, ecological planning, and water resources management. Although microwave remote sensing has been widely used in large-scale soil moisture monitoring, the accuracy of the downscaled retrieval results cannot be guaranteed for regions with high vegetation coverage and high soil heterogeneity. To address these challenges, this study built soil moisture indice set based on MODIS and elevation data by calculating the Pearson correlation coefficient (R) and Maximum Information Coefficient (MIC), then constructed decision tree models (Gradient Boosting Decision Tree and Random Forest) about the indice set and low-resolution Soil Moisture Active Passive (SMAP) by using two ensemble learning methods (Bagging and Boosting). The models were applied to the high-resolution soil moisture indices in Jilin Province for the years 2017 to 2020 to generate 1 km-resolution products. In the validation process, Triple Collocation Analysis (TCA), comparison of soil moisture maps with coarse and fine resolution, and in-situ measurements in Lishu County, Tongyu County, and Jilin City were used to evaluate the differences between downscaling soil moisture results and ground observations at network, seasonal and point scales. The results were as follows: (1) The correlation coefficient (R2) calculated by the TCA method was 0.733 (GBDT_36km) > 0.649 (RF_36km), and the error variance was 0.0004 (GBDT_36km) < 0.00058 (RF_36km). (2) R at network scale was 0.798 (GBDT_SM) > 0.662 (RF_SM), RMSE was 0.040 (GBDT_SM) < 0.044 (RF_SM), the point scale R was 0.864 (GBDT_SM) > 0.833 (RF_SM), RMSE was 0.029 (GBDT_SM) < 0.039 (RF_SM). The R in four stages of the growth period was GBDT_SM > RF_SM, RMSE was GBDT_SM < RF_SM. In conclusion, the GBDT and RF models can reliably downscale soil moisture in Jilin Province, and the Boosting ensemble learning method represented by GBDT had a better estimation performance.

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.

The trade-offs effect of ecosystem health and socio-economic development on tea production

Ecosystem health (EH) maintenance and improvement are important goals in environmental and ecological management, and help realize regional sustainable development goals. EH assessment and the regulatory mechanism have remained popular research fields in landscape ecology science. However, the improvement in EH assessment methods is ongoing, the influences of EH and socioeconomic development on agricultural production remain unclear. In this study, we chose Fujian Province in China as the study area, first quantitatively assess EH based on the Vigor–Organization–Resilience–Services (VORS) model, and thereafter explore the response of tea production to EH and socioeconomic development using the structural equation model (SEM). We found that the mean EH value in Fujian Province was 0.52 in 2010, which was 6.12% and 10.64% higher than that in 2000 and 2020, respectively. The high-EH-value regions were distributed in the northwest, while the southeastern regions exhibited low-EH-value agglomerations. According to the temporal evolution characteristics of EH, the kernel density curve patterns exhibited single peaks, and the curves shifted from a broad to a narrow peak from 2000 to 2020, indicating a trend toward a more balanced distribution of EH spatially. SEM results showed a significant positive effect of EH on tea production with a coefficient of 0.86. However, there is a significant trade-off between EH and economic development, as indicated by a coefficient of −0.6. There are also negative indirect effects on tea development when mediated by economic development. However, the overall effect of EH on tea production is 0.8, indicating that EH is still effective in promoting tea production. The results of this study have implications for the integration of ecosystem management and agricultural production management in the region.

Analisis Manajemen Rantai Pasokan Produk Pertanian Pasca Panen

This study aims to analyze the supply chain management of post-harvest agricultural products with a focus on increasing efficiency and reducing post-harvest losses. The research subjects are farmers in Cipayung Mas Ciputat, South Tangerang, Banten, Indonesia. This research was conducted in May 2024. The research method used is a qualitative descriptive method with a case study approach. Data were collected through in-depth interviews, field observations, and document analysis. Research participants include farmers, collectors, traders, and distributors. Data processing was carried out using thematic analysis to identify patterns in supply chain management and factors affecting post-harvest losses. The results of the study show that effective supply chain management can significantly reduce post-harvest losses and increase farmers' profits. Key factors identified include efficient storage, rapid distribution, and the use of modern technology in post-harvest processing. The study also found that training and education for farmers on good storage and distribution techniques are essential to improve the quality and quantity of produce reaching the market. This research provides recommendations for stakeholders to invest in storage infrastructure and processing technology, as well as to conduct regular training programs for farmers. By implementing better supply chain management, it is expected to improve farmers' welfare and regional food security

Agricultural economic security under the model of integrated agricultural industry development

As globalization and technological advancements progress, the integrated agricultural industry development model has become crucial for agricultural modernization. This model fosters inter-industry integration, enhancing agricultural competitiveness and sustainability. However, it also introduces new challenges, such as increased risk spillover and outdated regulatory mechanisms. This study employs a vector autoregression (VAR) model and a directed weighted risk spillover network for a quantitative analysis of risk dynamics within agricultural markets, identifying pathways and influencers of risk transmission across industry segments. A blockchain-based regulatory framework is proposed to improve traceability, regulatory efficiency, and data security in agricultural product management. This includes the development of registration processes, smart contracts, consensus mechanisms, and data upload protocols. The research expands the theoretical base of agricultural economic security and offers practical policy guidance, with substantial academic and practical implications. It highlights how blockchain can address market trust issues, mitigate information asymmetry, and reduce potential risks, contributing to the healthier development of agricultural economy.

Soil Physicochemical Properties under Selected Avocado Cultivars in Ethiopian Smallholder Agroforestry

This study investigated the impact of three avocado cultivars on selected soil physicochemical properties in Central Ethiopia, to enhance the knowledge on the influence of avocado cultivars on soil physicochemical properties, and assist smallholders in cultivar selection in agroforestry. Trees planted in farmers´ fields 8 years earlier were revisited. Soil samples were collected from 0–20 cm and 20–40 cm depth at three radial distances from trees (1 m and 2 m from tree trunk, and at 5 m from the canopy edge as a control). Soil texture, bulk density, moisture content, pH, electrical conductivity, soil organic carbon, total nitrogen and available phosphorus were determined. Soil moisture content and electrical conductivity were overall higher and bulk density was lower (p < 0.05) under the canopies of the avocado trees than in the control and soil organic carbon, total nitrogen and available phosphorus tended to increase in spite of nutrient inputs to the control whilst the trees were unfertilized. Differences between the studied cultivars were small, but Hass tended to have the largest impact on soil nutrient levels, whilst Ettinger and Nabal tended to have a somewhat larger effect on the soil organic carbon concentration. Integrating these avocado cultivars on farms can improve soil fertility in the study area. However, for optimal agricultural soil health and sustainable avocado production, cultivar and site-specific soil management practices must be applied.

Enhancing zinc biofortification and mitigating cadmium toxicity in soil–earthworm–spinach systems using different zinc sources

Cadmium (Cd) pollution poses significant threats to soil organisms and human health by contaminating the food chain. This study aimed to assess the impact of various concentrations (50, 250, and 500 mg·kg–1) of zinc oxide nanoparticles (ZnO NPs), bulk ZnO, and ZnSO4 on morphological changes and toxic effects of Cd in the presence of earthworms and spinach. The results showed that Zn application markedly improved spinach growth parameters (such as fresh weight, plant height, root length, and root-specific surface area) and root morphology while significantly reducing Cd concentration and Cd bioconcentration factors (BCF-Cd) in spinach and earthworms, with ZnO NPs exhibiting the most pronounced effects. Earthworm, spinach root, and shoot Cd concentration decreased by 82.3 %, 77.0 %, and 75.6 %, respectively, compared to CK. Sequential-step extraction (BCR) analysis revealed a shift in soil Cd from stable to available forms, consistent with the available Cd (DTPA-Cd) results. All Zn treatments significantly reduced Cd accumulation, alleviated Cd-induced stress, and promoted spinach growth, with ZnO NPs demonstrating the highest Cd reduction and Zn bioaugmentation efficiencies compared to bulk ZnO and ZnSO4 at equivalent concentrations. Therefore, ZnO NPs offer a safer and more effective option for agricultural production and soil heavy metal pollution management than other Zn fertilizers.

Fuzzy Logic Controller-based Intelligent Irrigation System Using Solar Radiation Data

Solar radiation is a critical factor influencing agricultural productivity and water resource management. Irrigation systems play a pivotal role in maintaining crop health and yield, and optimizing their operation requires accurate solar radiation data. This abstract explores the significance of solar radiation data in enhancing the efficiency of irrigation systems. Irrigating agricultural fields using an intelligent information system plays a crucial role. This study introduces an irrigation control system employing closed-loop control to use the available water resources efficiently. Continuous data collection from field sensors was done and transmitted to a central station in wireless mode. The data was then retrieved and processed in a computer-based or microcontroller-based solution model, enhancing system autonomy, reliability, and cost-effectiveness. Weather conditions are translated into fuzzy set values. The pump, water outlet valves, and sprinklers are set into motion according to control commands. This research simplifies the need for manual labor and reduces water wastage.

Explorando la eficiencia técnica de la producción agrícola en tres regiones de México

This work assesses the technical efficiency (TE) in agricultural production under irrigation of corn, alfalfa, sorghum, and plantain; the analysis is conducted for three hydrological subregions of Mexico: Río San Juan (north), Valley of Mexico (center) and Valle Bajo de Grijalva (South). The measurement was carried out by agricultural cycle (spring-summer, autumn-winter and perennial), and by land size. We use stochastic frontier analysis to corroborate whether the farmer's skills favor efficiency, with a production function that relates irrigated land as a basic element, and a set of explanatory socioeconomic variables. Despite limitations in the data, the results contribute to the knowledge of TE in Mexican agriculture. Indirectly, the contribution of irrigation is recognized, so identifying their economic relevance could help define strategies to improve agricultural production and support water management in agriculture.

Study of an auxiliary trimming device for cracked carrots

AbstractCracked carrots account for a large proportion of defective carrots, which are usually discarded or used as feed, resulting in resource waste and economic benefit loss. Cracked carrots can be used as raw materials for further processed carrot products after being trimmed. So, an auxiliary device was developed combining PLC (Programmable Logic Controller) and machine vision to achieve clamping and crack defect localization for intelligent trimming of crack defect. The carrot was orientated based on the area method, rotated by flexible jaws and a rotating cylinder to make the carrot head and tail aligned. The clamping device could non‐destructively clamp carrot in axial and radial direction with a force threshold of 10 and 22 N respectively, and rotate carrot to make the crack defect upwards. The results showed that the recognition accuracy of carrot head and tail orientation was 98%, the average deviation of carrot centroid clamping position was less than 9 mm at a speed of 300 mm/s, the rotational positioning time was less than 3 s and the number of adjustments were no more than 2 at a motor speed of 180 rpm, which could meet the subsequent trimming requirements.Practical applicationsTrimming the cracked area of carrots can reduce bacterial growth and decay, which is beneficial for subsequent processing and reduces resource waste. This article designs a carrot auxiliary trimming device, which uses image processing technology to recognize the orientation of the head and tail of carrots and the cracked area, and uses automated equipment to achieve stable clamping of carrots and make the cracked area face the trimming tool. This study is beneficial for the refined management of agricultural products.

Machine learning and genetic algorithm for mapping soil available phosphorus in coastal provinces in Southeast China

Soil available phosphorus (SAP) is a crucial nutrient for sustaining plant growth, and accurate prediction of its spatial distribution is vital for agricultural production and environmental management. Here, we comprehensively integrate multiple environmental variables impacting the phosphorus cycle, including climate, remote sensing imagery, vegetation indices, topography, and anthropogenic factors. Employing two geostatistical methods, three machine learning algorithms, and incorporating genetic algorithm (GA), the research aims to precisely forecast SAP content and generate spatial distribution maps. The GA successfully identifies the optimal indices of environmental variables, comprising Band3, Brightness Index, land use type, distance to water, and mean annual precipitation. In performance evaluation, the combination of GA with Gradient Boosting Decision Tree exhibits the best results (R2 = 0.60, MAE = 2.79, RMSE = 3.64). High-value SAP areas are predominantly located in the western region, demonstrating a correlation with land use patterns. Validating the model’s applicability in coastal provinces in Southeast China, covering Fujian, Zhejiang, and Guangdong, underscores the profound insights of this study into the dynamics and geographical correlations of SAP. Our findings demonstrate that GA exhibit outstanding stability and performance in mapping SAP, contributing to a better understanding of SAP dynamics, supporting improved soil management practices and sustainable agriculture.

Gamma-rays and X-rays spectrometries applied to evaluate soil redistribution

This work responds to the growing global demand for food, which requires improvements in agricultural production and sustainable management of natural resources. The focus is on soil erosion as a critical element in preserving agricultural productivity. From this perspective, the levels of radionuclides and chemical elements present in the soil, quantified through Gamma-Rays Spectrometry (GRS) and Energy Dispersive X-ray Fluorescence (EDXRF), were used to investigate soil redistribution over time. 27 soil samples ranging from 0 to 30 cm in depth were collected in an agricultural plot located in southern Brazil. Quantitative analysis indicated high mean concentrations of Fe (161 ± 7 gkg−1), Al (110 ± 17 gkg−1), Ca (2.6 ± 0.5 gkg−1), Mn (2.4 ± 0.3 gkg−1) and K (543 ± 165 mgkg−1) in comparison with the other detected elements. The quantification of 137Cs provided a mean inventory of 27 ± 17 Bqm−2. Using the proportional model, an estimated gross erosion rate of 28.2 tonha−1year−1 and a net soil deposition of 6.6 tonha−1year−1 were calculated. Therefore, a net soil loss of 21.6 tonha−1year−1 was experienced within the agricultural plot studied. The data set combination of both techniques with Principal Component Analysis (PCA) revealed correlations between the variables studied and the soil erosion dynamics. The PCA showed a tendency to separate the samples according to their sampling depth. Moreover, 137Cs behavior in soil proved to be similar to the behavior of elements found in fertilizers, like K. On the other hand, the individual influence of 137Cs was not enough to cause significant changes in the samples distribution in the scores plot, highlighting EDXRF as a promising technique to complement soil erosion studies.

Implementation and Evaluation of Spatial Attention Mechanism in Apricot Disease Detection Using Adaptive Sampling Latent Variable Network.

In this study, an advanced method for apricot tree disease detection is proposed that integrates deep learning technologies with various data augmentation strategies to significantly enhance the accuracy and efficiency of disease detection. A comprehensive framework based on the adaptive sampling latent variable network (ASLVN) and the spatial state attention mechanism was developed with the aim of enhancing the model's capability to capture characteristics of apricot tree diseases while ensuring its applicability on edge devices through model lightweighting techniques. Experimental results demonstrated significant improvements in precision, recall, accuracy, and mean average precision (mAP). Specifically, precision was 0.92, recall was 0.89, accuracy was 0.90, and mAP was 0.91, surpassing traditional models such as YOLOv5, YOLOv8, RetinaNet, EfficientDet, and DEtection TRansformer (DETR). Furthermore, through ablation studies, the critical roles of ASLVN and the spatial state attention mechanism in enhancing detection performance were validated. These experiments not only showcased the contributions of each component for improving model performance but also highlighted the method's capability to address the challenges of apricot tree disease detection in complex environments. Eight types of apricot tree diseases were detected, including Powdery Mildew and Brown Rot, representing a technological breakthrough. The findings provide robust technical support for disease management in actual agricultural production and offer broad application prospects.

SENTINEL-2, HERRAMIENTA EN LA SEGURIDAD AGROALIMENTARIA Y AGRICULTURA DE PRECISIÓN

&lt;p&gt;&lt;strong&gt;Background&lt;/strong&gt;. Food production as the world population increases is an issue of global importance, resulting in uncertainty around agri-food security (AS). It is essential to apply new technology that monitors and makes crop management more efficient to achieve sustainable development (SD). One technology responsible for improving conventional farmers' practices is precision agriculture (PA) using tools such as big data analytics, artificial intelligence and geospatial analysis. In this sense, the monitoring of markers through satellite images requires knowledge for adequate interpretations. Some satellites have multispectral images, monitoring with these has demonstrated high precision in predicting crop yields. There are several satellites, Landsat 8 (L8) and Sentinel-2 (S2) are the most advanced and open access. &lt;strong&gt;Objective&lt;/strong&gt;. To present the spectrophotometric fundamentals and current state of remote sensing with S2 multispectral images in relation to problems facing agriculture. It is concluded that remote sensing is a tool that contributes to the proper management of agricultural production, showing high reliability. &lt;strong&gt;Methodology&lt;/strong&gt;. This review was carried out in accordance with the PRISMA statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses), a systematic analysis of literature in the databases was carried out. MDPI, Springer Link, Science Direct, Taylor and Francis and Google Scholar were consulted. The key words were “Sentinel-2, R2, Salinity, Pest, Nitrogen, Drought”. Zotero (www.zotero.org) was used as a free access bibliography manager; articles that were not related to the research objectives were excluded. &lt;strong&gt;Results&lt;/strong&gt;. Articles related to the objective of this review were identified in the databases; the study was structured based on the needs of precision agriculture (PA). The importance of adequately managing the water demand of crops, salinity stress, pest detection and the movement of nitrogen (N) in the soil-plant-atmosphere interaction. &lt;strong&gt;Implications&lt;/strong&gt;. Remote perception of phenomena at the field level represents an area for improvement in crop management. Increasing resource efficiency is crucial to achieving agri-food security (AS) and thus part of the sustainable development goals (SDGs). &lt;strong&gt;Conclusion&lt;/strong&gt;. The S2 has multispectral images that, when processed, allow obtaining information about the crop, i.e. monitoring stress, humidity in plants and optimization of plant nutrition. With PA, decisions are made for crop management, costs are reduced by reducing losses due to crop stress, and resource efficiency increases.&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;

Women and Agricultural Entrepreneurship in Rural Areas in West Africa: Case of Benin

This research identified factors that can influence the forms of women&amp;apos;s entrepreneurship in rural areas in Benin. The Harmonized Survey on Living Conditions of Households database carried out in 2018 was used for the study. Data in several countries (Benin, Burkina Faso, Côte d&amp;apos;Ivoire, Guinea Bissau, Mali, Niger, Senegal, Togo) and financed by the World Bank. The annual survey to monitor the living conditions of households is part of the perspective of a better knowledge of poverty in WAEMU member countries, in order to understand its manifestations. This Data were submitted to a multinomial logistic regression model and, rural women in agricultural entrepreneurship were categorized into three groups based on sources of funding: entrepreneurship with access to credit (1.70%), entrepreneurship in association (17.88%), and entrepreneurship with equity (80.42%). The regression model identified factors such as socio-cultural affiliation, religion, geographical location, age, marital status, and the mother&amp;apos;s branch of activity as social factors that determine the choice of forms of entrepreneurship. In addition to these factors, the study identified some constraints on women&amp;apos;s agricultural entrepreneurship such as lack of employment, lack of access to land, level of education, access to water and grazing sites, floods/droughts, poor management, and poor sales of agricultural products as exogenous factors. Improvement of these factors could promote women&amp;apos;s agricultural entrepreneurship in rural areas.