Agricultural Food Security Research Articles

An efficient corn leaf disease prediction using Adaptive Color Edge Segmentation with Resnext101 model

Corn leaf disease prediction is essential in ensuring agricultural productivity and food security. Although classical segmentation and classification techniques have been used to identify diseases, they often fail to deal with complex leaf structures. Thus, deep learning has attracted growing attention for automatically learning discriminative features from data. This paper proposes a new approach to predicting corn leaf disease using deep learning and classical segmentation techniques for improved accuracy and efficiency. The proposed work presents an adaptive colour edge segmentation method improved by a hill-climbing search for robust feature extraction according to the diversified nature of corn leaves. With the help of stochastic hill climbing, the segmentation process is optimized by adaptively changing parameters to get better segmentation accuracy. After that, features are extracted and provided as input into a ResNeXt-101 model for disease classification. The novel activation function in ResNeXt-101 combines the Adam optimizer with a Gaussian-based GELU to enhance nonlinearity to the smooth activation of GELU. The results obtained from experiments show that the proposed approach is practical. The ResNeXt-101 model has achieved overwhelming performance, with 99% for testing accuracy and a minimum loss of 0.01, proving its superior generalization capability and robustness. The proposed work was performed with no overfitting and free from local minima during the training process.

A survey of detecting leaf diseases using machine learning and deep learning in various crops

<p><span lang="EN-US">For agricultural productivity and food security to be guaranteed, early detection and treatment of illnesses are crucial. Machine learning (ML) and deep learning (DL) approaches can be used to precisely and successfully identify plant leaf diseases. A heterogeneous dataset comprising photos of both healthy and diseased leaves such as bacterial blights, fungal infections, and viral manifestations provides the foundation for the model building and training. Accuracy, precision, recall, and F1-score are the measures used to assess the model's performance. ML techniques are helpful in the identification and extraction of pertinent information from plant leaf pictures, whereas DL techniques in general, and convolutional neural networks (CNN), in particular, are remarkable at learning complex hierarchical representations. Therefore, DL architectures like CNN are utilized in conjunction with ML approaches like support vector machines (SVM), decision trees, and random forests to extract complicated patterns and attributes from leaf pictures. This research provides an extensive analysis of the performance and application of DL and ML approaches recently applied to the early identification of leaf diseases in different crops.</span></p>

Plant Growth Promoting Rhizobacteria (PGPR) induced protection: A plant immunity perspective.

Plant-environment interactions, particularly biotic stress, are increasingly essential for global food security due to crop losses in the dynamic environment. Therefore, understanding plant responses to biotic stress is vital to mitigate damage. Beneficial microorganisms and their association with plants can reduce the damage associated with plant pathogens. One such group is PGPR (Plant growth-promoting rhizobacteria), which influences plant immunity significantly by interacting with biotic stress factors and plant signalling compounds. This review explores the types, metabolism, and mechanisms of action of PGPR, including their enzyme pathways and the signalling compounds secreted by PGPR that modulate gene and protein expression during plant defence. Furthermore, the review will delve into the crosstalk between PGPR and other plant growth regulators and signalling compounds, elucidating the physiological, biochemical, and molecular insights into PGPR's impact on plants under multiple biotic stresses, including interactions with fungi, bacteria, and viruses. Overall, the review comprehensively adds to our knowledge about PGPR's role in plant immunity and its application for agricultural resilience and food security.

Eco-Friendly Solutions: Integrating Wild Vegetables for Sustainable Agriculture Food Security and Human Health

Eco-Friendly Solutions: Integrating Wild Vegetables for Sustainable Agriculture Food Security and Human Health

Role of Agricultural Extension in Addressing Food Security

Agricultural extension services play a pivotal role in global efforts to achieve food security, acting as a critical link between research, farmers, and policymakers. This comprehensive review examines the multifaceted contributions of agricultural extension to food security goals, drawing on theoretical frameworks, empirical evidence, and case studies from diverse global contexts. This paper explores how extension services address the four dimensions of food security: availability, access, utilization, and stability. It analyses the evolution of extension models, from traditional top-down approaches to participatory and market-led systems, assessing their relative effectiveness in enhancing agricultural productivity and food security. This review paper highlights innovative practices, including the integration of information and communication technologies (ICT), community-based approaches, and public-private partnerships, which have shown promise in overcoming persistent challenges. The case studies from both developed and developing countries provide insights into successful strategies and their impacts on crop yields, farmer incomes, and nutritional outcomes. This paper also addresses critical challenges facing extension services, including financial constraints, technological barriers, and socio-cultural issues, proposing potential solutions and policy recommendations. By synthesizing current knowledge and identifying future research directions, this review underscores the indispensable role of agricultural extension in achieving global food security goals and emphasizes the need for continued innovation and adaptation in extension practices to meet evolving challenges in the agricultural sector.

Ameliorative effects of humic acid and L-tryptophan onenzyme activity, mineral content, biochemical properties, and plant growth of spinach cultivated in saline conditions.

Salinity poses a significant abiotic stress that limits plant productivity, thereby posing a serious threat to agricultural sustainability and worldwide food security. Techniques that can overcome this problem are needed. Recent focus has been placed on employing organic substances like humic acid (HA) and amino acids, including L-tryptophan (L-TRP), to mitigate the negative effects of salt stress on cultivated plants. Accordingly, in this research, the impact of foliar applications of HA and L-TRP, both separately and combined, on the growth parameters and biochemical properties of spinach subjected to salt stress was investigated. In the present study, eight treatments (1. control, 2. salt (NaCl), 3. HA, 4. L-TRP, 5. HA + NaCl, 6. L-TRP + NaCl, 7. HA + L-TRP, and 8. HA + L-TRP + NaCl) were investigated. The study showed that salt stress markedly reduced several growth properties in spinach, including plant height, number of leaves, leaf dimensions, and both fresh and dry weight. Additionally, it significantly lowered contents of chlorophyll (a, b, and total), carotenoid, polyphenol, lutein, anthocyanin, polyphenol oxidase, glycine betaine, relative water content, and the antioxidant enzyme activities (ascorbate peroxidase, catalase, peroxidase, and superoxide dismutase). On the other hand, significant increases were observed in sodium, chlorine, potassium, sulfur, zinc, nickel, proline, malondialdehyde, and hydrogen peroxide levels of spinach with salinity. Individual and combined applications of HA and L-TRP positively influenced plant growth, relative water content, activities of antioxidant enzyme, chlorophyll, and mineral contents of spinach under both normal and saline conditions. In conclusion, the combined use of HA and L-TRP under salt stress conditions is promising in mitigating the negative impacts of salinity and can be suggested as an effective alternative approach for cultivating spinach in saline environments.

Marketing Information System for Maize Commodities in Soppeng Regency to Increase Agricultural Productivity and National Food Security

Maize in Soppeng Regency is a strategic commodity and has bright marketing prospects. Therefore, efforts to increase maize production need to receive great attention, because with sufficient availability, maize is one of the main food needs in the world after rice. It is expected that the community's need for maize can be met and the selling price remains stable. This research aims to increase the productivity of corn commodity marketing in Soppeng Regency based on the application of online marketing information systems, so that the market world can be wider and make it easier for farmers to sell their corn commodity products and become corn crop production centers, in order to increase the productivity of food crops, especially in Soppeng Regency. This study uses preliminary data on corn production from villages and sub-districts sourced from the Office of Food Crops, Horticulture, Plantations, and Food Security of Soppeng Regency which consists of 49 villages, 21 sub-districts and 8 sub-districts. The system development method uses the SDLC (System Development Life Cycle) system development technique, the application of this method starts from the field condition analysis stage, the system design stage, the system creation stage, the system testing stage, the system usage stage in the field. In addition, to meet the sustainability of the program, technical guidance on the use of the system will be provided to each user at the sub-district level. The results of this research are in the form of using an online-based corn commodity marketing information system

Characteristics of clay dispersion and its influencing factors in saline-sodic soils of Songnen Plain, China

Under the influence of salinity-sodicity stress, soil salinization and sodification tend to be severe and the dispersion of clay particles increases, which threatens agricultural production and food security. To explore the characteristics of clay dispersion in saline-sodic soils and their influencing factors, the quantitative relationship between clay dispersion indices and soil physicochemical parameters in saline-sodic soils of the Songnen Plain in China was investigated, to provide theoretical and data support for clay dispersion control in saline-sodic soils. Forty-two natural soil samples (0–0.2 m) were collected from saline-sodic soils in Songnen Plain, northeast China, and their physicochemical parameters and clay dispersion indices were determined. Correlation analysis, random forest analysis, and multivariate linear regression analysis were used in this study. The results showed that water-dispersible clay (WDC) ranged from 0.60 % to 18.38 % and total clay (TC) ranged from 4.70 % to 20.68 %. The maximum value of the clay dispersion ratio (CDR) is nine times the minimum value. In terms of turbidity, mechanical dispersion turbidity (MDT) ranged from 39.60 to 59,433 NTU, while spontaneous dispersion turbidity (SDT) ranged from 1.11 to 154.67 NTU. In terms of zeta potential, the minimum value of mechanical dispersion ZETA potential (MDZP) was −46.42 mV, and the maximum value was close to 0 mV. There was a significant correlation between soil physicochemical parameters and clay dispersion indices. Exchangeable sodium percentage (ESP) was the most important explanatory variable for CDR, followed by pH, Na+ex, HCO3-, Na+, CEC, CO32-, SAR, CROSS, and SOC, which could help construct the following multiple linear regression model: CDR=0.143+0.015*ESP −0.036*CO32- −0.006*Na+. Exchangeable sodium percentage has the strongest effects on clay dispersion among the soil parameters. Clay dispersion indices vary with soil physicochemical parameters. Compared to MDT, SDT, MDZP, and SDZP, CDR is more suitable for evaluating and predicting the clay dispersion condition in saline-sodic soils.

Management on Transfer Pricing of Farmland Based on the Supply–Demand Mismatches for Multifunction: A Case Study from China

Realizing the multifunctional value of farmland is essential for regulating the pricing of farmland transfers and stabilizing the rural land market. However, in China, the mismatch between supply and demand leads to improper resource allocation, weakens the explicit value of farmland, and causes unreasonable transfer pricing mechanisms that threaten agricultural production and food security. This study develops an analytical framework to examine the relationship between farmland multifunction and transfer pricing from a supply–demand perspective. An evaluation index system is constructed, considering the physical, value, and material quantities. This study uses the matching index method and bivariate spatial autocorrelation to analyze the supply–demand match of farmland multifunction from 2014 to 2021 and its relationship with transfer prices. Additionally, management methods and strategies for dynamic zoning-based pricing under multifunctional matching trade-offs are proposed. The results show that: (1) There is significant heterogeneity in the supply and demand matching degree of different farmland functions in both space and time. The production and ecological functions of farmland are oversupplied, while the living functions are undersupplied. (2) Different spatial autocorrelation relationships exist between the degree of supply and demand matching of farmland functions and farmland transfer prices. Specifically, the supply and demand matching degrees of the production and living functions show a significant negative spatial correlation with farmland transfer prices. In contrast, the ecological function shows a significant positive spatial correlation with farmland transfer prices, which are continuously strengthening over time. (3) Based on the supply and demand matching situation of different farmland functions and the spatial autocorrelation of farmland transfer prices, nine types of regions are delineated for farmland functions. Among them, the surplus-coordinated development areas have the most cities, accounting for about 40%, with a wide distribution range. This study proposes zoning-based pricing instruments and management strategies. This research provides valuable insights for developing countries seeking to alleviate conflicts in multifunctional land use, enhance the sustainable protection of land resources, and improve land resource assessment frameworks.

Incentives-adoption-food security: Implications for sustainable cocoa production in Ghana.

This study investigates the adoption of the Cocoa Rehabilitation Programme (CRP), aiming to understand the factors influencing farmers' participation and assess the impact on food security. The research gathered quantitative data from 385 cocoa farmers in the Osino Cocoa District, Eastern region, Ghana. Mean, frequency, Tobit and ordered logit regression models were used to analyse the data. Results reveal that incentives like farm maintenance without a charge, extension access, monetary rewards regular farm monitoring and free tree-cutting strongly promote CRP adoption. Gender, age, education, and various incentives significantly influence adoption levels. The study revealed that practices like weedicide-free farming, zero burning, and efficient shade management play a substantial role in improving the food security of farmers. The paper contributes to existing literature by offering a comprehensive analysis of both monetary and non-monetary incentives, adoption patterns, and outcomes, particularly regarding food security in the context of cocoa production. The study suggests a multifaceted approach to enhance CRP efficacy and address the complex dynamics of sustainable agricultural initiatives.

Baculoviruses in Integrated Pest Management of Fall Armyworm, (Spodoptera frugiperda) (Lepidoptera: Noctuidae): Structure, Classification and Application

Baculoviruses are crucial biological agents in integrated pest management (IPM), particularly for controlling lepidopteran pests in agriculture. These are highly specific, eco-friendly viruses characterized by rod-shaped nucleocapsids containing a protein-encased genome. The Baculoviridae family comprises four genera: Alphabaculovirus, Betabaculovirus, Gammabaculovirus, and Deltabaculovirus, each targeting specific insect orders. Nucleo polyhedron virus (NPVs) and granuloviruses (GVs) are extensively used in pest management due to their high virulence and specificity, ensuring safety for non-target organisms. Ascoviruses are dsDNA viruses that infect Spodoptera frugiperda larvae, transmitted primarily by parasitoids. Despite high virulence, their use in pest control is limited due to ineffective oral transmission. Combining ascoviruses with other pathogens, such as Bacillus thuringiensis kurstaki (Btk), may enhance their pest control potential. The infection process begins when insect larvae ingest viral occlusion bodies, leading to systemic infection and host death. Despite their narrow host range and slower action compared to chemical insecticides, baculoviruses are invaluable in sustainable agriculture. Their application has expanded beyond pest control to include gene therapy and vaccine production. This review examines the structure, classification, infection process, and current status of baculoviruses in controlling the fall armyworm, Spodoptera frugiperda, a significant agricultural pest. By exploring their potential as a cornerstone in biological pest management, we highlight the importance of baculoviruses in developing sustainable and environmentally friendly pest control strategies. The integration of baculoviruses into IPM programs offers a promising approach to reduce chemical pesticide use, preserve biodiversity, and enhance crop protection, ultimately contributing to more sustainable agricultural practices and food security.

Impact of Climate Change on Agricultural Production and Food Security

This comprehensive analysis provides a detailed examination of the impacts of climate change on the agricultural sector, offering sustainable solutions with the objective of ensuring global food security. The detrimental impact of climate change on agricultural production, including global warming, significant alterations in precipitation patterns, and extreme weather events, has been meticulously assessed, with careful consideration given to their potential consequences for future food supply and security. In this context, a number of critical strategies and policies have been proposed, including the strengthening of water management policies, the advancement of sustainable agricultural practices, the development of climate-resilient seeds, and the enhancement of agricultural technologies. Moreover, the imperative for international collaboration and shared accountability has been forcefully emphasised, underscoring the necessity of maintaining this pivotal issue at the forefront of the global agenda. It is evident that a comprehensive approach is essential for combating climate change and ensuring sustainable agricultural production. Sustainable solutions and international cooperation are vital for guaranteeing a reliable future food supply and food security. In this context, the implementation of the proposed strategies and policies represents a crucial step towards aligning the agricultural sector with future needs and effectively addressing climate change.

Analysis of Fluorescently Labeled ISSR Markers to Reveal the Genetic Diversity of Cassava Varieties (Manihot esculenta Crantz) in the Kerala’s Tribal Area of India

The study Inter Simple Sequence Repeat (ISSR) molecular markers to genotype distinct cassava (Manihot esculenta Crantz) cultivars within tribal communities in Kerala, India. Fluorescent ISSR primers were labelled with 6-carboxyfluorescein and used for PCR amplification. The total number of bands produced was 72, of which 49 were found to be polymorphic markers; an average of 17 polymorphic bands was obtained per primer. The manuscript is a scientific validation of the traditional knowledge of tribal people regarding the diversity and wealth of cassava crops in the region, and this validation can therefore help in protecting the farmers right on a particular landrace that they cultivate. The technique used in the diversity analysis is a time-saving process as compared to conventional breeding programs for crop improvement. The data in the manuscript also supports similarity and differences of each variety to one another, which is analysed statistically with the molecular marker-based data generated. By examining genetic diversity at a high resolution, ISSR markers can provide valuable insights into the evolutionary dynamics of these cassava populations. This research contributes to the broader goal of enhancing agricultural sustainability and food security in tribal communities.

Design of a mathematical model to optimize farmer food security and promote rural development in Paraguay

Design of a mathematical model to optimize farmer food security and promote rural development in Paraguay

Peri-urban agriculture and household food and nutrition security around Eldoret, Kenya

Rapid urbanisation in low- and middle-income countries, which has encroached on agricultural lands but has not been consistently accompanied by corresponding improvements in water and sanitation services, has raised questions about its impact on the food and nutrition security of households living in transitional, peri-urban areas. Through an analysis of survey data collected from 518 households living around the town of Eldoret, Kenya, we investigate the existing links between peri-urban households’ engagement in agriculture, their dietary behaviour, and their children’s nutrition outcomes. We find that peri-urban households engaged in agriculture, particularly in crop growing and in the sales of their agricultural produce, have more diverse and nutritious diets than agriculturally non-engaged households, all other things being equal. However, a significant improvement in children’s health outcomes is observed in these households only when coupled with improved water, sanitation, and hygiene conditions.

Adapting Harvests: A Comprehensive Study of Farmers’ Perceptions, Adaptation Strategies, and Climatic Trends in Dera Ghazi Khan, Pakistan

Understanding farmers’ perceptions, attitudes, and adaptation strategies toward climate change is important for developing effective policies that support agricultural development and food security in rural communities. This study aimed to understand climatic trends over the past two decades (2003–2022), farmers’ perceptions, and adaptation strategies regarding climate change in Dera Ghazi Khan, Pakistan. The Mann–Kendall trend test identified a significant decrease in average minimum temperature (τ = −0.357, p < 0.05) and an increase in rainfall patterns (τ = 0.337, p < 0.05). A mixed-method approach is employed, utilizing a survey of 180 randomly selected farmers, focus group discussions, and climate data analysis. Ordinal and binary logistic regression models were used to analyze the influence of sociodemographic factors on farmers’ perceptions of climate change and their choice of adaptation strategies. The results indicate that farmers primarily rely on religious beliefs/prayers (65.6%) to cope with climate change, followed by seeking off-farm jobs (50%) and changing fertilizer use (42.2%). The result of the binary logistic regression indicates that among the different demographic characteristics, education, and land size significantly influence farmers’ decisions in taking adaptation measures against climate change. This study emphasizes the need for climate policies that integrate farmer knowledge with meteorological data and sociodemographic analysis to ensure a sustainable agricultural sector in Pakistan. Further research is necessary to explore more effective adaptation methods.

Linking demography and food consumption to project population growth and damage potential of Spodoptera frugiperda in India

Abstract The fall armyworm, Spodoptera frugiperda (J. E. Smith), identified as an invasive pest worldwide, has severely threatened agricultural production and food security in India. To formulate eco‐friendly integrated pest management strategies for S. frugiperda in its new invasive habitat, the basic knowledge about demographic parameters and damage potential of this pest is crucial. The effects of five host crops viz. maize (Zea mays L.), popcorn (Zea mays everta Sturt), sweet corn (Zea mays saccharata Sturt), sorghum (Sorghum bicolor (L.)) and soybean (Glycine max (L.) Merr.) on the development and food consumption of S. frugiperda were examined. Population projection curves based on life table and consumption rates were drawn on different hosts using computer simulation. The pre‐adult development of S. frugiperda was fastest on popcorn (28.02 days), maize (28.04 days) and sweet corn (28.31 days) but slowest on soybean (34.83 days). The highest net reproductive rate, intrinsic rate of increase and finite rate of increase were observed on maize. The feeding potential of S. frugiperda was also highest on maize. The maximum consumption of the sixth larval instar was observed on maize (19470.47 mm2) while lowest on soybean (9033.67 mm2). In the simulation period of 90 days, the fastest growth of S. frugiperda was expected on maize, popcorn and sweet corn while the slowest on soybean. High larval and pupal survival recorded on the non‐maize hosts (sorghum and soybean) reflects that fall armyworm could be an obstacle in their production as well as in the success of maize‐soybean intercropping system. Simulations based on age‐stage, two‐sex life table could be helpful in predicting the most appropriate time for the pesticide application as well as augmentative releases of egg and larval parasitoids for the control of fall armyworm.

Climate change adaptation: Challenges for agricultural sustainability.

Climate change poses a substantial threat to agricultural sustainability globally. Agriculture is a vital component of the gross domestic production of developing countries. The multifaceted impacts of climate change on agriculture, highlighting how extreme weather events such as water stress, heatwaves, erratic rainfall, storms, floods, and emerging pest infestations are disrupting agricultural productivity. The socioeconomic status of farmers is particularly vulnerable to climatic extremes with future projections indicating significant increment in ambient air temperatures and unpredictable, intense rainfall patterns. Agriculture has historically relied on the extensive use of synthetic fertilizers, herbicides, and insecticides, combined with advancements in irrigation and biotechnological approaches to boost productivity. It encompasses a range of practices designed to enhance the resilience of agricultural systems, improve productivity, and reduce greenhouse gas emissions. By adopting climate-smart practices, farmers can better adapt to changing climatic conditions, thereby ensuring more sustainable and secure food production. Furthermore, it identifies key areas for future research, focusing on the development of innovative adaptation and mitigation strategies. These strategies are essential for minimizing the detrimental impacts of climate change on agriculture and for promoting the long-term sustainability of food systems. This article underscores the importance of interdisciplinary approaches and the integration of advanced technologies to address the challenges posed by climate change. By fostering a deeper understanding of these issues to inform policymakers, researchers, and practitioners about effective strategies to safeguard agricultural productivity and food security in the face of changing climate.

Laboratory Evaluation of Soil Moisture Sensors for Precision Irrigation in Agriculture

Water is essential for agricultural production and food security, making efficient water use critical. Accurate measurement of soil moisture is vital for scheduling irrigation, ensuring that crops receive the right amount of water at the right time. This prevents both under- and over-irrigation, conserving water and maximizing crop production. Currently, digital soil moisture sensors are used for their accuracy and instant measurement capabilities. The soil moisture sensor's functionality was evaluated through observations of four Capacitive soil moisture sensors 1.2 (SMS A, B, C, D) at various moisture levels, with gravimetric method verification. The calibrated linear equations demonstrated a strong linear relationship for each sensor. The coefficient of determination (R2) were also found to be 0.92, 0.93, 0.91 and 0.93 respectively. These R² values indicate a strong linear relationship for each sensor. An automated drip irrigation module was developed by integrating these soil moisture sensors with the Arduino platform. The system activates the irrigation motor when soil moisture content falls below the field capacity or a desired set value, and turns it off once the required moisture level is reached. This sensor-based automated drip irrigation module enables farmers to irrigate their fields precisely and efficiently, delivering the right quantity of water at the right time. The system is also suitable for deficit irrigation practices, improving crop yield and water use efficiency.

Modeling the richness and spatial distribution of the wild relatives of Iranian pears (Pyrus L.) for conservation management

The preservation of the genetic resources of crop wild relatives (CWRs) is crucial for food production systems and is considered a vital measure for global agricultural health and food security. The identification of potential areas where CWRs can thrive is one of the first steps towards their conservation. In this study, we used a maximum entropy model (MaxEnt) to determine the habitat suitability of seven wild relatives of pears (Pyrus L.) for the first time. We aimed to identify high-priority areas for conservation and determine the hotspots for rich biodiversity in Iran. The study showed excellent predictive performance for all species studied (AUC value ≥ 90). The soil depth, solar radiation, minimum temperature of the coldest month (Bio6), and precipitation of the wettest quarter (Bio16) were the main environmental factors that influenced the habitat suitability of all seven species, according to permutation importance. The projected maps revealed that P. elaeagnifolia had the largest suitable habitat area, while P. glabra had the lowest. The results also showed that less than 5% of the suitable habitats for these seven species were in protected areas. This research highlights the need for national preservation policies and the development of cultivation and rehabilitation strategies for these threatened species.