Food Security Challenges Research Articles

Efficient sorghum and maize transformation using a ternary vector system combined with morphogenic regulators.

Sorghum bicolor (sorghum) is a vital C4 monocotyledon crop cultivated in arid regions worldwide, valued for its significance in both human and animal nutrition. Despite its agricultural prominence, sorghum research has been hindered by low transformation frequency. In this study, we examined sorghum transformation using the pVS1-VIR2 ternary vector system for Agrobacterium, combined with the morphogenic genes BABY BOOM and WUSCHEL2 and selection using G418. We optimized Agrobacterium-mediated infection, targeting key parameters such as bacterial optical density, co-cultivation time, and temperature. Additionally, an excision-based transformation system enabled us to generate transgenic plants free of morphogenic regulators. The method yielded remarkable transformation frequencies, reaching up to 164.8% based on total isolated plantlets. The same combination of ternary vector, morphogenic genes and geneticin-based selection also resulted in a marked increase in transformation efficiency of the Zea mays (maize) inbred line B104. The potential for genomic editing using this approach positions it as a valuable tool for the development of sorghum and maize varieties that comply with evolving European regulations. Our work marks a significant stride in sorghum biotechnology and holds promise for addressing global food security challenges in a changing climate.

The protein transition—environmental footprints of farmed fish, microalgae and chicken for food, a case study in Kenya

AbstractChallenges of land scarcity, climate change and water shortage are contributing to escalating the shortage of protein rich food in sub-Saharan Africa. In Kenya a total of 27.8% of the population was undernourished, and a total of 72.3% were categorised as moderately or severe food insecure in 2021–2022. The need to investigate alternative protein sources with less impacts on land, water and climate change, which potentially could replace, at least some of the current production of livestock as the main protein source, is getting increasingly urgent. The main aim of this study is therefore to conduct an environmental footprint analysis of favourable protein production systems in Kenya to evaluate environmental impacts on land, water, and climate. The methodological approach used in this study is referred to as environmental footprint analysis, which is applied to assess; (1) farmed fish in ponds, (2) farmed fish in solar based affordable recirculation aquaculture systems (A-RAS), (3) dried spirulina (Arthrospira Platensis), and (4) chicken from semi-intensive farms. The results show that the most promising alternatives to cattle production are dried spirulina produced in ponds, followed by fish produced in affordable recirculation systems (A-RAS) run on solar energy. Spirulina fully replacing beef production would reduce water usage with 34.7 billion m3/year, land/water surface area with 34.8 thousand km2/year, and GHG emission with 28.2 megaton CO2eq/year. For fish farmed in A-RAS, such replacement would reduce water, land and emissions with 30.7 billion m3/year, 32.0 thousand km2/ year and 23.0 megaton CO2eq/year, respectively. This study illustrates that it is possible to tackle urgent challenges of food and nutrition security for millions of people in sub-Saharan Africa and worldwide with a sustainable protein transition towards a more sustainable, inclusive production system of small-scale farmers, if making use of alternative protein sources, such as from solar based A-RAS and microalgae.

A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.

Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population.

Food Security Challenges for Hospital Sustainability

Background: Food security and safety are becoming major concerns worldwide. Providing food security and safety in hospitals is particularly challenging since their populations are diverse in dietary and nutritional requirements and more vulnerable to foodborne illnesses. The aim of this research is to promote hospital sustainability by addressing food security and food safety. Method: This study uses a quantitative method with a bibliometric study approach. This research has 212 articles from the Scopus database. All the documents were collected simultaneously in August 2022. The selected article encompassed the years from 2017 to 2021, including document type, publication stage, source type, language, and subject area. The data was analyzed using VOSviewer and NVivo analytical tools. Results: The results of the studies on food security and safety increased from 2017-2022 with current trends in hospital sustainability, food waste, and waste management in food. Our study has identified four clusters: hospital food services, hospital sustainability, nutrition, and food safety. Food security is positively correlated with assessment, challenges, measurement, status, and sustainability (Pearson correlation score is 1). Disease control, proper waste management, and environmental health are all associated with food safety (Pearson correlation score ±0.79-0.64). Conclusion: Food safety and security are critical to achieve hospital sustainability, therefore awareness-raising efforts must continue. It is recommended that hospital stakeholders, food service providers; public and private sectors adopt policies and guidelines to disseminate information on food safety and security concerning hospital sustainability.

An Eco-friendly β-cyclodextrin/Stilbene-Integrated supramolecular material Realizes the effective treatment of bacterial diseases via enhancing the biofilm eradication and Agrochemical bioavailability

Bacterial biofilm-associated diseases have become the leading challenge for global food security. The inherently dense biofilm matrix provides bacteria with violent resistance to the host immune response and chemical attack. Current antimicrobials with weak biofilm-targeting options and inadequate foliar adhesion severely degrade the disruptor-biofilm interactions. To tackle these issues, the host–guest supramolecular technique was employed to optimize a bactericidal stilbene-decorated ingredient (StA8) by β-cyclodextrin (β-CD). Thus, a potent supramolecular bactericidal material (StA8@β-CD), owning both enhancive biofilm eradication and excellent foliar deposition properties, was successfully fabricated. This inclusion effectively disrupts pre-formed biofilms of Xanthomonas oryzae pv. oryzae (Xoo) and definitely kills planktonic and biofilm-enclosed bacteria. After aged Xoo cells for 48 h, subsequent StA8@β-CD treatment provides excellent biofilm eradication rate of 82.8 % at 15.68 μg mL−1. Mechanism researches disclose that StA8@β-CD inhibits exopolysaccharides (EPS) production, the transcription of swimming-associated genes, bacterial quorum sensing, and cellulase/amylase activity, all pivotal for biofilm formation and bacterial virulence. Also, this self-assembled material dramatically relieves droplet splash and rebound on hydrophobic rice leaves, thereby improving efficient utilization of agrochemicals. Combining these advantages, in vivo anti-Xoo experiments reveal that StA8@β-CD has excellent control efficiency of 50.8 % at 200 μg mL−1, quite superior to the thiodiazole-copper-20 %SC formulation (34.5 %). Besides, StA8@β-CD exhibits broad-spectrum bioactivities against citrus and kiwifruit cankers with outstanding control efficacies of 73.3 % and 73.7 %, respectively. This attempt provides a new direction for the exploitation of multipurpose supramolecular bactericidal materials.

Fiber-based Biomaterial Scaffolds for Cell Support towards the Production of Cultivated Meat

The in vitro production of animal-derived foods via cellular agriculture is emerging as a key solution to global food security challenges. Here, the potential for fiber-based scaffolds, including silk and cotton, in the cultivation of muscle cells for tissue formation was pursued. Mechanical properties and cytocompatibility with the mouse myoblast cell line C2C12 and immortalized bovine muscle satellite cells (iBSCs) were assessed, as well as pre-digestion options for the materials due to their resilience within the human digestive track. The fibers supported cell adhesion, proliferation, and guided muscle cell orientation, facilitating myotube formation per differentiation. A progressive increase in biomass was also documented. Interestingly, iBSC proliferation was enhanced with coatings of recombinant proteins while C2C12 cells showed minimal response. Thus, both cotton and silk yarns were suitable as fiber-based scaffolds towards cell supportive goals, suggesting an alternative path toward structured protein-rich foods via this initial stage of textile engineering for food. Biomass prediction models were generated, enabling forecasts of cell growth and maturation across various scaffold conditions and cell types. This capability enhances the precision of the cultivation process towards an engineering approach, building on the inherent benefits of hierarchical muscle tissue structure, but here via textile engineering with these initial muscle-coated edible fibers. Further, the approach offers to reduce costs by optimizing cultivation time and media needs. These approaches are part of a foundation for future scalable and sustainable cultivated meat production. Statement of SignificanceThis research investigates the use of one-dimensional fiber-based scaffolds for cultivated meat production, contributing to advancements in cellular agriculture. It introduces a method to measure changes in biomass and scaffold degradation throughout the cultivation process. Additionally, our development of biomass prediction models improves the precision and predictability of cultivated meat production. This research not only aids in scaling up cultivated meats but also enhances the use of textile engineering techniques in tissue engineering, paving the way for producing complex, three-dimensional meat structures more sustainably.

Navigating food security challenges: A comprehensive analysis, gaps, and future directions

In the context of contemporary global challenges such as the COVID-19 pandemic, geopolitical conflicts, and climate change, food security assumes particular significance, being an integral part of national security. This study aims to investigate the interplay between food security and national security systems, with a focus on identifying gaps in the literature and determining directions for further research. The study conducted a systematic literature review on food security and national security systems employing a rigorous and transparent process. The qualitative analysis is grounded in the quantitative one, encompassing studies from Scopus. The examination of the selected peer-reviewed articles revealed several methodological and thematic limitations in existing research: i Geographic imbalance: There is a predominant focus on developed countries, while food security issues in developing countries remain insufficiently studied; ii Insufficient explication: There is a lack of research dedicated to managerial and economic aspects of food security in the context of national security; iii Methodological constraints: There is a predominance of quantitative methods and retrospective/cross-sectional studies. Recommendations include developing comprehensive strategies at both global and national levels to enhance food stability and accessibility.

Al-driven Precision Agriculture: Advancing Crop Yield Prediction

This article explores the integration of Artificial Intelligence (AI) in precision agriculture, focusing on its role in advancing crop yield prediction and improving overall agricultural productivity. It examines three key areas: satellite imagery analysis, soil health monitoring, and weather data integration. The paper discusses how AI techniques such as Convolutional Neural Networks, IoT sensor networks, and ensemble methods are revolutionizing farming practices. It highlights the technical implementations of these technologies, their applications, and their significant impact on yield optimization, resource efficiency, and decision-making in agriculture. The study emphasizes the crucial role of AI in addressing global food security challenges and improving agricultural resilience to climate change.

Advanced Predictive Modeling for Peanut Production Estimation in Mozambique: Addressing Food Security Challenges through ARIMA and LSTM Approaches Aligned with SDG 2

Advanced Predictive Modeling for Peanut Production Estimation in Mozambique: Addressing Food Security Challenges through ARIMA and LSTM Approaches Aligned with SDG 2

Is Green Energy the Pathway to Sustainability? – An Explanation From the Perspective of Degrowth Theory

The discourse surrounding energy transition is intensifying due to insufficient global energy resources. Nonetheless, given that the capitalist economy prioritizes expansion and growth, it remains uncertain whether green energy solutions can genuinely pave the way towards sustainability and address the challenges of climate change, energy shortages, and food security concerns. Degrowth calls for a radical reorganization of politics and economics to reduce resource, energy consumption. This paper discusses the crisis of capitalism from multiple dimensions. It uses the Analytic Hierarchy Process (AHP) as an approach to explain degrowth and green energy. It also explores green energy in terms of three aspects of sustainable development: the profit-making industry, the high-efficiency paradox, and the re-challenge to the environment. Finally, it discusses the possible pathways for society to solve the global environmental crisis in the context of degrowth. These movements encourage critical thinking about the transition to green energy and offer a feasible pathway to sustainability without relying on growth ideology. This highlights the viability of the degrowth framework as a means to address a range of problems.

Applications of remote sensing technology in agriculture: A review of contributions to food security challenges and strategies in developing countries

In the agricultural sphere, particularly in developing countries where food security is still a major concern, remote sensing technology has also surfaced as an important tool. In this review, the usages of remote sensing in agricultural areas and its contributions to food security are surveyed; The overall framework is illustrated dynamically by indicating available image platforms that were instrumental for agriculture application paradigms. This research identifies certain areas where some selected case studies and existing literature are reviewed to serve as proof that remote sensing technology has been tremendously impactful. Areas like crop monitoring, yield prediction, or natural resource management have been very well reported by the literature review process adopted. This field analysis of related studies used highly sophisticated RS sensor data. The challenges like data availability, technological limitations, and socio-economic factors have been discussed besides the strategies, for instance, capacity building, policy interventions, and prospects for technological advancements. These results underscore the vitality and adoption of remote sensing technologies in agricultural practices and policies which is the key driver of improved food security in developing countries.

Food security and dietary patterns in the Tajik Pamirs

For mountain regions in the so-called ‘developing world’, an increase in the number of people considered vulnerable to food insecurity has been noted since the turn of the millennium. Challenging environmental settings, as well as difficult societal conditions have been identified as sources of aggravation to mountain food systems. This paper applies a case study approach, conducted in a remote settlement in the Western Pamirs of Tajikistan, to underpin these general insights with detailed accounts of the complexity of the subject under study, and examines the question of how food security plays out in a specific local setting. The goal is to illustrate how socio-ecological conditions influence the food situation in a specific location, how different parameters generate unequal access to food and effect the nutrition patterns of local households, and what dynamics these patterns exhibit over time. Empirical material was analyzed using Cannon’s food systems approach. The findings exemplify how ensuring food security is made more difficult by the concurrence of challenging conditions, that there is a relationship between the socio-economic status and the dietary patterns of individual households, and how consumption patterns are subject to fluctuations over time. Based upon this, I argue that customized strategies are vital in order to tackle food security challenges in specific settings effectively, and that differentiated, empirically grounded analyses are essential to developing such approaches.

Unlocking Innovation in Crop Resilience and Productivity: Breakthroughs in Biotechnology and Sustainable Farming

Recent advancements in agricultural biotechnology and sustainable practices are revolutionizing crop production and resilience, addressing global food security challenges. This review highlights cutting-edge innovations in plant sciences, including genetic engineering, genome editing technologies, and biotechnological interventions that enhance crop resistance to biotic and abiotic stresses. It discusses the integration of precision agriculture and digital farming tools that optimize resource use and boost productivity, providing data-driven insights for better crop management. Additionally, sustainable agricultural practices such as agroecology, organic farming, and integrated pest management are examined for their potential to reduce environmental impact while maintaining high yield. The review investigates the key practices like conservation tillage, crop rotation, and biological pest control, emphasizing their roles in enhancing soil health, biodiversity, and agricultural resilience. Significant contributions of biotechnological interventions, including genetic engineering and genome editing, are explored for developing stress-resistant and high-yield crop varieties. Moreover, the integration of precision agriculture and digital farming technologies is discussed for their potential to optimize resource utilization and improve crop management through advanced data analytics.

Biofertilizer based on <i>Agrobacterium </i>as a key to food security

Background: The concept of food security is contingent upon the fulfillment of two fundamental requirements: physiological and socioeconomic. The former comprises providing sustenance that is both safeguarded and enhanced in line with people’s nutritional needs and priorities, whereas the latter includes the mechanisms by which these needs are supplied. The objective is to ensure a state of robust and healthy well-being. As a sustainable solution, biofertilizers play a pivotal role in addressing the challenges of food security. Biofertilizers represent a cost-effective and environmentally friendly solution, with the potential to enhance soil productivity through nitrogen fixation. They also enhance crop yield by increasing the concentration of nutrients. Access to functional foods derived from biofertilizer-enhanced yields may contribute to improved dietary diversification and overall well-being. Objective: The objective is to isolate and screen the most active nitrogen-fixing bacterium as a basis to a new biofertilizer. Methods: The isolation of nitrogen-fixing bacteria was conducted using the soil sowing technique. The selection of nitrogen-fixing bacteria was based on their morphophysiological characteristics. Bacterial growth was evaluated by viable cell count. Cultivation of the selected bacterium occurred in both flasks (on a shaker) and a bioreactor to compare growth conditions. The stimulatory effect of the selected bacterium on seed germination was assessed based on the final germination rate. The selected bacterium identified through molecular taxonomy. Results: A total of 100 pure cultures were isolated, from which nitrogen-fixing bacteria were selected. The cultivation conditions for these bacteria were optimized regarding pH, temperature and process duration in a flask on a shaker. Strain NB4 exhibited the most favorable development under optimal settings. Additionally, cultivation parameters for this strain were optimized in a laboratory bioreactor. Notably, a bacterial liquid culture water solution comprising 1.5% strain NB4 facilitated complete germination of wheat (Triticum aestivum L.), seeds and produced high-quality sprouts. The 16S rRNA gene sequence of strain NB4 was submitted to the GenBank database under accession number MT670424.1, identifying it as Agrobacterium Pusense RP 1. Conclusion: This research aimed to develop an effective biofertilizer to tackle the pressing issue of food security. The project’s cornerstone was Agrobacterium pusense RP 1, nitrogen-fixing strain isolated and identified through rigorous research. Keywords: Isolation and identification of nitrogen-fixing bacteria, cultivation in bioreactor, germination of wheat seeds, Agrobacterium pusense.

Balancing the right to food and environmental sustainability: A call for holistic transformation

This paper conducts an in-depth examination of the intricate nexus between the right to food and ecosystem conservation, recognizing the fundamental importance of addressing these interlinked issues for sustainable global development. Anchored in international and national legal frameworks, the right to food is explored not only as a moral imperative but as a binding human rights obligation. The analysis spans a broad spectrum, encompassing the impact of human activities such as population growth, climate change, and intensive agricultural practices on natural resources, biodiversity, and environmental sustainability. The literature review highlights the far-reaching repercussions of food access constraints on marginalized populations, emphasizing the profound relationship between food and the environment. The study underscores the critical role of biodiversity, soil health, and climate change in shaping food security challenges, with a particular focus on the consequences of industrial agriculture. The examination of agroecology emerges as a central theme, offering a holistic approach that integrates ecological, health, social, and economic considerations into agricultural and food systems. The methodology involves a comprehensive review of published studies in the specified domains, providing a robust foundation for unraveling the complex dynamics and interdependencies between the right to food, ecosystem conservation, and the broader context of climate change and human activities. The paper puts forth a series of recommendations, emphasizing the need for policies that integrate human rights into agricultural actions, enhanced inclusivity for smallholder farmers, widespread adoption of agroecology, and educational campaigns to raise awareness. Agroecology is positioned as a key solution, not only for its contributions to climate resilience but also for its positive impact on soil health, biodiversity conservation, and long-term environmental sustainability. The paper advocates for a paradigm shift in global agricultural strategies, emphasizing integrated, sustainable approaches that prioritize inclusivity, environmental stewardship, and the well-being of present and future generations. The recommendations serve as a blueprint for policymakers, practitioners, and stakeholders to collaboratively work towards a future where food security coexists harmoniously with ecosystem conservation, ensuring a sustainable and equitable world for all. Key words: Right, Food, Ecosystem, Conservation, Sustainable, Climate, Biodiversity, Agroecology, Agriculture, Policy

Drought-Induced Agricultural and Food Security Challenges in the Baribo Basin, Cambodia

Rice production within the Tonle Sap basin is a critical driver of economic and social development in Cambodia. This region has been subject to various natural disasters, with increasing attention directed towards drought. This study aims to evaluate the impacts of drought on agriculture and food security through an in-depth case study of the Baribo basin, a sub-basin of the Tonle Sap. The analysis spans the period from 1985 to 2008, a timeframe characterized by relatively high-quality data. Drought assessment was conducted using ground observations and satellite-based products, with the Standardized Precipitation Index (SPI) and Standard Vegetation Index (SVI) employed to assess meteorological and agricultural droughts, respectively. Findings from both the SPI and SVI indicate that drought constitutes a significant natural hazard contributing to food insecurity in the study area. The highest drought intensity (DI) and drought severity (DS) were recorded during the 1993–1994 period, while the most prolonged drought duration (DD) was observed from 2002 to 2006. The year 2004 witnessed the most severe impact on rice production, with approximately 46% of the total cultivated area affected. The analysis further reveals a strong correlation between the drought duration and the extent of rice cultivation affected, as well as the overall food security in the Tonle Sap basin.

Analysis of Farmers’ Perceptions on Sealing Techniques for Runoff Harvesting Ponds: A Case Study from Burkina Faso

Water conservation in arid and semi-arid regions faces significant challenges due to low and irregular rainfall, worsened by climate change, which negatively affects rain-fed crop productivity. Various techniques, including supplemental irrigation using runoff harvesting ponds, aim to address these issues but often suffer from water loss due to infiltration, influenced by the pond liner type. This study uses a factorial analysis to assess the farmers’ perceptions of four pond sealing techniques. Using the Waso-2 method, a survey conducted in 2022 among 41 rainwater harvesting pond owners across three regions of Burkina Faso revealed that farmers prioritized impermeability and ease of maintenance over cost and availability. Concrete, scoring 16/20, was the most preferred, chosen by over 75% of farmers for its durability and resistance to weathering, despite its high cost. Geomembrane, with a score of 12/20, was valued for its waterproofing properties but had durability concerns. Clay, although cheap and available, scored 8/20 due to poor waterproofing on unstable ground. Bitumen, the least favored with a score of 6/20, was hindered by scarcity and lack of familiarity. To enhance supplemental irrigation in Burkina Faso and similar regions, waterproof concrete or durable geomembrane liners are recommended. Further research into improving bitumen and clay liners is also suggested. These findings provide key insights into farmers’ preferences, offering guidance for developing effective water conservation strategies to boost agricultural productivity and address food security challenges in the context of climate change.

Insect Production: A Circular Economy Strategy in Iceland

In this review, the multifaceted issue of food security is addressed, emphasizing the need for innovative and culturally appropriate solutions. Exploring insect livestock farming emerges as a potential remedy, offering a pathway to alleviate food insecurity and promote food sovereignty, particularly when integrated with social acceptability. Stakeholder engagement on both production and consumption fronts, coupled with sustained support, is vital for successful implementation. The expanding landscape of commercial insect farming in the West prompts questions about its broader scalability and equitable deployment, especially for vulnerable populations. Existing research gaps underscore the need for a coordinated effort across international, national, and legal frameworks to effectively integrate insect farming into existing agricultural systems. In this review, we have delved into the industrial-scale production processes of mealworms and black soldier flies (BSFs), known for their high protein content and organic waste conversion, covering small and industrial cultivation methods, offering insights into mealworm production life cycles, innovative rearing systems, and harvesting techniques. This review concludes with climate-specific recommendations for insect facilities, stressing the importance of sustainable practices, continuous research and development, effective market strategies and economic feasibilities in Iceland. In the context of escalating demand for sustainable protein sources, industrial-scale insect production emerges as a pivotal player in addressing global food security challenges.

A Summative Review of Advances in Sensor Technology for Precision Agriculture

Sensor technology has become a cornerstone of precision agriculture, offering a wide array of applications that enhance farming efficiency, productivity, and sustainability. By providing real-time data on soil health, crop growth, and environmental conditions, sensors enable farmers to optimize inputs such as water, fertilizers, and pesticides, reducing waste and improving yields. The development of advanced sensors, such as multi-spectral and hyper-spectral imaging, combined with artificial intelligence (AI) and machine learning (ML) algorithms, has revolutionized crop monitoring and disease detection, allowing for timely interventions that prevent significant losses. Additionally, the integration of wireless sensor networks (WSNs) and the Internet of Things (IoT) facilitates the seamless collection and transmission of data, enabling remote and automated farm management. This technology has proven instrumental in addressing the challenges of food security by increasing agricultural productivity and reducing input costs. Sensor technologies also promote sustainable land and water management by improving irrigation efficiency and reducing chemical runoff, thus minimizing environmental impact. Economic benefits are substantial, with farmers experiencing cost savings, increased yields, and higher profitability. Moreover, sensor technology contributes to climate-smart agriculture by improving resource use efficiency and reducing greenhouse gas emissions. However, challenges remain, including the high cost of sensor installation and maintenance, limited accessibility in remote regions, data privacy concerns, and the lack of interoperability between different sensor platforms. The future of sensor technology lies in the development of low-cost, biodegradable sensors, the increased use of autonomous and robotic platforms, and enhanced AI integration for more accurate data interpretation. Expanding the use of sensors in smallholder and developing world agriculture will be crucial for global food security and environmental sustainability, offering a promising path toward a more resilient and efficient agricultural system.

Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors

This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet’s lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions.