World Food Supply Research Articles

A Review on Plant Leaf Disease Classification Using Deep Learning

The Food and Agriculture Organization (FAO) claims that plants provide more than 80% of the world's food supply. The report (2023), found that plant diseases are responsible for an estimated 40% of global crop losses each year. Therefore, it was necessary to find a solution that detects and classifies plant diseases early in the plant growth stage, using deep learning techniques. In this review, we will discuss the techniques for detecting plant diseases using leaves that have been studied in previous years. An overview of the articles cited in this review reveals that EfficientNet outperforms all other CNN models for plant leaf disease classification. It achieves an impressive accuracy rate that surpasses even the best-performing VGG, ResNet, and DenseNet models. DenseNet is also a good option, especially when computing resources are limited.

Separation of Avian Preovulatory Follicle Granulosa and Theca Cell Layers for Downstream Applications.

Layer hens (egg-laying chickens) and broiler breeders (breeding stock for meat-producing chickens) are crucial to the world's food supply as a reliable source of protein. They are also an emerging animal model for the study of human reproductive disease. As the field of poultry research develops, the health and function of the layer hen and broiler breeder ovary will be an important point of study for both agricultural and biomedical researchers. One of the challenges presented by this emerging interest is the need for replicable techniques that all researchers can employ in ovarian specimen collection. In particular, a detailed visual process must be established to define the proper separation of the specialized granulosa and theca cell layers from hen follicles to achieve agreement and consistency among researchers. This study describes the extraction of preovulatory follicles and ovary tissue in white leghorn hens of prime reproductive age. The separation of these follicles is performed under cold, liquid conditions to congeal the yolk for easier manipulation and to prevent the follicle's own weight from tearing apart cell layers during the separation process. Once the separation is complete, the desired cell layers can be further digested for tissue culture approaches or can be cryopreserved for genomic and proteomic analyses.

Isolation, characterization, and assessment of Bacillus rugosus potential as a new probiotic for aquaculture applications

Aquaculture is an important component of the world food supply and a significant source of protein. However, this industry faces numerous problems. Including poor fish feed digestion and uneconomic nutrient utilization. This can result in unsatisfactory growth rates and poor stock performance. Utilizing probiotics, which are beneficial microbes that can enhance digestive systems and general fish health, is one possible way to address these issues. This study was designed to identify and evaluate a novel strain of Bacillus as a promising probiotic. The strain of Bacillus rugosus that was examined and coded NM007 showed promising probiotic characteristics that could help fish digest and utilize their feed more efficiently, reduce feed waste, and improve their digestive systems. B. rugosus NM007 exhibited the ability to produce digestive enzymes like protease, amylase, and lipase, which are the main digestive enzymes. It showed strong auto-aggregation activity and co-aggregation activity with Aeromonas sp. and Streptococcus sp. It also demonstrated tolerance to the presence of bile salt, acidic pH, and salinity up to 60 ppt. The sensitivity analysis towards antibiotics, hemolytic activity and the safety assessment on Nile tilapia fish (Oreochromis niloticus) confirmed the safety of this isolate. Based on the findings of this investigation and the isolate’s characterization, Bacillus rugosus NM007 could serve as a new promising probiotic bacterium for aquaculture.

Health and functional advantages of cheese containing soy protein and soybean-derived casein.

The global food system faces a challenge of sustainably producing enough food, and especially protein, to meet the needs of a growing global population. In developed countries, approximately 2/3 of protein comes from animal sources and 1/3 from plants. For an assortment of reasons, dietary recommendations call for populations in these countries to replace some of their animal protein with plant protein. Because it is difficult to substantially change dietary habits, increasing plant protein may require the creation of novel foods that meet the nutritional, orosensory, and functional attributes consumers desire. In contrast to plant-based milks, plant-based cheeses have not been widely embraced by consumers. The existing plant-based cheeses do not satisfactorily mimic dairy cheese as plant proteins are unable to replicate the functional properties of casein, which plays such a key role in cheese. One possible solution to overcome current constraints that is currently being explored, is to produce hybrid products containing soy protein and soybean-derived casein. Producing soybean-derived casein is possible by utilizing traditional genetic engineering tools, like Agrobacterium-mediated plant transformation, to express genes in soybeans that produce casein. If a cheese containing soy protein and soybean-derived casein satisfactorily mimics dairy, it presents an opportunity for increasing plant protein intake since US dairy cheese consumption has been steadily increasing. Soybeans are an excellent choice of crop for producing casein because soybeans are widely available and play a large role in the US and world food supply. Additionally, because a casein-producing soybean offers soybean farmers the opportunity to grow a value-added crop, expectations are that it will be welcomed by the agricultural community. Thus, there are benefits to both the consumer and farmer.

Recent Advances in Design and Development of Diazole and Diazine Based Fungicides (2014-2023).

With fungal diseases posing a major threat to agricultural production, the application of fungicides to control related diseases is often considered necessary to ensure the world's food supply. The search for new bioactive agents has long been a priority in crop protection due to the continuous development of resistance against currently used types of active compounds. Heterocyclic compounds are an inseparable part of the core structures of numerous lead compounds, these rings constitute pharmacophores of a significant number of fungicides developed over the past decade by agrochemists. Among heterocycles, nitrogen-based compounds play an essential role. To date, diazole (imidazole and pyrazole) and diazine (pyrimidine, pyridazine, and pyrazine) derivatives make up an important series of synthetic fungicides. In recent years, many reports have been published on the design, synthesis, and study of the fungicidal activity of these scaffolds, but there was a lack of a comprehensive classified review on nitrogen-containing scaffolds. Regarding this issue, here we have reviewed the published articles on the fungicidal activity of the diazole and diazine families. In current review, we have classified the molecules synthesized so far based on the size of the ring.

Application of genome editing in plant reproductive biology: recent advances and challenges.

This comprehensive review underscores the application of genome editing in plant reproductive biology, including recent advances and challenges associated with it. Genome editing (GE) is a powerful technology that has the potential to accelerate crop improvement by enabling efficient, precise, and rapid engineering of plant genomes. Over the last decade, this technology has rapidly evolved from the use of meganucleases (homing endonucleases), zinc-finger nucleases, transcription activator-like effector nucleases to the use of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (CRISPR/Cas), which has emerged as a popular GE tool in recent times and has been extensively used in several organisms, including plants. GE has been successfully employed in several crops to improve plant reproductive traits. Improving crop reproductive traits is essential for crop yields and securing the world's food supplies. In this review, we discuss the application of GE in various aspects of plant reproductive biology, including its potential application in haploid induction, apomixis, parthenocarpy, development of male sterile lines, and the regulation of self-incompatibility. We also discuss current challenges and future prospects of this technology for crop improvement, focusing on plant reproduction.

Pollinator response to livestock grazing: implications for rangeland conservation in sagebrush ecosystems.

World food supplies rely on pollination, making this plant-animal relationship a highly valued ecosystem service. Bees pollinate flowering plants in rangelands that constitute up to half of global terrestrial vegetation. Livestock grazing is the most widespread rangeland use and can affect insect pollinators through herbivory. We examined management effects on bee abundance and other insect pollinators on grazed and idle sagebrush rangelands in central Montana, USA. From 2016 to 2018, we sampled pollinators on lands enrolled in rest-rotation grazing, unenrolled grazing lands, and geographically separate idle lands without grazing for over a decade. Bare ground covered twice as much area (15% vs. 7) with half the litter (12% vs. 24) on grazed than idle regardless of enrollment. Bee pollinators were 2-3 times more prevalent in grazed than idle in 2016-2017. In 2018, bees were similar among grazed and idled during an unseasonably wet and cool summer that depressed pollinator catches; captures of secondary pollinators was similar among treatments 2 of 3 study years. Ground-nesting bees (94.6% of total bee abundance) were driven by periodic grazing that maintained bare ground and kept litter accumulations in check. In contrast, idle provided fewer nesting opportunities for bees that were mostly solitary, ground-nesting genera requiring unvegetated spaces for reproduction. Managed lands supported higher bee abundance that evolved with bison grazing on the eastern edge of the sagebrush ecosystem. Our findings suggest that periodic disturbance may enhance pollinator habitat, and that rangelands may benefit from periodic grazing by livestock.

ENHANCING TOMATO VARIETY SELECTION AND YIELD PREDICTION MODEL USING ADVANCED MACHINE LEARNING TECHNIQUES

One important agricultural practice that greatly contributes to the world's food supply is the growing of tomatoes. However, choosing the right tomato varieties and accurately estimating their yields are difficult undertakings that depend on several variables, such as crop statistics, climate, and geography. Advanced machine learning approaches can be used to improve prediction accuracy, feature selection, and preprocessing to overcome these problems. To optimize tomato variety selection and yield prediction, this work investigates an integrated approach that uses improved preprocessing approaches for outlier and missing information, an advanced feature selection method, and a hybrid algorithm.

Single Stage Autonomous Solar Water Pumping System Using PMSM Drive

Water supply technology has advanced significantly with the development of autonomous solar water pumping devices. These solar-powered systems offer a dependable and sustainable way to pump water in remote and off-grid areas. They are the perfect solution for solving problems with water delivery in places with restricted access to power because of their capacity to function independently and without the requirement for grid electricity. Autonomous solar water pumping systems present a viable and creative approach to a sustainable water supply at a time when demand for renewable energy solutions is only expected to increase. Sustainable and effective agricultural techniques have advanced significantly with the introduction of autonomous solar water pumping equipment. These solar-powered devices provide an economical and environmentally responsible way to address the problems of declining water supplies and growing energy expenses. Adoption of autonomous solar water pumping devices will be essential to maintaining the productivity and long-term sustainability of our world's food supply as the agricultural sector develops.

A Mobile Application for Crop Optmisation using Satellite Imagery and Disease Detection using AI

Abstract: Despite being crucial to the world's food supply, small-scale farmers do not have access to resources or contemporary technologies. This makes it more difficult to make educated decisions, which lowers yields and causes financial hardship. To help farmers overcome this obstacle, our research uses satellite data to provide them with useful insights that will enable them to increase production and make wise decisions.

Antifungal Efficacy of the crude Alkaloid, Flavonoid, and Terpenoid of Saussurea costus (Falc.) Lipschitz Roots against Aspergillus species isolated from Rice Seeds

Background: Rice is an essential component of the diets of people all over the world, and it accounts for a sizeable amount of the calories that they consume. The world's food supply suffers huge economic losses due to fungal deterioration on rice seeds. The medicinal plant Saussurea costus contains bioactive chemicals and is well-known for its many therapeutic uses. There are a number of species of Aspergillus that are considered to be diseases in vegetables and fruits. One of these species is Aspergillus niger. The purpose of this current investigation was to investigate the impact of the crude Alkaloid, Flavonoid, and Terpenoid chemicals derived from Saussurea costus' roots on Aspergillus species Purified from Rice seeds obtained from various markets in Hillah, Iraq.Methods: In vitro, antifungal activity against Aspergillus species was achieved using the food poisoning method. Three concentrations 5, 10, and 15mg/ml of each crude compound were prepared and compared to a positive control represented by Carbendazim500g/l and a negative control represented by 10% dimethyl sulfoxide.Results: An objective of the current investigation was to manage the Aspergillus species growth identified in Rice seeds by utilizing secondary compounds derived from the roots of Saussurea costus. The study's data indicated that the extracts represented by active compounds such as Alkaloids, Flavonoids, and Terpenoids obtained from Saussurea costus roots exhibited a significant decrease in the of Aspergillus species growth, particularly at a concentration of 100mg/ml, in contrast to the negative control. This effect was equivalent to that of the positive control, Carbendazim 500g/l.Conclusion: Finally, Saussurea costus roots have shown the most efficacy in controlling Aspergillus species, particularly through the presence of Alkaloid, Flavonoid, and Terpenoid chemicals at a concentration of 100mg/ml.Keywords: Antifungal activity; Saussurea costus; Alkaloids; Flavonoids; Terpenoids

Monitoring Agricultural Land Loss by Analyzing Changes in Land Use and Land Cover

The agricultural sector's output holds paramount significance for the global population, serving as an indispensable resource for survival and consumption. Consequently, alterations in agricultural landscapes bear substantial implications for the world's food supply. The objectives of this research are to investigate the depletion of agricultural land, with a specific focus on Samut Songkhram Province—an agriculturally prominent region in Thailand renowned for supplying seafood and fruits to Bangkok. By employing advanced remote sensing and change detection methods and incorporating indices like NDVI, NDWI, and NDBI, the study meticulously analyzed land-use changes. The outcomes were rigorously scrutinized through supervised classification, validated by on-site inspections, and corroborated with data from pertinent agencies. Findings revealed that Samut Songkhram had sustained its prominence in agricultural land, constituting around 70% of the province's total area over the past two decades. However, this expanse has undergone persistent transformation during the last 20 years. Notably, the most substantial surge was observed in the conversion of agricultural land to urban and developed areas, particularly in the urban zones of Amphawa District, followed by Mueang Samut Songkhram and Bang Khonthi districts. This investigation illuminates a consistent downward trend in agricultural land, a vital source of sustenance for Thailand's population and the global community. Doi: 10.28991/ESJ-2024-08-02-020 Full Text: PDF

A Raspberry Pi-Powered IoT Smart Farming System for Efficient Water Irrigation and Crop Monitoring

Water irrigation remain as a challenge to supply adequate amount of water to sustain the growth of plant and crops yield along the year in certain part of the world which are heavily affected by climate change. This scenario creates a huge risk toward the world food supply chain. Hence, the application of smart farming system is crucially important now to pave the way for a better the agriculture monitoring system to replace traditional manual monitoring labour by farmers. The smart farming system are usually equipped with environmental stimuli sensing system such as temperature, humidity, soil moisture, light intensity sensing sensors coupled with automation actuators to control the water irrigation rate for the crops in order to save water and at the same time provide adequate water supply for plant growth. The aim of using such smart farming system is to enable higher crops production and less human labour at the same time optimising resources available to minimize cost of farming. Hence, this paper aims to introduce a novel approach of a Raspberry Pi powered IoT smart farming system (ISFS) which can incorporate autonomous monitoring of plant irrigation, temperature, humidity, soil moisture and light intensity, to design a smartphone app that allows users to monitor plantation-related conditions in a user-friendly manner, and to enable automatic control of a drip irrigation system for plants based on data obtained on soil moisture, temperature and sunlight intensity. The proposed prototype with the functionality mentioned is aim to resolve the existing problem and to meet the demand of smart farming application in current era.

Fusion of AI Techniques: A Hybrid Approach for Precise Plant Leaf Disease Classification

Classification of plant leaf diseases is an important step in protecting the world's food supply and agricultural yields. There has been encouraging progress in improving the efficiency and accuracy of plant leaf disease diagnosis via the combination of deep learning methods with artificial intelligence (AI) in recent years. This research introduces a new hybrid strategy, CNN+SVM and CNN+RF, that uses deep learning techniques like Convolutional Neural Networks (CNN) in conjunction with more traditional machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM). Moreover, two hybrid variants, CNN + RF and CNN + SVM, are proposed to exploit the strengths of both paradigms synergistically. To further improve classification accuracy, the study employs Particle Swarm Optimization (PSO) as a feature selection technique. PSO optimizes the feature subset for each classification model, facilitating the extraction of the most informative features, which leads to better discrimination between healthy and diseased plants. The dataset used for experimentation consists of a comprehensive collection of plant leaf images representing various diseases across multiple plant species. Experimental results demonstrate the efficacy of the proposed hybrid approach compared to individual classification methods. The hybrid models achieve higher accuracy rates and improved generalization performance, showcasing the synergistic benefits of combining AI and deep learning techniques. Furthermore, the feature selection process through PSO contributes significantly to enhancing the classification outcomes, providing insights into the discriminative power of selected features. This research contributes to the advancement of plant leaf disease classification methodologies by offering an innovative hybrid approach that leverages the complementary strengths of AI, deep learning, and feature selection techniques. The study's findings underscore the potential for improving plant leaf disease management strategies, ultimately leading to enhanced crop productivity and sustainable agriculture. The proposed hybrid framework can serve as a blueprint for similar classification tasks in other domains, demonstrating the broader impact of synergizing different AI techniques for improved accuracy and performance. CNN+RF gives 95% accuracy, 93% precision, 96% recall and 94% F1 score, whereas CNN+SVM gives 93% accuracy, 91% precision, 94% recall and 92% F1 score.

APPLICATION OF BIOTECHNOLOGY IN AQUACULTURE

Biotechnology plays a major role in fish production through various techniques such as selective breeding , genetic engineering to enhance the growth rate and disease resistance in aquaculture . Fish culture business is fastest-growing sector of agricultural fields. The consumer demand for fish products continues to rise at an alarming rate. At the same time, wild fish stocks are declining at a fast pace due to over-fishing – a major concern. Every year, aquaculture contributes more than 18 million tons of fish and shellfish to the world's food supply. The increased public demands for fish, coupled with the dwindling natural marine habitats, have led scientists to study ways in which biotechnology can help boost fish and shellfish production. application of biotechnology allows researchers to identify and combine traits in fish and shellfish to enhance the productivity and quality . This article delves into the various ways in which biotechnology is applied to aquaculture and what the future holds for fish farming. The field of aquaculture has been revolutionized by the introduction of biotechnology. With the help of advanced genetic research, scientists have been able to enhance the growth of fish production.

Vulnerability assessment of food imports—Conceptual framework and empirical application to the case of Switzerland

The world's food supply is becoming increasingly vulnerable because of rising political and climatic crises. To identify food supply vulnerabilities at an early stage, this paper proposes a multifactorial and standardized import vulnerability index (IVI). The IVI encompasses (i) an exporter vulnerability index (EVI) consisting of four exporter-specific factors and (ii) a product vulnerability index (PVI) consisting of three product-specific factors. We applied a robust principal component analysis to derive weights to combine these individual factors into a standardized IVI. The IVI was applied to food import-dependent Switzerland from 2014 to 2019 as an empirical case study. The results showed that a large share of Swiss food imports mainly originated from neighboring countries, indicating a very low to moderate EVI. Regarding the PVI, only product groups that were imported from a small number of countries (e.g., oilseeds) or that showed a low self-sufficiency ratio (e.g., coffee) or high price volatility (e.g., meat) exhibited a high value. Moreover, the findings demonstrate that the IVI showed neither large fluctuations nor a decreasing or increasing trend. With regular updates, the proposed indicator can become a valuable monitoring tool for food supply security.

Infants’ Dietary Pattern Characterized by Ultraprocessed Foods Is Associated With Rapid Weight Gain and Overweight/Obesity Risk: National Health and Nutrition Examination Survey 2009-2018

BackgroundGlobal trends toward childhood obesity have been associated with several factors, including suboptimal infant feeding practices, the increasing availability of ultraprocessed foods in the world's food supply, and the corresponding changes in children's dietary patterns. ObjectiveTo describe infants’ dietary patterns and assess their associations with weight status outcomes in a nationally representative sample of US infants. DesignCross-sectional analyses were performed on data collected from infants participating in the 2009-2018 National Health and Nutrition Examination Survey. Participants/settingParticipants included 744 infants aged 6 to 12 months who had data from at least 1 day of valid 24-hour dietary recall data. Main outcome measuresRapid weight gain and overweight/obesity risk. Statistical analyses performedPrincipal component analysis was used to identify dietary patterns considering the energy intake of 39 Nova food subgroups (expressed in calories per day), including breast milk. Associations were evaluated using logistic regression adjusted for potential confounders. ResultsA total of 42% infants experienced rapid weight gain, and 33% were at risk of overweight/obesity. Most infants (65.5%) were started on solid foods early. Three main dietary patterns were derived. The first pattern, labeled Natural or Minimally Processed Foods, had positive loadings for a variety of natural or minimally processed foods, some processed culinary ingredients, and a few processed and ultraprocessed foods. The second pattern, labeled Infant Formula, had high negative loading for breast milk, and high positive loading for infant formula and breakfast cereal. The third pattern, labeled Ultraprocessed Foods, had negative loadings for natural or minimally processed foods and processed culinary ingredients, positive loadings for other processed foods and for a variety of ultraprocessed foods, and negative loading for infant formula. Infants who adhere to the Ultraprocessed Foods dietary pattern were more likely to present rapid weight gain (adjusted odds ratio 1.3, 95% CI 1.1 to 1.5) and overweight/obesity risk (adjusted odds ratio 1.2, 95% CI 1.0 to 1.4). ConclusionsHigher adherence to a dietary pattern characterized by ultraprocessed foods was associated with a greater likelihood of both rapid weight gain and overweight/obesity risk early in life. Promoting breastfeeding and increasing consumption of unprocessed/minimally processed foods during early infancy while restricting ultraprocessed foods are key components to reducing the growing burden of childhood obesity.

FOOD SECURITY AND RAJASTHAN

This research paper explores the nuanced understanding of "food security," tracing its evolution from a mere "availability at all times of adequate world food supply" in the 1970s to Amartya Sen's comprehensive perspective on entitlements in the 1980s. Sen's emphasis on entitlements, beyond mere food availability, distinguishes his approach from traditional notions of effective demand. While Keynes' effective demand related to monetary income in a capitalist economy, Sen's analysis extends to a "mixed economy" with multiple classes.
 In the context of Rajasthan, this study reveals the intricate dynamics of food security, considering diverse demographics, geographical variations, and vulnerability indicators. Urbanization trends, examined through three distinct phases, present potential implications for food distribution and accessibility across regions. Recognizing the heterogeneity of regions in Rajasthan, the paper stresses the importance of tailored policies that account for climatic variations, regional interests, and health demands.
 A thorough investigation into the nutritional status of the population highlights concerning trends, particularly the prevalence of anemia and malnutrition among women and children. Despite advancements in education and awareness, persistent nutritional challenges demand a holistic approach addressing both immediate and underlying causes.
 In conclusion, this research underscores the multi-faceted nature of food security challenges in Rajasthan. It advocates for targeted interventions incorporating sustainable agriculture, improved infrastructure, and inclusive policies. By addressing the root causes of food insecurity and malnutrition, the paper provides valuable insights for policymakers, researchers, and practitioners striving to enhance food security in the region.Top of Form

Does the financialization of agricultural commodities impact food security? An empirical investigation

The possible effect of the financialization of agricultural commodities on food security has become an evolving worry in recent years. This study seeks to empirically investigate this complicated problem and influence policy choices to ensure a more stable and secure food system by analyzing the role of financialization in global food markets. The study uses the panel data regression model, moderating effects model, and panel data regression with threshold variable to analyze financialization due to three agricultural commodities: wheat, maize, and soybean. For wheat, maize, and soybean futures traded on the Chicago Board of Trade, we utilize data related to annual trading volume, annual open interest contracts, and a ratio of annual trading volume to annual open interest contracts. The sample covers five developed countries - the United States, Australia, Canada, France, and Germany, and seven developing countries- China, Russia, India, Indonesia, Brazil, Vietnam, and Thailand. Annual panel data are constructed for the 2000–2021 period. The Human Development Index (HDI) is the threshold variable to differentiate the impact across these countries. The findings reveal that the financialization of agricultural commodities has negatively impacted food security globally, with wheat and soybean having a greater negative impact than corn. Also, there is a more considerable impact on developing countries compared to developed countries. The study finds that monetary policy can potentially reduce the impact of agricultural financialization on food security. The findings of this paper act as a guide to assist policymakers in ensuring that the world's food supply stays secure and available.

IMPLEMENTATION OF FUZZY MEMBERSHIP FUNCTION TO DETERMINNE AUTOMATIC WATERINNG TIME

Global warming impacted to agriculture, thereby affecting the world's food supply. UNICEF calls on every country to be able to meet its food needs independently. Indonesia, as an agricultural country, has the potential to meet food needs independently. One effort that can be made is to combine existing agricultural systems with technology through the application of IoT. In this research, it was applied to citrus plants using DHT11 temperature and air humidity sensors and soil moisture sensor YL69. Data processing uses a fuzzy algorithm approach, using two input variables, namely temperature and humidity variables and soil moisture variables. Meanwhile, the resulting output variable is the time for watering the plants. At the fuzzification stage, each variable is grouped into three memberships. For temperature and humidity variables are divided into hot, medium and cold membership. Soil moisture variables are divided into wet, medium and dry membership. And the time variables consists of short, medium and long time membership. In the defuzzification stage, it is calculated by calculating the center of area value for each graphic area resulting from the fuzzification stage. Furthermore, to test the system, the testing carried out resulted in an error value of 5.19 percent. Based on test and trial that be done, the result shows that average volume of water that flows every second when watering plants is 36.5 ml per second.