Artificial Intelligence: Implications for the Agri-Food Sector

Agronomy, Journal Year: 2023, Volume and Issue: 13(5), P. 1397 - 1397

Published: May 18, 2023

Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process analyze large amounts data, identify patterns relationships, make predictions or decisions based on analysis. AI has become increasingly pervasive across a wide range industries sectors, with healthcare, finance, transportation, manufacturing, retail, education, agriculture are few examples mention. As technology continues advance, it is expected have an even greater impact in future. For instance, being used agri-food sector improve productivity, efficiency, sustainability. It potential revolutionize several ways, including but not limited precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, safety. This review emphasizes how recent developments transformed by improving reducing waste, enhancing safety quality, providing particular examples. Furthermore, challenges, limitations, future prospects field summarized.

Language: Английский

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THE FOURTH INDUSTRIAL REVOLUTION AND ITS IMPACT ON AGRICULTURAL ECONOMICS: PREPARING FOR THE FUTURE IN DEVELOPING COUNTRIES

International Journal of Advanced Economics, Journal Year: 2023, Volume and Issue: 5(9), P. 258 - 270

Published: Dec. 15, 2023

This study provides a concise overview of the exploration transformative intersection between Fourth Industrial Revolution (4IR) and agricultural economics in developing countries. The work investigates profound changes brought about by technological advancements, emphasizing their implications for traditional farming practices, economic structures, overall sustainability. analyzes case studies presents key concepts, offering insights into challenges opportunities arising from 4IR sector. Additionally, proposes policy recommendations future strategies governments stakeholders to navigate this dynamic landscape. concludes highlighting relevance practical application findings, its contribution guiding decision-makers shaping resilient technology-driven economies nations. Keywords: Agricultural Economics, 4IR, Developing Countries, Impact, Future.

Language: Английский

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Harnessing artificial intelligence and remote sensing in climate-smart agriculture: the current strategies needed for enhancing global food security

Cogent Food & Agriculture, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 20, 2025

Global food security is seriously threatened by climate change, which calls for creative agricultural solutions. However, little known about how different smart technologies are integrated to enhance security. As a strategic reaction these difficulties, this review investigates the incorporation of remote sensing (RS) as well artificial intelligence (AI) into climate-smart agriculture (CSA). This demonstrates advances can improve resilience, productivity, and sustainability utilizing AI's capacity predictive analytics, crop modelling, precision agriculture, along with RS's strengths in projections, land management, continuous surveillance. Several important tactics were covered, such combining AI RS regulate risks, maximize resource utilization, practice choices. The also discusses issues like policy frameworks, building, accessibility that prevent from being widely adopted. highlights further CSA offers insights they help ensure systems remain secure changing climates.

Language: Английский

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Artificial intelligence on the agro-industry in the United States of America

AIMS Agriculture and Food, Journal Year: 2024, Volume and Issue: 9(4), P. 959 - 979

Published: Jan. 1, 2024

<p>Integrating artificial intelligence (AI) into agriculture is a pivotal solution to address the pressing challenges posed by rapid population growth and escalating food demand. Traditional farming methods, unable cope with this surge, often resort harmful pesticides, deteriorating soil health. However, advent of AI promises transformative shift toward sustainable agricultural practices. In context United States, AI's historical trajectory within sector showcases remarkable evolution from rudimentary applications sophisticated systems focused on optimizing production quality. The future American lies in AI-driven innovations, spanning various facets such as image sensing for yield mapping, labor management, optimization, decision support farmers. Despite its numerous advantages, deployment does not come without challenges. This paper delved both benefits drawbacks adoption domain, examining impact agro-industry environment. It scrutinized emergence robot farmers role reshaping practices while acknowledging inherent problems associated implementation, including accessibility, data privacy, potential job displacement. Moreover, study explored how tools can catalyze development agribusiness, offering insights overcoming existing through innovative solutions. By comprehensively understanding opportunities obstacles entailed integration, stakeholders navigate landscape adeptly, fostering more resilient system generations.</p>

Language: Английский

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A narrative review of artificial intelligence to optimize the use of fertilizers: A game changing opportunity

Crop Forage & Turfgrass Management, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 6, 2025

Abstract The green revolution, which came after the industrial boosted crop yields produced per unit of land, but it also increased need for synthetic fertilizers and pesticides lowered water table salinization. In order to improve farm productivity, soil fertility is crucial preserving fertility, boosting yields, enhancing harvest quality, fertilizer essential. decline in a key constraint food production worldwide, improper nutrient management significant cause this problem. Agroecosystems will implement contemporary technologies produce enough mitigate detrimental effects chemical fertilization on environment. Hence, agri‐food industry progressively utilizing artificial intelligence (AI) increase efficiency, sustainability. AI uses computational models process data identifies patterns predictions or decision‐making. This review emphasizes how technology could be used manure compositions improvement safety quality. We aimed identify role supporting evidences field studies characterize controlled combinations efficient with lowest possible plant toxicity. Also, we discuss constraints challenges agricultural sector. conclusion, AI‐based approaches suggested that combining organic inorganic can synergistically growth yield parameters.

Language: Английский

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Steering Conservation Biocontrol at the Frontlines: A Fuzzy Logic Approach Unleashing Potentials of Climate-Smart Intercropping as a Component within the Integrated Management of Fall Armyworm in Africa

Heliyon, Journal Year: 2025, Volume and Issue: 11(4), P. e42530 - e42530

Published: Feb. 1, 2025

Highlight•We present a computational index to determine potential deployment sites for push-pull technology.•The fuzzy sets theory identified suitable with key variables such as the suitability of companion plants, presence maize, and livestock.•The generated helped locally identify pertinent locations deployment.•Low areas, less favourable technology, showed no null probability.AbstractThis study introduces that employs component in integrated management Fall Armyworm (FAW) Africa. The index, validated through known testing informed by insights from field data practical observations, is primarily based on plants (Desmodium intortum Brachiaria brizantha), livestock, maize covariates. developed set rules linking each selected covariate output membership functions, which are later combined using an algebraic operator. It identifies extensive farms across Africa potentially Push-Pull although varies region. Farms eastern southern regions predicted be highly suitable, while West expected improve over time due perennial nature agronomic benefits plants. proposed metric deploying providing roadmap effective practices Africa, assisting farmers decision-makers FAW. Overall, our results indicate fuzzy-based tool identifying areas maximise technology FAW management. Our appropriate application, allowing careful use resources increasing likelihood pest This approach will ultimately safeguard cereal crops, boost agricultural productivity, aid ensuring food security

Language: Английский

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Leveraging artificial intelligence for bamboo breeding in the context of "Bamboo as a Substitute for Plastic" initiative

Industrial Crops and Products, Journal Year: 2025, Volume and Issue: 228, P. 120896 - 120896

Published: March 25, 2025

Language: Английский

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Leveraging Artificial Intelligence for Enhancing Wheat Yield Resilience Amidst Climate Change in Sub-Saharan Africa

LatIA, Journal Year: 2025, Volume and Issue: 3, P. 88 - 88

Published: Feb. 19, 2025

The introduction of a deep learning-based method for non-destructive leaf area index (LAI) assessment has enhanced rapid estimation wheat and similar crops, aiding crop growth monitoring, water, nutrient management. Convolutional Neural Network (CNN)-based algorithms enable accurate, quantification seedling areas assess LAI across diverse genotypes environments, demonstrating adaptability. Transfer learning, known efficiency in plant phenotyping, was tested as resource-saving approach training the model. These advancements support breeding, facilitate genotype selection varied accelerate genetic gains, enhance genomic LAI. By capturing this can improve resilience to climate change. Additionally, advances machine learning data science better prediction distribution mapping global rust pathogens, major agricultural challenge. Accurate risk identification allows timely effective control measures. Moreover, lodging models using CNNs lodging-prone varieties, influencing decisions yield stability. artificial intelligence-driven techniques contribute sustainable enhancement, especially context change increasing food demand.

Language: Английский

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A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system

Frontiers in Plant Science, Journal Year: 2025, Volume and Issue: 16

Published: May 22, 2025

Maize-soybean intercropping is a sustainable farming practice that optimizes resource use efficiency and improves yield potential. Accurate prediction essential for effective agricultural management in such systems. This study proposes Fuzzy-Optimized Hybrid Ensemble Model (FOHEM), integrating stacked ensemble machine learning algorithms with fuzzy inference system (FIS) to improve prediction. The dataset includes four treatments: SM (sole maize), SS soybean), 2M2S (two rows of maize alternating two 2M3S three soybean). Key input features include environmental factors, soil nutrients, practices across different treatments. FOHEM framework integrates the outputs FIS model comprising Random Forest (RF), Categorical Boosting (CatBoost), Extreme Learning Machine (ELM)). A genetic algorithm (GA) dynamically adjusts weights between model, optimizing final while enhancing accuracy robustness. Additionally, LIME SHAP are used interpretability, identifying influencing factors. validated using performance metrics as MSE, MAE, R2. results demonstrated proposed significantly enhances accuracy, offering valuable insights highlights potential learning, optimization techniques advance precision agriculture decision-making farming.

Language: Английский

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A Data-Driven Strategy for Long-Term Agrarian Sustainability using the Application of Machine Learning Algorithms to Predictive Models for Pest and Disease Management

SHS Web of Conferences, Journal Year: 2025, Volume and Issue: 216, P. 01033 - 01033

Published: Jan. 1, 2025

The reactive pest and disease management strategies implemented for sustainable agriculture are delayed, pesticide use is high, crop losses high due to human monitoring. It not very efficient, free of errors prone, environmentally friendly. In order address these problems, this study presents the Pest Disease Management Machine Learning Algorithm (PDM MLA), a data driven control approach. PDM-MLA based on predictive modeling predicts infestations with accuracy by analyzing weather, parameters soil, history outbreaks pests, health data. Real time decisionmaking help it helps in making proactive intervention which minimizes damage also better pesticides. unlike conventional methods which, even when targeting specific cancers, may create chemical dependency issues unnecessary risks environment. addition, costs ecological balance increased resource efficiency insofar as measures only applied needed. results from empirical evidence show an improved accuracy, thus lower losses, yield more farming. This framework combines IoT sensor networks big analytics, AI forecasting, offer scalable solution precision agriculture. By pointing out its potential transform modern farming terms food security machinery, people be aware.

Language: Английский

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