| Titre : | Artificial intelligence in the agri-food sector: applications, risks and impacts |
| Auteurs : | J. De Baerdemaeker, coord. ; S. Hemming ; G. Polder ; A. Chauhan ; A. Petropoulou ; F. Rovira-Más ; D. Moshou ; G. Wyseure ; T. Norton ; B. Nicolai ; F. Hennig-Possenti ; I. Hostens |
| Type de document : | Rapport, Expertise, Working Paper |
| Editeur : | Strasbourg [France] : European Parliament, 2023 |
| ISBN/ISSN/EAN : | 978-92-848-0190-9 |
| Format : | 79 p. |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.4 - Production Agricole. Système de ProductionThésaurus IAMM INTELLIGENCE ARTIFICIELLE ; INDUSTRIE ALIMENTAIRE ; SYSTEME AGROALIMENTAIRE ; SECTEUR AGROINDUSTRIEL ; DONNEE STATISTIQUE ; COLLECTE DE DONNEES ; MODELE ; AIDE A LA DECISION ; AGRICULTURE NUMERIQUE ; CHAINE DE VALEUR ; PROTECTION DES PLANTES ; GRANDE CULTURE ; SYSTEME DE CULTURE ; GESTION DES EAUX ; GESTION DES RESSOURCES ; GESTION DU SOL ; IRRIGATION ; PRODUCTION ANIMALE ; ELEVAGE ; CHAINE D'APPROVISIONNEMENT ; HORTICULTURE ; MATERIEL |
| Résumé : | An increasingly digitised society involves recording human activity and monitoring products and processes. In the agri-food sector this gives rise to large quantities of data. At the same time, data is also generated for research and scientific experiments. There is a growing interest in the applications of artificial intelligence (AI) in the agri-food sector to extract or exploit the information that can be detected in these data sets. Artificial intelligence algorithms, and the models derived from them, are used as support systems for better decision making or, in some cases, are implemented in automatic control processes and robotics, to alleviate drudgery. In this study, sensing and data collection in different agri-food sectors are described, together with how the data can lead to better management and better decision making in crop and animal production. As with other technological advances, AI in this domain comes with its own set of benefits, risks, ethical issues and societal implications. Questions raised with respect to AI include: how to balance potential benefits against potential risks; how to govern the use of these technologies; and how to incorporate socio-ethical value considerations into the policy and legal frameworks under development. Policies for training and education have to support potential users. |
| Note de contenu : |
1. Introduction
• 1.1. Smart agriculture • 1.2. Changes in data collection in agriculture • 1.3. Activities in the agricultural value chain • 1.4. Artificial intelligence for smart agriculture • 1.5. European initiatives for AI and is applications in agri-food • 1.6. Approach of the study 2. Artificial intelligence and protected cultivation • 2.1. Plant phenotyping of horticultural crops and the use of crop sensors • 2.2. Autonomous growing and the use of AI • 2.3. Digital twins and decision support for market-oriented production • 2.4. Challenges for AI in protected cultivation 3. Artificial intelligence in field crop production • 3.1. Vehicle automation and smart field operations • 3.2. Expert systems and decision support applications • 3.3. Intelligent crop planning • 3.4. Challenges of AI in field crop production 4. AI in soil and water management and irrigation • 4.1. Water budgeting at local or regional level • 4.2. Management of aquifers and river catchments • 4.3. Challenges for AI in soil and water applications 5. AI in animal production • 5.1. Why are AI technologies impacting animal production? • 5.2. Hardware for AI processing on livestock farms • 5.3. AI for improving animal productivity • 5.4. AI for improving animal welfare • 5.5. AI for improving animal health • 5.6. AI for improving animal breeding • 5.7. Challenges for AI solutions on livestock farms 6. AI in supply chain management of horticultural products • 6.1. AI in online sorting and grading of fruit and vegetables • 6.2. AI for linking postharvest quality to pre-harvest conditions • 6.3. Digital twins of horticultural supply chains • 6.4. Challenges in supply chain management of horticultural products 7. AI and the agricultural machinery industry: collecting data and decision deployment • 7.1. Challenges for agricultural machinery development in Europe 8. Barriers, challenges, and outlook for AI adoption in agri-food • 8.1. Technical developments to reduce barriers for AI in agri-food • 8.2. Challenges for models, data, and analytics • 8.3. Some concerns, expectations, and recommendations 9. Policy options for the use and simulation of AI in the agri-food sector • 9.1. Issues with the application of AI in the agri-food sector • 9.2. Action and regulation |
| Cote : | En ligne |
| URL / DOI : | https://www.europarl.europa.eu/RegData/etudes/STUD/2023/734711/EPRS_STU(2023)734711_EN.pdf |
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