Advancing sustainability: The impact of emerging technologies in agriculture
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Notice bibliographique
Résumé
The need to ensure food security and promote environmental sustainability has led to a transformative period in agriculture. This period is characterized by the use of novel technology, which provides solutions that effectively address ecological concerns while also ensuring economic viability. Emerging technologies, such as precision farming enabled by drones, sensor-based monitoring systems and genetic editing techniques that result in drought-resistant crops, are significantly changing the agricultural sector. The integration of data analytics and machine learning algorithms is transforming supply chain management and enhancing the capabilities of predictive analytics in the context of crop diseases. Technological interventions serve to optimize efficiency and minimize the adverse ecological effects associated with farming, promoting the goals of sustainable agriculture. However, it is important to carefully address ethical and socio-economic considerations, including accessibility and data privacy, to manage these effects effectively. Therefore, the objective of this study is to examine the contributions of emerging technology to sustainable agriculture, evaluate its constraints, and suggest a comprehensive framework for its ethical and equitable integration. Communication technology has also impacted the agricultural sector, particularly with the increased use of connected devices. Artificial intelligence and deep learning advancements make processing collected data faster and more efficient, leading to more sustainable agricultural production using free, open-source software and sensor technology solutions. This technology enhances land optimization and boosts agricultural productivity, making sustainable farming practices more viable for both large and small-scale farmers. Our bibliometric analysis indicates a notable increase in interest in integrating sustainable agricultural methods with new technologies, particularly since 2018. It also revealed a strong link between precision agriculture, smart farming, machine learning, and the Internet of Things. However, awareness of technology is not very prevalent in the Asian region, especially among small-scale farmers. As a result, excessive usage of agricultural resources and wastage bring many adverse repercussions, and it's a high constraint to sustainable agricultural practices in the region. • The agriculture industry is undergoing a major transformation. • Focus is shifting towards food security and environmental sustainability. • Emerging technologies are improving crop disease management and supply chain efficiency. • This study explores how new technology affects sustainable agriculture. • Farmers are adopting technologies to better manage land and boost sustainable production.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle