Advancing sustainability: The impact of emerging technologies in agriculture
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".