Transforming the Future of Livestock Farming: Towards Efficient, Economical, and Innovative Approaches with Artificial Intelligence
Bibliographic record
Abstract
Artificial intelligence (AI) is currently one of the most widely used technologies, offering precise and accurate results while reducing manpower requirements. The application of AI in livestock farming represents a groundbreaking opportunity for India’s production system. India has one of the largest livestock populations in the world, with hundreds of millions of cattle, buffaloes, sheep, goats, pigs, and poultry, making it a key player in global animal agriculture. India is the world’s largest milk producer, with production recently reaching around 248 million tonnes annually, accounting for roughly a quarter of global milk production (about 25 %) and significantly supporting rural livelihoods. India also ranks second in egg production, with production nearing 149 billion eggs per year, and is among the top four producers of meat globally, with meat production of around 10.5 million tonnes (2024‑25). Despite these strengths, livestock management in India faces challenges in breeding, housing, nutrition, health care, and market access. Many farmers lack access to modern tools and information, which limits productivity and profitability. AI offers clear advantages not only in management but also in disease diagnosis, treatment support, and predictive decision‑making. However, several constraints—such as high implementation costs, limited technical knowledge among farmers, and infrastructure gaps—make adoption difficult. In this article, we discuss the advantages, limitations, and future trends of AI in livestock farming, highlighting its potential to transform Indian animal agriculture.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".