Enhancing Sustainable Agriculture Through Digital Farming Technologies: Auto-Irrigation, Nutrient Monitoring, and Disease Detection
Bibliographic record
Abstract
Agriculture forms the backbone of India's economy and significantly influences daily life.Its role is evident in the food that people consume, the jobs it generates, and its contribution to economic stability and well-being.However, due to poor yields, the number of farmers is gradually declining.According to existing literature, three key factors affect the yield of cultivated land: effective water management, early detection and diagnosis of plant diseases, and an adequate supply of essential nutrients such as nitrogen, phosphorus, and potassium.To boost production, auto-irrigation systems, nutrient monitoring systems, and disease forecasting tools (apps) have been developed.A field model of an auto-irrigation system has been implemented.An Arduino-based NPK sensor system has been developed to measure soil nitrogen, phosphorus, and potassium levels.Additionally, farmers receive nutrient data through an NPK sensor monitoring app, and a web app provides fertilizer recommendations based on NPK data.Finally, an app will be developed to identify the type of disease affecting a plant and to offer a treatment for that condition.The diseases are identified using a Convolutional Neural Network (CNN) algorithm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".