Precision Agricultural Technologies for enhancing crop productivity: A way forward to Sri Lankan Agriculture
Bibliographic record
Abstract
Precision agriculture (PA) is an information-based production technology that manages spatial and temporal variability within a farming system to optimize its productivity and profitability while ensuring the sustainability of land resources. Today, several sophisticated technologies such as robotics, wireless sensor networks (WSN), aerial images, a global positioning system (GPS), global navigation satellite system (GNSS), smart mobile devices, internet of things (IoT), variable rate application (VRA), weather modelling, radio-frequency identification (RFI) are greeted with PA at a global scale. An exponentially increasing trend in adoption can be seen in developed countries such as the USA, Canada, Australia, and European countries, but to a limited extent in some developing countries. The degree of adoption of PA varies on economic, social, and geographic factors such as the scale of production, input cost, and features of the technology. Developing economies like Sri Lanka, where small-scale food crop agriculture is dominating, have the potential to benefit from precision agricultural technologies (PATs) to a greater extent. Relatively low-cost but effective PATs that would fit well with small-farm production units are emerging globally. Providing small farming units with the correct tools and greater control of the production process would support such farming communities, unlocking their potential and meeting the ever-increasing national and global food demand. Land laser levelling, real-time variable-rate fertilizer and pesticide application, mechanical harvesting and low-cost IoT-based crop management systems for protected agriculture are the most promising PATs that have great potential in Sri Lanka. This review presents a global overview of PA technologies for enhancing food crop production and their benefits, the adoption of PA technologies by different countries and the constraints, and the role of PA in Sri Lankan Agriculture, past and present. Finally, the synthesis and way forward.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".