Bibliographic record
Abstract
Horticulture sector has emerged as an important segment in Indian Agriculture. Starting with a back-yard farming in the beginning it has gone through transformation and its production has surpassed that of agricultural crops. Continuously growing population expected to reach 1.9 billion by 2047’s and limited availability of land and water will call for developing, testing and adapting most modern technology tools in horticulture sector to meet the demand of ever growing population. Digital technology comprising in-situ and crop growing environment sensors, advanced softwares including artificial intelligence/machine learning to generate prescription and coupled with devices to operate water and nutrient delivery systems have emerged useful for horticulture crop husbandry. Remote sensing, navigation and positioning system, geographic information system in conjunction with information and communication technology are expected to provide time and location specific information on crop area, condition and yield. Need for introducing traceability of input, process and storage conditions used in crop production, value addition. Supply-chain are expected to be facilitated with use of block-chain technology. Host of other digital technology solutions will enable screening genetic traits, breeding of varieties with desirable characteristics. Meeting the challenges of climate change will be a formidable task while planning economically profitable and sustainable horticulture crop production. Human resource development to meet the emerging requirement is also possible using digital technology. This review describes the potential use of a large number of digital technology applications in horticulture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".