Intelligent Information Technologies in Fruit Industry
Bibliographic record
Abstract
Fruit production has several characteristics, such as long life of the plant, economic production cycle, high production efficiency, and high competitiveness.Fruit production technologies have directly affected the yield rate, quality and industrial benefit.At the same time, modern agricultural information technologies (IT) and precision management technologies show better and better social and economic benefits and development potential.Moreover, informatization, precision, intelligentization, and mechanization have become important parts of the modern management techniques in fruit production, and are important directions of scientific and technological development of fruit production.They are also the key fields for innovative application of modern agricultural sciences and technologies.In recent years, the IT for fruit production in the world has made important progress.A number of new theories, methods, technologies, and products have supported the development and application of the IT to fruit production in developed countries.Numerous examples prove that, through the development and application of precision agriculture technology, the efficiency of the fruit production has been significantly increased, while the production cost and agricultural non-point source pollution are reduced, and the market competitiveness of fruit is promoted and food safety has been significantly improved.Remote DSS (decision support system) can provide the whole-course, all-weather, real-time scientific and technical guidance to fruit production, and greatly improve the industrial development.Intelligent agricultural machines can effectively solve the following problems: Reduction of rural labors, decline of the educational level of the labors, low standardization of agricultural works and production efficiency.Thus, it can be expected that the development and application of modern fruit information technology and precision management technology will provide important scientific and technological support for improving the good quality produce, fruit industry efficiency and the sustainable development.In order to promote the exchange of the recent progress in innovation and application of intelligent information and precision management technology in modern fruit industries among the scientists and engineers in the world, the International Symposium on Informationization and Precision Management in Fruit Industry (ISIPMFI'2013) was organized by the Chongqing Science and Technology Commission, Citrus Research Institute of Southwest University (CRI, SWU), China Agricultural University (CAU) and National Engineering Research Center for Information Technology in Agriculture (NERCITA).The latest practical applications on various topics, such as image processing technologies, computing technologies and intelligent information technology in fruit industry were reported at this symposium.In this special issue, 15 high quality papers on information technologies in fruit industries have been selected from the ISIPMFI'2013 submissions.The main topics include, spectroscopy applications in
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.053 |
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".