Vegetables Production (Tomato, Lettuce, Spinach, and Capsicum) Through Utilization of Hydroponic Technology
Bibliographic record
Abstract
In the present situation, Bangladesh needs food security which entails that each and every person must have physical and economic access to safe and nutritious food to meet dilatory needs. Scarcity of usable water for agriculture leads to production of lesser production of food which ultimately leads to hunger and malnutrition of a large number of people in our country. So, there is an utmost need for the adoption of such technology in agriculture that can contribute towards water saving and have a positive impact on food production and availability. ‘Hydroponics’ is one such methodology of soilless cultivation and the water use efficiency of this is much more than conventional system. Currently, hydroponics cultivation is gaining popularity all over the world because of its management of resources in a very efficient way and the production of quality foods. Several benefits of this technique include less growing time for crops than conventional crop growing in soil, round-the-year production, minimum disease and pest infestation, and elimination of several intercultural operations like weeding, spraying, watering, etc. which is labor intensive. Under hydroponics, by using different nutrient solutions and substrates such as coco coir, wood fiber, rice bran, and water, production of leafy as well as other vegetables, 70%-90% water is saved. Some leading countries like Israel, France, Canada, and the Netherlands have adopted this technique at the commercial level. On the basis of the above performance, it is revealed that hydroponics can play a significant role in quality vegetable production. For this reason, an experiment was conducted at Rabindra Maitree University (RMU) in Kushtia Sadar, Kushtia district and the title states that "Vegetable production (Tomato, Lettuce, Spinach, and Capsicum) through the utilization of hydroponics technology”.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".