Improving the Conversion Efficiency of Photosynthetically Active Radiation (PAR) Absorbed by Winter Wheat Under Deficit Irrigation Using Cost-Effective Autonomous IoT Devices
Bibliographic record
Abstract
Water shortage is considered the most critical issue in the regions of arid and semi-arid climate, which is affecting the growth cycle and yield of the winter wheat crop (Triticum durum Desf.). Consequently, irrigation is necessary to increase crop production and maximize water use efficiency (WUE). This study was carried out over three consecutive cropping seasons (2021-2022), (2022-2023), and (2023-2024) at the Research Unit of the National Institute of Rural Engineers, Water and Forests (INRGREF) at Cherfech. This study was aimed at examining the impact of three levels of continued deficit irrigation (D1 = 75% ETc, D2 = 60% ETc, and D3 = not irrigated only by rainfall) on leaf area index (LAI), water consumption (WC), photosynthetic active radiation absorbed (PARabs), radiation use efficiency of grain yields (RUEY), and the relationship between cumulative WC and cumulative PARabs. The cost-effective and precise autonomous Internet of Things device with artificial intelligence at the edge was utilized to monitor irrigation and crop water consumption. At harvest, a decrease in TPDM was registered in the two treatments, DI2 and DI3, by (30.2%; 29%) and (40%; 38.7%) in 2020-2021, (14.7%; 12.3%) and (32%; 30.1%) in 2022-2023, and (19.7%; 14%) and (38%; 34.1%) in 2023-2024, when contrasted with the respective DI1 and FI treatments, respectively. During the three-cropping season (2021-2024), ANOVA analysis revealed that DI and FI treatments were not significantly affected (P ˃ 0.05) by the accumulated PARabs. The cumulative PARabs in D2 and D3 were dropped to 1.7% and 4.9%, respectively, compared to FI. In the second season (2022-2023) and in the third season (2023-2024), the PARabs in D2 and D3 decreased by (4 - 4.9%) and (15.7 - 15.8%), respectively, compared with FI. The lowest GY was registered under a rainfed treatment, and it decreased from 64.5 to 68.6% in the three experiments compared to FI. The highest RUEY was registered in FI. There was a reduction in D1 and D2 in the first season of 6.6% and 51.9%, respectively, compared with FI. Under the treatment D2 at the second and third seasons, the RUEY decreased from 28.6 to 43% compared to the control treatment. Photosynthetically active radiation (PARabs) and crop water consumption (CWC) have a strong linear relationship; this relationship can be used to estimate crop water requirements as a simple measure of cumulative radiation absorbed (cereal crops).
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".