Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems
Bibliographic record
Abstract
Chlorosis, or leaf yellowing, in crops is one of the quality issues that primarily occurs due to interference in the production of chlorophyll contents. The primary contributors to inadequate chlorophyll levels are abiotic stresses, such as inadequate environmental conditions (temperature, illumination, humidity, etc.), improper nutrient supply, and poor water quality. Various techniques have been developed over the years to identify leaf chlorosis and assess the quality of crops, including visual inspection, chemical analyses, and hyperspectral imaging. However, these techniques are expensive, time-consuming, or require special skills and precise equipment. Recently, computer vision techniques have been implemented in the agriculture field to determine the quality of crops. Computer vision models are accurate, fast, and non-destructive, but they require a lot of data to achieve high performance. In this study, an image processing-based solution is proposed to solve these problems and provide an easier, cheaper, and faster approach for identifying the chlorosis in lettuce crops grown in an aquaponics facility based on their sensory property, foliage color. The ‘HSV space segmentation’ technique is used to segment the lettuce crop images and extract red (R), green (G), and blue (B) channel values. The mean values of the RGB channels are computed, and a color distance model is used to determine the distance between the computed values and threshold values. A binary indicator is defined, which serves as the crop quality indicator associated with foliage color. The model’s performance is evaluated, achieving an accuracy of 95%. The final model is integrated with the ontology model through a cloud-based application that contains knowledge related to abiotic stresses and causes responsible for lettuce foliage chlorosis. This knowledge can be automatically extracted and used to take precautionary measures in a timely manner. The proposed application finds its significance as a decision support system that can automate crop quality monitoring in an aquaponics farm and assist agricultural practitioners in decision-making processes regarding crop stress management.
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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 | 0.004 |
| 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".