Mango Plant Disease Detection System Using Hybrid BBHE and CNN Approach
Bibliographic record
Abstract
Detection of plant diseases plays a crucial role in taking disease control measures to increase the quality and quantity of crops produced. Plant disease automation is beneficial because it eliminates surveillance work at significant farms. As plants are a food source, diagnosing leaf conditions early and accurately is essential. This work involves a detailed learning approach that automates leaf disease detection in mango plant species. This paper presents a detection system using Brightness Preserving Bi-Histogram Equalization (BBHE) and Convolutional Neural Network (CNN). The photographs of mango leaves were first flattened, then resized and translated to their threshold value, followed by feature extraction. CNN and BBHE have extensively been used for pattern recognition. The test images of affected leaves were subsequently uploaded to the system and then matched to the ailments being trained. Training data and test data were cross-validated to balance over-adjustment and under-adjustment problems. The proposed method correctly detects the mango leaves disease at the early stage with 99.21% maximum accuracy.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".