PFDI: a precise fruit disease identification model based on context data fusion with faster-CNN in edge computing environment
Bibliographic record
Abstract
Abstract Fruits have a significant impact on everyday living i.e., citrus fruits. Numerous fruits have a solid nutritious value and are packed with multivitamins and trace components. Citrus fruits are delicate, so they are susceptible to many diseases and infections. Many researchers have suggested various deep learning and machine learning based fruit disease detection and classification models. In this research we are presenting precise fruit disease identification (PFDI) model based on context data fusion with Faster-CNN in edge computing environment. The goal is to develop a precise, efficient, and trustable fruit disease detection model, which is a critical component of an autonomous food production in robotic edge platform. This research examines and explores four different diseases of citrus fruits using CNN deep learning models to be adopted as edge computing solution. Identification of citrus diseases namely cankers black spot, greening, scab, melanose, and healthy citrus fruits are implemented using the proposed sequential model without pruning, with pruning having different sparsity level followed by post quantization. Through transfer learning method, we optimize this model for the assignment of fruit disease detection employing visuals from two patterns: Near-infrared (NIFR) and RGB. For integrating multi-model (NIFR and RGB) facts, early and late data fusion techniques are evaluated. The accuracy obtained from the proposed model for the canker disease is 97%,scab 95%, melanose 99% ,Greening 97%,Black spot 97% and for healthy 97%. In this paper we compared and evaluated the results of proposed model with the sparsity levels of 50–80%, 60–90%, 70–90%, 80–90% pruning and also obtained the results of post-quantization on each level. The results show that the size of the model with 60–90% pruning can be counteracted to the 47.64 of the baseline model without significant loss of accuracy. Moreover, post quantization can further reduces the of 60–90% pruning from 28.16 to 8.72. In addition to enhanced precision, the above initiative is much faster to implement for new fruits diseases because it needs bounding box annotation (BBA) instead of pixel-level annotation (PLA).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".