Beans Leaf Diseases Classification Using MobileNet Models
Bibliographic record
Abstract
In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, beans is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, beans are a great source of protein that offer many health benefits, but there are a lot of diseases associated with beans leaf which hinder its production such as angular leaf spot disease and bean rust disease. Thus, an accurate classification of bean leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify beans leaf disease by using public dataset of leaf image and MobileNet model with the open source library TensorFlow. In this study, we proposed a method to classify beans leaf disease and to find and describe the efficient network architecture (hyperparameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying bean leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using MobileNetV2 architecture under the some controlled conditions as MobileNet to check if we could get faster training times, higher accuracy and easier retraining, we evaluated and implemented MobileNet architectures on one public dataset including two unhealthy classes (angular leaf spot disease and bean rust disease) and one healthy class, the algorithm was tested on 1296 images of bean leaf. The obtained results showed that our MobileNet model achieves high classification performance for beans leaf disease, the classification average accuracy of the proposed model is more than 97% on training dataset and more than 92% on test data for two unhealthy classes and one healthy class.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".