Deep Learning Approaches for Potato Leaf Disease Detection: Evaluating the Efficacy of Convolutional Neural Network Architectures
Bibliographic record
Abstract
In agriculture, timely and accurate detection of plant diseases is essential to obtain healthy crop yields and ensure food security.However, detecting diseases in potato leaves is challenging because of the complex symptoms and variability in leaf appearances.This requires the development of an effective and efficient method that can overcome these challenges and improve disease detection accuracy.Utilizing the power of computer vision and deep learning, this paper presents a comprehensive study on potato leaf disease detection using a multi-architecture Convolutional Neural Networks (CNNs) approach.We evaluate five different CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, and AlexNet, to assess their classification capabilities.The research encompassed the dataset collection, data augmentation, model selection, hyperparameter tuning, and evaluation, leading to a rigorous analysis of detection accuracy, model convergence, and training efficiency.Our findings revealed that ResNet50 was the standout performer, achieving a remarkable 97% testing accuracy and 98% specificity.Conversely, the VGG19 architecture was the least effective.A consistent challenge across all models was accurately classifying categories of healthy leaves, indicating a potential area for model refinement.This study not only highlights the efficacy of deep learning in plant health diagnosis but also highlights the importance of specificity as an important metric in such tasks.The results of our study provide a promising avenue for real-time diagnosis of potato leaf diseases in the field, paving the way for healthier crops and increased agricultural productivity.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".