Comparative Analysis of AlexNet, GoogLeNet, VGG19, ResNet50, and ResNet101 for Improved Plant Disease Detection Through Convolutional Neural Networks
Bibliographic record
Abstract
To meet the increasing need for food from a growing global population, it is imperative to make progress in agricultural technology, namely in the area of disease detection. This will help reduce the need for pesticides and improve the overall health of crops. The issue at hand is the need for accurate and effective identification of plant diseases to enhance food production. This research tackles this problem by using cutting-edge computational neural networks (CNNs) on pre-existing agricultural datasets to assess and compare their effectiveness. Existing research does not show the precise and detailed performance of the latest CNN models in agricultural data sets. We addressed this issue by examining five Convolutional Neural Network (CNN) models: AlexNet, GoogLeNet, VGG19, ResNet50, and ResNet101. This approach involved meticulous training and assessment of these models to compare their precision, loss, duration of training, and complexity of the model. ResNet 101 outperformed all other models with an accuracy rate of approximately $\mathbf{97\%}$. Nevertheless, the practical implementation of this technology is hindered by its intricate model complexity and demanding resource needs, which make it unsuitable for low-power applications. GoogLeNet demonstrated superior performance in terms of accuracy, model complexity, training time, and other evaluation measures.
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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 | 0.001 |
| 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".