Design and Implementation of Smart Agricultural System for Pest Disease Detection in Crops Using Artificial Intelligence
Bibliographic record
Abstract
Agriculture plays a vital role in food production and economic stability, yet plant diseases significantly affect crop yield, leading to global losses.Efficient and accurate disease detection is crucial for sustainable farming.This study presents a deep learningbased approach for plant disease detection using a Convolutional Neural Network (CNN).The proposed model incorporates batch normalization, the Adam optimizer with a power-exponential learning rate, and the ReLU activation function to enhance learning efficiency and generalization.A dataset of 70,000 images is utilized for training and evaluation.The model successfully classifies plant leaves as healthy or diseased and identifies specific diseases with an accuracy of 99.4%.Performance metrics such as precision (96.77%) and recall (96%) further validate the robustness of the proposed system.Compared to conventional methods, the integration of advanced optimization techniques improves convergence speed and classification accuracy, making the model highly suitable for real-world agricultural applications.The findings demonstrate the potential of AI-driven smart agricultural systems in early disease detection, enabling timely interventions and improving overall crop health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".