Plant Leaf Disease Detection Using Metaheuristic Optimization Algorithms and Deep Learning
Bibliographic record
Abstract
Plant diseases significantly reduce the yield and the production of crops across the globe.Crop productivity, plant development and human access to food have all been hampered by the prevalence of plant diseases throughout the history.In general, leaves exhibit symptoms if the plant is affected by diseases.Therefore, it is essential to identify the type of infestation to reduce the destructiveness of the disease.This scenario allows one to replicate the spread of infectious diseases and the inability of farmers to recognize and remember them.One possible approach to tackle this issue is to utilise Deep Learning (DL) techniques in conjunction with Machine Learning (ML) approaches within the domain of Computer Vision (CV).The current research has introduced the APLDD-ESOSDL approach, which utilises deep learning to optimise the search for symbiotic organisms in order to automate the detection of plant leaf diseases.The objective of the proposed APLDD-ESOSDL approach is to enhance agricultural yields and reduce crop losses by offering farmers a visual depiction of disease symptoms.The goal of the APLDD-ESOSDL approach is to accurately classify the presence of leaf diseases.The APLDD-ESOSDL technique utilises the inception ResNet-v2 model as a feature extractor and the Stacked Long Short-Term Memory (SLSTM) model for classification.In addition, the hyperparameters of the SLSTM algorithm are adjusted using the Enhanced Symbiotic Organism Search (ESOS) approach.A comprehensive experiment was carried out utilising the reference data set to verify the effectiveness of the APLDD-ESOSDL approach.The APLDD-ESOSDL algorithm outperformed more advanced systems, achieving a maximum accuracy of 99.22%, precision of 98.52%, sensitivity of 98.06%, and specificity of 99.54% in experimental experiments employing six distinct cutting-edge approaches.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".