Leveraging Inception V3 for Precise Early and Late Blight Disease Classification in Potato Crops
Bibliographic record
Abstract
The global significance of potato (Solanum tuberosum) as a staple food necessitates innovative approaches to safeguard its production against detrimental fungal pathogens, notably early and late blight diseases.These afflictions not only jeopardize crop health but also exacerbate economic pressures on agricultural stakeholders by diminishing yields and necessitating increased use of chemical treatments, which in turn degrades soil quality and elevates disease susceptibility.In response to these challenges, the deployment of Artificial Intelligence (AI) offers a transformative solution for the automatic detection of plant diseases, minimizing human intervention and maximizing diagnostic precision.This study employs the Inception V3 model, a deep learning algorithm, on a dataset comprising 2,152 potato leaf images, segmented into training and validation subsets at an 80:20 ratio.Through the application of image augmentation techniques such as scaling, rotating, and flipping, the diversity of the dataset was substantially enhanced, facilitating the model's capacity to distinguish between early blight, late blight, and healthy leaves with an unprecedented accuracy of 98.60%.This research not only underscores the efficacy of AI in revolutionizing agricultural disease management but also contributes to the broader discourse on sustainable farming practices by reducing reliance on chemical interventions.The findings advocate for the integration of AI-driven diagnostic systems in agricultural management, promising significant advancements in crop preservation and economic sustainability.
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 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 | 0.001 |
| 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 | 0.001 |
| 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".