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| cff-version: 1.2.0 | |
| message: "If you use this software or its companion paper, please cite as below." | |
| title: "agri-analyze: when do hand-crafted rules help modern CNNs for agricultural disease classification?" | |
| abstract: > | |
| Research codebase for a multi-architecture evaluation of hand-crafted rule | |
| engines, learned decision-tree rules, and LLM-generated rule baselines | |
| composed with modern CNN classifiers on agricultural disease datasets. | |
| Includes reproducibility tooling, statistical protocol (bootstrap, | |
| Holm-Bonferroni, Dietterich 5x2cv, Friedman-Nemenyi), expected | |
| misclassification loss (EML) sensitivity analysis, and a mega-dataset | |
| training pipeline combining 14 public Roboflow/Kaggle rice+wheat sources | |
| (~30 K de-duplicated images, 22 canonical classes). | |
| type: software | |
| version: "0.3.0-mega-dataset" | |
| license: MIT | |
| url: "https://github.com/Ashut0sh-mishra/agri-analyze" | |
| repository-code: "https://github.com/Ashut0sh-mishra/agri-analyze" | |
| authors: | |
| - family-names: "Mishra" | |
| given-names: "Ashutosh" | |
| affiliation: "Independent Researcher, India" | |
| email: "mishra.ashutosh@gmail.com" | |
| orcid: "https://orcid.org/0009-0000-4764-8160" | |
| keywords: | |
| - agricultural-ai | |
| - plant-disease-classification | |
| - rule-based-ai | |
| - neuro-symbolic | |
| - reproducibility | |
| - benchmarking | |
| - mega-dataset | |
| preferred-citation: | |
| type: article | |
| title: "When Do Hand-Crafted Rules Help Modern CNNs for Agricultural Disease Classification?" | |
| authors: | |
| - family-names: "Mishra" | |
| given-names: "Ashutosh" | |
| year: 2025 | |
| journal: "Smart Agricultural Technology (under review)" | |