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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)"
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