iCorn: A Methodological Framework for Multi-Label Disease Classification in Maize Using Uncurated Field Images

Abstract

iCorn: Multi-Label Maize Disease Classification Framework Complete implementation of iCorn—a methodological framework for multi-label classification of maize leaf diseases from uncurated field images using deep learning, with production-ready mobile deployment. **Manuscript**: "iCorn: A Methodological Framework for Multi-Label Disease Classification in Maize Using Uncurated Field Images" (Nature Scientific Reports) **Contents**: 1. **icorn-ml-workflows** — Python training and evaluation pipeline: - Multi-label classification training (1_multiClass.py, 2_multi_label_augmentation.py) - Object detection preprocessing with Faster R-CNN (3_multi_label_augmentation_obj_detection_segmentation.py) - ResNet18 backbone with PyTorch - TensorFlow.js model export for mobile inference (pt_to_tfjs.py, 4_get_tfjs_models.py) - Data loading and utility functions (dataset.py, utils.py) - Requirements: Python 3.8+, PyTorch 1.x, TensorFlow 2.x, torchvision 2. **icorn-mobile-application** — React Native production app: - Cross-platform iOS/Android mobile application - Real-time disease classification on-device (~266–273 ms inference latency) - TensorFlow.js model integration - Mobile-optimized UI for farmer-facing diagnostics - Complete build configuration and deployment scripts **Performance Metrics** (ResNet18, multi-label classification on held-out test):- Baseline (no preprocessing): Micro-F1 = 0.612, Macro-F1 = 0.452- Detection-assisted (ROI preprocessing): Micro-F1 = 0.628, Macro-F1 = 0.480- On-device latency: ~392–399 ms end-to-end (125–126 ms preprocessing + 266–273 ms inference) **Disease Classes** (6-way classification):Gray Leaf Spot (GLS), Northern Corn Leaf Blight (NCLB), Phaeosphaeria Leaf Spot (PLS), Common Rust (CR), Southern Rust (SR), Other **Note**: This repository contains code and application artifacts only. The maize disease dataset (Craze & Berger uncurated field images, ~2,355 images) and pre-trained model weights are not included. Refer to the Methods section of the manuscript for dataset access and reproducibility details.