Real Time RGB Proxy Vegetation Indexing and Texture Analysis for UAV and Handheld Crop Imagery

This paper introduces N_GACL, an open-source desktop software pipeline designed for low-resource exploratory agronomic image analysis, featuring a dual-interface GUI (PyQt6 GIS-style and Tkinter classic). Built around a streaming, memory-bounded batch aligner, N_GACL integrates a fully labeled RGB proxy vegetation index family, multispectral NDVI/NDRE support, and GLCM texture extraction alongside classical machine learning baselines (PCA, KNN, K-Means) and optional pretrained deep learning inference (ResNet101/Faster R-CNN). We provide the complete mathematical formulation for all fourteen analysis modules and validate pipeline execution through procedural demonstration imagery and real user-session auditing. To demonstrate deployment utility, an eight-model benchmark was executed across two datasets. A 20,000-row reference dataset served as a negative control, confirming baseline architectural integrity at chance level. On an independent validation set of 1,543 real crop-disease photographs across 22 classes, classical features extracted by the pipeline achieved a 33.8% balanced accuracy against a 4.55% chance baseline, a result that remained stable after removing augmented data. Finally, to combat metric inflation and silent failures in agricultural decision-support systems, we introduce the Verifiable Reporting Framework (VRF) a source-code auditable checklist that ensures strict data fidelity and transparent metric reporting. N_GACL offers a reproducible, accessible framework for field-level agronomic computer vision.

RGB proxy vegetation indices; GLCM texture; streaming image registration; precision agriculture; reproducibility

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