GreenVision: A Sustainability-Focused Deep Learning Pipeline with Explainable AI for Multiclass Plant Pathogen Classification

) Computational Energy Inefficiency: Training state-
of-the-art architectures from scratch consumes massive
computational power, contributing to a high carbon
footprint that contradicts the principles of sustainable
“Green AI.”
2) Resolution Discrepancies: Real-world plant pathogen
datasets are often heterogeneous, containing mixed im-
age resolutions (e.g., 256 × 256 and 512 × 512 pixels).
Standard resizing techniques often degrade fine-grained
visual features like necrotic lesion margins, causing
classification errors.
3) The “Black-Box” Problem: Standard deep learning
models lack interpretability. Without understanding why
a model classified a leaf as infected, farmers and
agronomists cannot fully trust its predictions.