Crop Disease Detection from Transfer Adaptation in Smallholder Farm Images: Graph Analysis

Abstract

The early and accurate detection of crop diseases is paramount for ensuring global food security, particularly in the context of smallholder farming, which contributes significantly to the global food supply. Smallholder farm environments present unique challenges for automated visual disease detection, including heterogeneous crop varieties, complex backgrounds, variable illumination, and high degrees of occlusion. While deep convolutional neural networks have shown promise in controlled laboratory settings, their performance degrades substantially when deployed in real-world agricultural environments due to the domain shift between training and testing data. This paper proposes a novel framework that integrates transfer adaptation with graph analysis to predict and detect crop diseases in smallholder farm images. By conceptualizing the agricultural image as a structured graph where superpixels act as nodes and spatial relationships define the edges, the proposed method captures complex topological features that are robust to environmental noise. Transfer adaptation techniques are employed to align the feature distributions between source domains comprising high-quality, controlled images and target domains consisting of noisy, real-world smallholder images. Comprehensive evaluations demonstrate that the integration of graph-based relational reasoning with domain adaptation significantly improves classification accuracy and generalizability. The findings suggest that modeling the structural context of plant diseases can overcome the limitations of traditional grid-based feature extraction paradigms.