Authors - Mainford Mutandavari, D. Hemavathi Abstract - Maize (Zea mays L.) is an essential staple produce for smallholder farmers in developing nations, yet Northern Corn Leaf Blight (NLB), Grey Leaf Spot (GLS), and Common Rust foliar diseases cause yield losses of 30–70%. Infection detection is done at advanced stages due to labor intensity resulting from the conventional disease monitoring methods. A Low-Cost Drone-Mounted Multispectral Imaging (LCDMI) framework for resource-constrained smallholder systems is presented in this paper, pairing a consumer-grade UAV with a five-band multispectral sensor. The vegetation-index features are fused with multispectral band data using a Spectral-Spatial Attention Vision Transformer (SSAViT) classifier and a Spectral-Constrained Synthetic Data Generation (SC-SDG) module addresses training-data scarcity. A hardware cost of USD1,940 is projected for field evaluations across twelve plots in Zimbabwe over two growing seasons yielding 95.8% detection accuracy, identifying diseases 7–12 days before visible symptom onset. A multi-label extension enables simultaneous classification of co-occurring infections. Georeferenced disease maps are delivered within 6.3 min/ha. With perhectare costs as low as USD2.10 on a scale, the economic analysis projects ROI within two seasons for cooperatives managing 50+ hectares.
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