Predictive Models for Crop Yield and Pest Detection Using Low-Cost Phone Cameras in Smallholder Agriculture

• Author(s): Dr. Madumere Smart Onyemaechi; Ihim Kingsley; Frank Uchehara O.
• Paper ID: 1718149
• Page: 5329-5331
• Published Date: 10-06-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

Smallholder farmers in Sub-Saharan Africa lack access to affordable tools for yield forecasting and early pest detection. This paper presents an end-to-end system that uses low-cost smartphone cameras combined with lightweight deep learning models to predict maize yield and detect fall armyworm infestation. We collected 18,400 field images and 1,200 plot-level yield measurements across Nigeria and Ghana over two growing seasons. A MobileNetV3-Small model for pest classification achieved 92.1% F1-score on-device, while a multimodal CNN + tabular regression model predicted yield with RMSE = 0.41 t/ha. We show that models trained on low-resolution images captured under variable field conditions generalize to unseen farms when augmented with weather and soil data. Our system runs at 18 FPS on a $80 Android phone, enabling real-time decision support without internet connectivity. Results demonstrate that low-cost mobile AI can provide actionable agronomic insights at scale for resource-constrained farmers.

Keywords

Precision Agriculture, Smallholder Farming, Crop Yield Prediction, Pest Detection, Mobile Deep Learning, Computer Vision

Citations

IRE Journals:
Dr. Madumere Smart Onyemaechi, Ihim Kingsley, Frank Uchehara O. "Predictive Models for Crop Yield and Pest Detection Using Low-Cost Phone Cameras in Smallholder Agriculture" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 5329-5331 https://doi.org/10.64388/IREV9I11-1718149

IEEE:
Dr. Madumere Smart Onyemaechi, Ihim Kingsley, Frank Uchehara O. "Predictive Models for Crop Yield and Pest Detection Using Low-Cost Phone Cameras in Smallholder Agriculture" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1718149