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Predictive Models for Crop Yield and Pest Detection Using Low-Cost Phone Cameras in Smallholder Agriculture
Subject area: Science,Engineering and Technology · Area of research: Predictive Models for Crop Yield
DOI: https://doi.org/10.64388/IREV9I11-1718149
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
References
[1] Howard, A., et al. Searching for MobileNetV3. ICCV, 2019.
[2] Howard, A., et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861, 2017.
[3] Krause, J., et al. The AI Institute for Next Generation Food Systems. Nature Food, 2021.
[4] Paudel, D., et al. Machine Learning for Crop Yield Prediction in Smallholder Systems. Agricultural Systems, 2022.
[5] Van Klompenburg, T., et al. Crop Yield Prediction Using Machine Learning: A Systematic Literature Review. Computers and Electronics in Agriculture, 2020.
[6] Zhang, J., et al. Deep Learning for Plant Disease Detection: A Review. Frontiers in Plant Science, 2021
How to cite this paper
@article{1718149,
author = {Dr. Madumere Smart Onyemaechi, Ihim Kingsley, Frank Uchehara O.},
title = {Predictive Models for Crop Yield and Pest Detection Using Low-Cost Phone Cameras in Smallholder Agriculture},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {5329-5331},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1718149.pdf},
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},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1718149}
}