Matshwene E. Moshia

Institute for Soil, Climate and Water, University of Fort Hare

Papers

Total Citations

H-Index

About

Matshwene E. Moshia is a pioneering researcher at the intersection of agricultural science and artificial intelligence, focusing on precision weed management and plant disease detection. His work leverages deep learning neural networks to address critical challenges in crop protection, particularly for smallholder farming systems. Moshia’s most cited paper, “Mexican poppy (Argemone mexicana) control in cornfield using deep learning neural networks: a perspective” (2018, 17 citations), proposes an innovative AI-driven approach to combat this persistent, noxious weed that threatens corn production through its deep root system and prolific seed bank. He further extended this methodology to plant pathology in “Identification of Citrus Canker on Citrus Leaves and Fruit Surfaces in the Grove Using Deep Learning Neural Networks” (2020, 5 citations), targeting the incurable, quarantine-class bacterial disease that endangers global citrus industries. By demonstrating that convolutional neural networks can accurately detect both weeds and diseases in real field conditions, Moshia’s work bridges the gap between advanced computational techniques and practical agricultural needs. His research offers scalable, non-chemical solutions for integrated pest management, positioning him as a key contributor to the growing field of digital agriculture and smart farming technologies.