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FAWINSTARNet: A Lightweight MobileNetV2 Model for Early Instar Fall Armyworm Detection in Maize
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 76 |
| Year of Publication: 2026 |
| Authors: Monica Shinde, Kavita Suryawanshi |
10.5120/ijca2026926312
|
Monica Shinde, Kavita Suryawanshi . FAWINSTARNet: A Lightweight MobileNetV2 Model for Early Instar Fall Armyworm Detection in Maize. International Journal of Computer Applications. 187, 76 ( Jan 2026), 46-51. DOI=10.5120/ijca2026926312
Abstract
The Fall Armyworm (Spodoptera frugiperda) has emerged as a major constraint on maize cultivation throughout warm-climate agricultural zones. Management practices are most effective during the earliest larval stages, making precise recognition of first- and second-instar caterpillars essential for minimizing crop damage and limiting indiscriminate pesticide application. In response to this requirement, the present work proposes FAWINSTARNet, a computationally efficient deep-learning framework derived from the MobileNetV2 family and tailored for six-category instar discrimination. An initial image repository containing 12,169 samples validated by entomological experts was systematically enlarged to 187,152 images through controlled augmentation to enhance feature variability. A group of ten pretrained convolutional neural networks was evaluated to determine an appropriate trade-off between predictive performance and resource demand. The selected FAWINSTARNet configuration attained an accuracy near 97% and was sufficiently lightweight for execution on mobile hardware, thereby supporting on-site pest surveillance for growers. The study offers a full account of dataset development, experimental procedures, architectural design, and comparative assessment of competing models.
References
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