Efficient and Rapid Classification of Various Maize Seeds Using Transfer Learning and Advanced AI Techniques
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Abstract
The classification of maize (Zea mays) is crucial for agricultural efficiency, breeding programs, and market specifications. The EfficientMaize dataset was utilized alongside Google’s Teachable Machine to develop a model separating maize varieties into classes: Bhihilifa, SanzalSima, and WangDataa. As a result, the study demonstrated that user-friendly machine learning tools are helpful in agriculture since they delivered high accuracy rates, such as 99% in Bhihilifa, 95% in SanzalSima, and 85% in WangDataa. This paper also emphasizes how modern machine-learning technologies can be accessible to farmers and researchers through tools such as Google’s Teachable Machine, which does not require coding knowledge or online expertise. To validate the results obtained with Google Teachable Machine, further analyses were conducted using RESNET-50. These findings add to previous studies on deep learning and hyperspectral imaging, leading to seed classification by increasing the potential of using machine learning to improve agricultural practices and food security.
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References
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