Maize Seed Defect Detection Using Deep Learning

In the realm of agriculture, ensuring high-quality seeds is pivotal for maximizing crop yield and productivity. This study delves into the application of deep learning techniques, specifically Google Net, to address the challenge of maize seed defect detection. By utilizing convolutional neural networks (CNNs) and leveraging the power of transfer learning, this research aims to accurately identify and classify various defects in maize seeds. The technique begins with dataset curation and preprocessing, followed by the adaptation of the Google Net architecture. The model is then trained, validated, and tested, with performance metrics.