Smart Seed Classification System based on MobileNetV2 Architecture

• Published in: 2022 2nd International Conference on Computing and Information Technology (ICCIT) pp. 217 - 222
• Main Authors: Hamid, Yasir, Wani, Sharyar, Soomro, Arjumand Bano, Alwan, Ali A., Gulzar, Yonis
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.01.2022
• Subjects: Agriculture; Artificial Intelligence; Convolutional Neural Network; Deep learning; Feature extraction; Machine Learning; Memory architecture; MobileNetV2 architecture; Precision Agriculture; Productivity; Profitability; Seed Classification; Training
• DOI: 10.1109/ICCIT52419.2022.9711662

The agricultural transformation in the last decade using artificial intelligence has led to significant gains in productivity and profitability. The traditional machine learning approaches present inherent limitations in extracting features and information from image data. Deep learning techniques, particularly CNN's, help to overcome these limitations due to their multi-level architecture. Various deep learning applications in agriculture include crop disease identification, fruit classification, and germination rate monitoring. Seed image analysis is considered a significant task for the preservation of biodiversity and sustainability. This research uses MobileNetV2, a deep learning convolutional neural network (DCNNs) for seed classification. This model has been preferred due to its simple architecture and memory-efficient characteristics. A total of 14 different classes of seeds were used for the experimentation. The results indicate accuracies of 98% and 95% on training and test sets, respectively.