A Deep Learning Model Compression and Ensemble Approach for Weed Detection

Ofori, Martinson
El-Gayar, Omar
O'Brien, Austin
Noteboom, Cherie
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Site-specific weed management is an important practice in precision agriculture. Current advances in artificial intelligence have resulted in the use of large deep convolutional neural networks for weed detection. In this paper, a transfer learning, model compression, and ensemble learning approach is introduced that is suitable for resource-limited hardware such as mobile and embedded devices. The resulting ensemble model achieves 91.2% classification accuracy which is comparable to the performance of state-of-the-art deep learning models (such as the vanilla VGG16, DenseNet, and ResNet) while being about 62.22% smaller in size than DenseNet (the smallest-sized full-sized model). The approach used in this study is beneficial for further development of deep convolutional neural networks on smaller resource-limited hardware typically used in agriculture, as well as other industries such as healthcare and telecommunication.
Analytics and Decision Support for Green IS and Sustainability Applications, deep convolutional neural networks, deep learning, model compression, precision agriculture, weed detection
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