A Deep Learning-Based Spatial and Temporal Data: Plant-Growing Case Study

Abstract

Deep learning is a technique for image processing and data analysis with promising results and large potential. We investigated the performance of Deep Convolutional Neural Network (DCNN) for recognizing our spatiotemporal data in surveillance camera images. We studied how the magnitude of image dataset affected DCNN base models. We extracted spatialtemporal data into seven different interval datasets of Okra vegetation and applied them to two well-known convolutional networks; AlexNet and GoogLeNet. We experimented with spatiotemporal datasets on the convolutional networks and compared them in different epochs. The 1-Minute, 15-Minute, and 30-Minute periodic spatiotemporal datasets can achieve an excellent deep learning model with accuracy higher than 99% for both AlexNet and GoogLeNet.