Agriculture becoming the major driver for Indian economy, applying some of the latest technological digital innovations to solve critical Agri-based challenges are becoming vital to improve the productivity and lower the cost of operations. Primary productivity index of agriculture is directly dependent on how much the crops escaped from attacks either by pests or by external intruders. Applying some of the advanced machine learning techniques in Computer Vision and multiple object detection algorithms in the field of Agriculture surveillance generates huge interest among farmer communities. In this paper, an aapproach which includes deployment of sensors to monitor the whole cultivation area, fixing appropriate cameras and detecting motions in the agro field, is proposed for Agro field surveillance. An orchestrated deployment of necessary sensing devices such as motion-sensing, capturing video based on demand and passes it on to the deep learning algorithms for further synthesis. The model is developed and trained leveraging technologies such as tensorflow, keras with google Colab, Jupyter notebook environment that runs entirely in the google cloud that requires very minimal setup. To evaluate the model, the authors create a test set which contains 200 captured events, more than 60,000 images that are relevant for this scope and available in public to train Deep Learning CNN based models.
Optimizing on-demand GPUs in the Cloud for Deep Learning Applications Training
