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Workshop: AI for Science: Mind the Gaps

Distributed Deep Learning for Persistent Monitoring of agricultural Fields

Yasaman Esfandiari · Koushik Nagasubramanian · Fateme Fotouhi · Patrick Schnable · Baskar Ganapathysubramanian · Soumik Sarkar


Distributed deep learning algorithms have shown eminent performance in learning from data that are privately allocated between several agents. Recent advances in sensor technology have enabled the cheap collection of spatial and temporal high-resolution data for agriculture across a wide geographical area. This continuous increase in the amount of data collected has created both the opportunity for, as well as the need to deploy distributed deep learning algorithms for a wide variety of decision support tasks in agriculture. Distributed deep learning algorithms are typically divided into two major categories: centralized vs decentralized learning algorithms, depending on whether a central parameter server exists for gathering information from participating agents. In the case of rural agriculture applications, transferring a large amount of high-resolution data (e.g., images, videos) collected with IoT devices to a central server/cloud could be very expensive especially with limited communication infrastructure. This suggests the need for decentralized learning approaches, which also naturally provide some measure of privacy. Here, autoencoders are trained using a decentralized optimization algorithm to create a latent representation of growing maize plants in a large-scale field experiment involving several hundred cameras deployed in a maize genome diversity growth experiment. We trained the autoencoders for different communication network topologies of the field-deployed cameras. The feature representations from these autoencoders are then utilized to solve downstream tasks such as anomaly detection and image retrieval. Experimental results show that distributed deep learning is effective in learning from large datasets distributed among several learning agents associated with different cameras. Anomaly detection in particular was useful to make course corrections in imaging protocol and identify localized crop management.