Invariable of the agriculture type (precision, smart, or digital), the monitoring process of factors that increase the crop yield and growth is mostly non-ML, manually structured approaches with practical pain points. In this scenario, to reduce monitoring costs and maintenance efforts, there is a requirement for low-cost semi-autonomous distributed systems that can remotely collect plant data and perform standalone ML-based analytics without depending on cloud servers or the internet. In this work, we provide an embedded ML pipeline, which users can use/follow for end-to-end solution design and implementation for any of their use-cases. To demonstrate the pipeline, we use it to collect image data, train a CNN-based regression algorithm, perform hardware-specific tuning, generate optimized code, and deploy binaries on Sony Spresense setup. The initial testing shows that even the resource-constrained MCU-based Spresense, in real-time (992 ms), high performance (96.2% accuracy, 1.86 cm2RMSE), could analyze a plant in a semi-autonomous environment to predict the leaf area and plant growth.

Transmitting updates of high-dimensional models between client IoT devices and the central aggregating server has always been a bottleneck in collaborative learning - especially in uncertain real-world IoT networks where congestion, latency, bandwidth issues are common. In this scenario, gradient quantization is an effective way to reduce bits count when transmitting each model update, but with a trade-off of having an elevated error floor due to higher variance of the stochastic gradients. In this paper, we propose ElastiCL, an elastic quantization strategy that achieves transmission efficiency plus a low error floor by dynamically altering the number of quantization levels during training on distributed IoT devices. Experiments on training ResNet-18, Vanilla CNN shows that ElastiCL can converge in much fewer transmitted bits than fixed quantization level, with little or no compromise on training and test accuracy.