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 cm2 RMSE), could analyze a plant in a semi-autonomous environment to predict the leaf area and plant growth.
The Paper was accepted as a Poster to TinyML Asia 2021, IEEE BigData Smartfarm 2021, and IPSN 2022.