tinyML Asia 2021 Video Poster: Plant Growth and LAI Estimation using quantized Embedded Regression..

tinyML Asia 2021
Video Poster
Plant Growth and LAI Estimation using quantized Embedded Regression models for high throughput phenotyping
Dhruv Sheth, Intern, Edge Impulse

Due to the influence of climate change, and due to its unpredictable nature, the majority of agricultural crops have been affected in terms of production and maintenance. Hybrid and cost-effective crops are making their way into the market, but monitoring factors that affect the increase in yield of these crops, and conditions favorable for growth have to be manually monitored and structured to yield high throughput. Farmers are showing the transition from traditional means to hydroponic systems for growing annual and perennial crops. These crop arrays possess growth patterns that depend on environmental growth conditions in the hydroponic units. Semi-autonomous systems which monitor this growth may prove to be beneficial, reduce costs and maintenance efforts, and also predict future yield beforehand to get an idea of how the crop would perform. These systems are also effective in understanding crop drools and wilt/diseases from visual systems and traits of plants. Forecasting or predicting the crop yield well ahead of its harvest time would assist the strategists and farmers in taking suitable measures for selling and storage. Accurate prediction of crop development stages plays an important role in crop production management. In this article, I~propose an Embedded Machine Learning approach to predicting crop yield and biomass estimation of crops using an Image-based Regression approach using EdgeImpulse that runs on an Edge system, Sony Spresense, in real-time. This utilizes a few of the 6 Cortex M4F cores provided in the Sony Spresense board for Image processing, inferencing, and predicting a regression output in real-time. This system uses Image processing to analyze the plant in a semi-autonomous environment and predict the numerical serial of the biomass allocated to the plant growth. This numerical serial contains a threshold of biomass which is then predicted for the plant. The biomass output is then also processed through a linear regression model to analyze the efficacy and compare with the ground truth to identify the pattern of growth. The image Regression and linear regression model contribute to an algorithm that is finally used to test and predict biomass for each plant semi-autonomously.

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