Abstract
Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high- throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. The influence of climate change, and due to its unpredictable nature, 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 transition from traditional means to hydroponic systems for growing annual and perennial crops. These crop arrays possess growth patterns, which depend on environmental growth conditions in the hydroponic units. Semi-autonomous systems, which monitor these growths, may prove to be beneficial, reduce costs and maintenance efforts, and predict future yield beforehand to get an idea on 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 for taking suitable measures for selling and storage. Accurate prediction of crop development stages plays an important role in crop production management. Such predictions will also support the allied industries for strategizing the logistics of their business. Several means and approaches of predicting and demonstrating crop yields have been developed earlier with changing rate of success, as these do not take into considerations the weather and its characteristics and are mostly empirical. Crop yield estimation is also affected by taking into account a few other factors. Plant diseases enormously affect the agricultural crop production and quality with huge economic losses to the farmers and the country. This in turn increases the market price of crops and food, which increase the purchase burden of customers. Therefore, early identification and diagnosis of plant diseases at every stage of plant life cycle is a very critical approach to protect and increase the crop yield. 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 edge Impulse that runs on Edge system, Sony Spresense, in real time. This utilizes few of the six 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 efficacy and compared with the ground truth to identify pattern of growth. The image Regression and linear regression model contribute to an algorithm, which is finally used to test and predict biomass for each plant semi-autonomously.
Highlights
Advancements in computer vision and machine learning technologies have transformed plant scientists ability to incorporate high-throughput phenotyping into plant breeding
Machine learning-based highthroughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research
Hybrid and cost-effective crops are making their way into the market, but monitoring factors which affect the increase in yield of these crops, and conditions favorable for growth have to be manually monitored and structured to yield high throughput
Summary
Advancements in computer vision and machine learning technologies have transformed plant scientists ability to incorporate high-throughput phenotyping into plant breeding. The third dataset contains spatial and depth information of these plants under the same environment and observed growth patterns In this approach, we’ll be using the augmented data set to increase efficacy of model and couple images in a similar visual pattern. Greenhouse lettuce image collection and preprocessing -: The Prior to the construction of the CNN model, the original experiment was conducted at the experimental greenhouse of digital image dataset was divided into two datasets in a ratio of 8:2, i.e., a training dataset and a test dataset. The two datasets both covered all three cultivars and sampling intervals. The Sony Spresense comes with on-board 1536 kB RAM and 8192 kB ROM for Infer-
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