AgriTech - Assess grain quality using Vision Analytics | Tensorflow

Overview

The use case covers the assessment of grain quality by performing segmentation and classification on a heap of grains.

Digital Driver

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To assess the quality of an agriculture yield there is a need for manual labor where a human must take a look at the overall yield to find the ratio of healthy against the total number of grains where grains could be damaged, broken, foreign matter, etc.

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With the help of Image segmentation, the model can extract individual grains from a heap and then classify each grain based on the classes provided.

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By taking out a small heap from a sack, and then capturing a picture from the mobile, the platform will get the required healthy grain ratio as well as provide details for each type of grain.

Business Value

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Reduces the time taken to segregate the grains based on quality.

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The assessed data can be logged in some datasets to gain insights over different timespans, such as months, seasons, etc to make better business decisions.

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The platform can be completely cloud-native. The ML model can be offered as an API service to the end-user and can be integrated into any device.

Technology

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