Fruit Quality and Ripeness Estimation Using a Robotic Vision System (FQRRV)
Keywords:
Internet of Things, Deep Learning, Convolutional Neural Network, Gray-Level Co-occurrence MatrixAbstract
In agriculture, there is a large demand for smart irrigation. The task of fruit grading plays a vital role in agricultural industry as the consumer’s concern on fruit quality is high. Deep Learning helps in fruit recognition, counting, quality and ripeness estimation. The video input is fed to the system via camera and is converted into frames. Using Convolutional Neural Network (CNN), the image is processed after turning it into grayscale image. CNN and Gray-Level Co-occurrence Matrix (GLCM) are used to extract the features of the fruit and measure the quality by comparing them to the trained photos. Finally, the type, quantity and quality of the fruit is recognized and sends an email with an image if any disease detected through Internet of Things (IoT).
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