Abstract
Automated Tobacco Grading Using Image Processing Techniques and a Convolutional Neural Network
Highlights
Tobacco industry is one of the strongest pillars of the world economy because of its high commercial value
Computations and implementations of the proposed approaches in tobacco leaf detection, segmentation, and classification were performed in Windows 10 64-bit operating system, 2.80 GHz Intel(R) Core(TM) i7-7700HQ CPU, 16 GB RAM, and NVIDIA GeForce GTX 1050 4GB video RAM
This graph was generated by Tensorboard which visualizes the layer output shapes at different levels, displayed in the greyed connections
Summary
Tobacco industry is one of the strongest pillars of the world economy because of its high commercial value. [1] reported that tobacco companies are among the highest profit making organizations in the world in 2017. The Imperial Tobacco Group in the United Kingdom was proclaimed as the largest global tobacco company with about 39.2 billion US dollars worth of sales. Philip Morris International and British American Tobacco came in second and third in rank respectively. A high-quality tobacco leaf production must continue to take advantage of the growing export markets and must address quality requirements for domestic cigarette manufacturing. Quality evaluation or grading of tobacco leaves plays a crucial role in quality assurance of tobacco productions
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