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Transfer Learning Based Model for Classification of Cocoa Pods

机译：基于转移学习的可可豆分类模型

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Over time, more sophisticated open source convolutional neural networks are becoming accessible for use. In conjunction with transfer learning, this allows projects with more focused and industry-specific requirements to generate positive results and make innovations within their field. This paper further describes the process of refining a deep learning model trained to identify cocoa pods from their surroundings, to additionally differentiate the ripe cocoa pods that are ready to be harvested. This is an ongoing project aiming to eventually automate the harvesting of cocoa pods on cocoa plantations, using automated robotic workers which can gather and transport products. This refinement is a vital step towards eventually circumnavigating the pitfalls which have, until now, left the cocoa industry working at pre-industrial standards. The obtained results show the satisfactory ability of the model proposed to classify correctly cocoa pods within static images.
机译：随着时间的流逝，越来越复杂的开源卷积神经网络变得可以使用。结合转移学习，这可以使具有更突出针对性和特定于行业要求的项目产生积极的成果并在其领域内进行创新。本文进一步描述了改进深度学习模型的过程，该模型经过训练可从周围环境中识别可可豆荚，以额外区分准备收获的成熟可可豆荚。这是一个正在进行的项目，旨在使用可收集和运输产品的自动化机器人工人，最终使可可种植园中可可豆荚的收获自动化。这种精炼是迈向最终规避陷阱的关键一步，这些陷阱至今仍使可可行业保持工业化前的标准。获得的结果表明，该模型具有令人满意的能力，可以对静态图像中的可可豆荚进行正确分类。

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