基于图像处理和Inception-V3深度学习模型的豇豆叶片自动检测

Vijayata Choudhary, P. Guha, Kuldeep Tripathi, Sunita Mishra
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引用次数: 3

摘要

印度是一个以农业为基础的国家,大多数人口依靠农业为生。豇豆营养丰富,在维持健康生活中起着至关重要的作用。然而,迫切需要利用人工智能实施新技术来提高作物的生产力,减轻农艺师的成本、努力和损失。我们提出了一种基于深度学习算法的豇豆叶片识别模型。实验所用图像取自ICAR-NBPGR农场。我们在深度学习的框架下使用TensorFlow和Keras平台。该方法允许我们1)在图像分类的基础上重组豇豆叶片,2)估计它们的重量。启始模型是一种利用基于CNN的架构来完成与二元或多类分类相关的问题的应用程序,这些问题将再次通过使用迁移学习技术进行缓和。建立了豇豆叶片与其他叶片(芒果)的二元分类模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Detection of Cowpea leaves Using Image Processing and Inception-V3 Model of Deep Learning
India is an agriculture-based country, and most of the population depends on agriculture for livelihoods. Cowpea plays a vital role to maintain healthy life because of its nutritious parameters. However, there is an urgent need to implement new technologies using Artificial Intelligence to enhance the productivity of crops and mitigate the cost, effort and loss of the agronomist. We have proposed a model based on deep learning algorithms to identify Cowpea leaves. For experiments, images were captured from the ICAR-NBPGR farm. We use TensorFlow and Keras platform under the umbrella of Deep Learning. Such methodology permits us 1) reorganization of the cowpea leaves based on image classification and 2) to estimate their weight. Inception model is type of application utilizes CNN based architecture to complete problems related to either binary or multiclass classification, that will again moderate by using transfer learning technique. We have initiate model for binary classification of Cowpea leaves with other leaves (Mango).
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