Deep Learning based Disease Detection in Tomatoes

S. J, Eugine Mary. J, Dikshna. U, Blessy Athisaya Malar, A. Diana Andrushia, T. Neebha
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引用次数: 5

Abstract

Agriculture is the common for all creatures in the world. The agriculture products play a vital role in the food commodities. This paper presents the defect detection of tomatoes using deep learning techniques. The RGB tomato images are taken for the experiment. The pre-processing steps of background removal is used to separate the tomatoes from the background. Heuristic threshold based background removal is adhered. The convolution neural network based deep learning method is adopted to detect and classify the tomatoes. Three layers of CNN has used for the disease detection of tomatoes. The real time tomato images are used for this study and achieved 0.971 detection accuracy.
基于深度学习的番茄病害检测
农业是世界上所有生物的共同事业。农产品在粮食商品中占有重要地位。本文介绍了利用深度学习技术对番茄进行缺陷检测。实验采用RGB番茄图像。背景去除预处理步骤用于将番茄从背景中分离出来。采用启发式阈值法去除背景。采用基于卷积神经网络的深度学习方法对番茄进行检测和分类。三层CNN被用于番茄的疾病检测。本研究采用实时番茄图像,检测准确率达到0.971。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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