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首页> 外文期刊>Journal of Sensors >A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases
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A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases

机译：基于番茄植物对自主侦察机器人的学习策略的基准，内部数据库

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摘要

Greenhouse crop production is growing throughout the world and early pest detection is of particular importance in terms of productivity and reduction of the use of pesticides. Conventional eye observation methods are nonefficient for large crops. Computer vision and recent advances in deep learning can play an important role in increasing the reliability and productivity. This paper presents the development and comparison of two different approaches for vision based automated pest detection and identification, using learning strategies. A solution that combines computer vision and machine learning is compared against a deep learning solution. The main focus of our work is on the selection of the best approach based on pest detection and identification accuracy. The inspection is focused on the most harmful pests on greenhouse tomato and pepper crops, Bemisia tabaci and Trialeurodes vaporariorum. A dataset with a huge number of infected tomato plants images was created to generate and evaluate machine learning and deep learning models. The results showed that the deep learning technique provides a better solution because(a) it achieves the disease detection and classification in one step, (b) gets better accuracy, (c) can distinguish better between Bemisia tabaci and Trialeurodes vaporariorum, and (d) allows balancing between speed and accuracy by choosing different models.
机译：温室作物产量在全世界越来越多，在生产力和减少杀虫剂的情况下，早期的害虫检测特别重要。传统的眼睛观察方法对大作物不足。计算机愿景和深度学习的最新进步可以在提高可靠性和生产力方面发挥重要作用。本文介绍了两种不同探索自动害虫检测和识别方法的发展和比较，采用学习策略。将计算机视觉和机器学习结合的解决方案与深度学习解决方案相结合。我们工作的主要重点是基于害虫检测和识别准确性选择最佳方法。该检验专注于温室番茄和胡椒作物，Bemisia Tabaci和Trizuurodes Vaporiorum上最有害的害虫。创建具有大量受感染的番茄植物图像的数据集以产生和评估机器学习和深度学习模型。结果表明，深度学习技术提供了更好的解决方案，因为（a）它在一步中达到疾病检测和分类，（b）得到更好的准确性，（c）可以区分Bemisia tabaci和Trigehurodes vaporariorum之间更好，并（d ）通过选择不同的型号，允许平衡速度和准确性之间的平衡。

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  • 来源
    《Journal of Sensors》 |2019年第2期|共15页
  • 作者

    Aitor Gutierrez; Ander Ansuategi; Loreto Susperregi; Carlos Tubio; Ivan Rankic; Libor Lenza;

  • 作者单位

    Autonomous and Intelligent Systems Unit IK4-Tekniker;

    Autonomous and Intelligent Systems Unit IK4-Tekniker;

    Autonomous and Intelligent Systems Unit IK4-Tekniker;

    Autonomous and Intelligent Systems Unit IK4-Tekniker;

    Department of Chemistry and Biochemistry Faculty of AgriSciences Mendel University;

    Department of Chemistry and Biochemistry Faculty of AgriSciences Mendel University;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 TP212;
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