Corn Plant Counting Using Deep Learning and UAV Images

IF 4 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-08-08 DOI:10.1109/LGRS.2019.2930549

Bruno T. Kitano, C. Mendes, A. R. Geus, Henrique C. Oliveira, Jefferson R. Souza

引用次数: 65

Abstract

The adoption of new technologies, such as unmanned aerial vehicles (UAVs), image processing, and machine learning, is disrupting traditional concepts in agriculture, with a new range of possibilities opening in its fields of research. Plant density is one of the most important corn (Zea mays L.) yield factors, yet its precise measurement after the emergence of plants is impractical in large-scale production fields due to the amount of labor required. This letter aims to develop techniques that enable corn plant counting and the automation of this process through deep learning and computational vision, using images of several corn crops obtained using a low-cost unmanned aerial vehicle (UAV) platform assembled with an RGB sensor.

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利用深度学习和无人机图像进行玉米植株计数

新技术的采用，如无人机、图像处理和机器学习，正在颠覆农业的传统概念，在其研究领域开辟了一系列新的可能性。植物密度是玉米（Zea mays L.）最重要的产量因素之一，但由于需要大量的劳动力，在大规模生产领域中，在植物出现后对其进行精确测量是不切实际的。这封信旨在开发通过深度学习和计算视觉实现玉米植株计数和这一过程自动化的技术，使用由RGB传感器组装的低成本无人机平台获得的几种玉米作物的图像。

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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来源期刊

IEEE Geoscience and Remote Sensing Letters 工程技术-地球化学与地球物理

CiteScore

7.60

自引率

12.50%

发文量

1113

审稿时长

3.4 months

期刊介绍： IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.