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Field broccoli growth vigor monitoring system and method based on deep learning

A deep learning and monitoring system technology, applied in the field of intelligent agriculture, can solve problems such as large demand for raw data, complex network structure, and difficulty in meeting actual production needs, and achieve the effect of strong operability and high precision

Active Publication Date: 2020-07-28
ZHEJIANG ACADEMY OF AGRICULTURE SCIENCES
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, these network structures are relatively complex and require a large amount of raw data, which is difficult to meet actual production needs.

Method used

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  • Field broccoli growth vigor monitoring system and method based on deep learning
  • Field broccoli growth vigor monitoring system and method based on deep learning
  • Field broccoli growth vigor monitoring system and method based on deep learning

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Embodiment Construction

[0034] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned herein.

[0035] The present invention combines machine vision and deep learning technology to propose a field broccoli growth monitoring system based on deep learning. First of all, through the self-developed ground platform, canopy orthophotos of multiple growth stages were obtained; using the improved U-Net full convolutional neural network proposed in the present invention to train and learn the labeled data, thereby establishing broccoli balls Segmentation model; on the basis of this model segmentation, the analysis of curd freshness was further realized by the method of maximum between-class variance (Otsu).

[003...

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Abstract

The invention discloses a field broccoli growth vigor monitoring system and method based on deep learning. The system comprises a field mobile platform and an image acquisition system. The field moving platform comprises a wheel type base, a three-degree-of-freedom support and an automatic control device. The image acquisition system comprises two industrial cameras and a workstation, the three-degree-of-freedom support is arranged on the wheel type base, the two industrial cameras are fixedly installed on the three-degree-of-freedom support, the industrial cameras are in communication connection with the workstation, and the automatic control device is used for achieving automatic and synchronous shooting of the industrial cameras and controlling the three-degree-of-freedom support to conduct lifting operation. According to the invention, machine vision and deep learning technologies are combined; a self-developed ground platform is used for training and learning annotation data basedon an improved U-Net full convolutional neural network, an Otsu algorithm is used for further achieving flower ball freshness analysis, precision performance is good, high robustness is achieved, anda certain reference value is achieved for phenotypic analysis of field broccoli in the future.

Description

technical field [0001] The invention relates to the technical field of intelligent agriculture, in particular to a field broccoli growth monitoring system and method based on deep learning. Background technique [0002] Broccoli is a cruciferous one or two-year herbaceous plant. It has high protein content and is rich in various vitamins and polyphenols, and has high planting benefits and economic value. my country is a big producer and consumer of broccoli, with the current planting area and output ranking first in the world. In the past, the growth monitoring of broccoli mainly relied on manual work, that is, the agronomists regularly and irregularly measured geometric parameters such as broccoli curd diameter and roundness in the field to obtain its dynamic growth model. However, traditional manual field surveys are time-consuming, labor-intensive, highly subjective, and cannot provide real-time data. At present, computer vision technology has been widely used in fruit ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/46G06N3/04G06N3/08G06Q50/02
CPCG06N3/08G06Q50/02G06V20/10G06V10/267G06V10/50G06V10/56G06V10/462G06N3/045
Inventor 周成全叶宏宝徐志福华珊许敏界韩恺源
Owner ZHEJIANG ACADEMY OF AGRICULTURE SCIENCES
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