An internet of things field crop leaf disease detection method based on full convolution network

A fully convolutional network, field crop technology, applied in the field of IoT field crop leaf disease detection, can solve the problems of limited detection performance, high storage overhead, low computing efficiency, etc., to reduce dimensions, parameters, and simple processes. Effect

Pending Publication Date: 2019-02-19
郑州西亚斯学院
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Problems solved by technology

Although this method achieves high detection accuracy, the disadvantages of this method are large storage overhead and low computational efficiency
Moreover, si...

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  • An internet of things field crop leaf disease detection method based on full convolution network
  • An internet of things field crop leaf disease detection method based on full convolution network
  • An internet of things field crop leaf disease detection method based on full convolution network

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Embodiment Construction

[0036] In order to make the purpose and technical solution of the present invention clearer, the implementation steps in the present invention will be described in detail below in conjunction with the accompanying drawings.

[0037] The present invention comprises the following steps:

[0038] Step 1: Collect 3 000 images of diseased leaves based on the Internet of Things. Considering that the size of the lesion in the leaf image is small and the shape is close to a circle, when generating the candidate area, select 3 smaller areas and 3 colors similar to the lesion, and generate the boundary circle area of ​​9 candidate areas, and mark Training images, the original training set containing 3000 images with sample labels is obtained.

[0039] Step 2: Normalize the scale of each leaf image sample in the original training set obtained in step 1: scale each leaf image sample in the original training set to a 220×220 size image, see for details figure 2 .

[0040] Step 3: After...

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Abstract

The invention relates to an internet of things field crop leaf disease detection method based on the whole convolution network, which utilizes the internet of things to collect a plurality of images of the diseased leaves, labels the images, and then normalizes the scale of each image sample of the diseased leaves. The training set is expanded to 20 images by four preprocessing operations: translation, rotation, scaling and color shaking. All samples in the training sample set are averaged, and then each training sample in the training sample set is subtracted from the averaged value, and thenscrambled to form the averaged training set. The FCN is constructed by training the FCN with the samples from the training set after averaging. In the test, the full-size leaf image is used as the input to detect the disease on the FCN after training. The invention can learn the multi-level features from low to high, quickly realizes the disease detection with high precision, and is especially suitable for the leaf disease detection of crops based on the video leaf image of the Internet of Things.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a method for detecting leaf diseases of field crops in the Internet of Things based on a fully convolutional network. Background technique [0002] Crop diseases have seriously affected the yield, quality and sales of crops. Disease control is an important link in crop production management, and it is also an important expense. The premise of crop disease control is to detect the occurrence of diseases in time and identify the types of crop diseases. The occurrence of most diseases first causes symptoms on the leaves of crops. In fact, leaf symptoms are the main basis for crop disease detection. However, manual detection of crop leaf diseases is a time-consuming, expensive task that requires long-term training of professionals. The automation of crop leaf disease detection based on the Internet of Things is the basis for the development of intelligent agriculture. Detect...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0012G06T2207/20084G06T2207/20081G06T2207/20021G06T2207/10024G06T2207/10004G06T2207/30004G06F18/2413
Inventor 邵彧朱新慧张善文井荣枝张高杰
Owner 郑州西亚斯学院
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