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Pesticide spraying detection method based on machine learning

A technology of pesticide spraying and machine learning, applied in the direction of instruments, computer parts, character and pattern recognition, etc., can solve the problems of widely varying effects, achieve high practicability, avoid tedious and error-prone, and high accuracy

Active Publication Date: 2014-04-02
FUDAN UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

For different environments, lighting, and different job backgrounds, the effects vary widely

Method used

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  • Pesticide spraying detection method based on machine learning
  • Pesticide spraying detection method based on machine learning
  • Pesticide spraying detection method based on machine learning

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Embodiment 1

[0039] Embodiment 1: The specific calculation steps of the method of the present invention are as attached figure 1 As shown, first, in a large number of surveillance videos of agricultural scenes, a large number of normally growing plant leaves and plant leaves with diseases and insect pests are obtained, and a random sampling strategy is adopted to extract part of the pictures from the normally growing plant leaves and the plant leaves with diseases and insect pests. Sample, extract features (including color features, HSV features, edge features, and HOG features) of each leaf image, and combine these features into feature vectors; then use SVM machine learning methods to train the feature vectors of each leaf image, After training, a classifier is formed, and then a large number of plant leaf images are detected with this classifier to detect whether plant leaves have pests or diseases.

[0040] (1) Obtain images of agricultural workers carrying pesticide boxes and spraying pe...

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Abstract

The invention belongs to the technical field of digital image processing and mode recognition, in particular to a pesticide spraying detection method based on machine learning. The method comprises the steps of obtaining a large number of videos in which a farmer with a pesticide box and without the pesticide box moves from monitoring videos of an agriculture operation scene, and detecting the pesticide box in a more proper rigidity object detecting method; extracting parts of pictures from the videos as samples, performing feature extraction on pesticide box images for each extracted picture, and combining features into feature vectors; performing cascading training on each feature vector by an Adaboost method to form an Adaboost cascading classifier, and detecting whether the pesticide box exists in the massive images comprising or not comprising the pesticide box by the classifier. The method is highly real-time and highly implementable, and the defect that the condition whether a person sprays pesticide and the pesticide spraying effect are judged by a field visit is overcome.

Description

Technical field [0001] The invention belongs to the technical field of digital image processing and pattern recognition, and specifically relates to a pesticide spraying detection method in agricultural video monitoring. Background technique [0002] As an interdisciplinary cutting-edge technology, pesticide spraying detection integrates agricultural pesticide spraying, computer industry image processing, pattern recognition, artificial intelligence and other theoretical knowledge in many different fields. It has broad application prospects in the field of automatic detection and monitoring of pesticide spraying in agricultural scenes, and it has important practical significance and theoretical value for the research of pesticide spraying detection methods. [0003] Pesticide spraying detection is to detect whether there are farmers spraying pesticides in specific areas in agricultural scenes. In the research of pesticide spraying detection methods, there are roughly two ways of t...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 冯瑞李斌蒋龙泉
Owner FUDAN UNIV
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