Grapefruit disease and insect pest monitoring method based on Internet of Things technology

Abstract

The invention discloses a grapefruit disease and insect pest monitoring method based on the Internet of Things technology, and belongs to the field of grapefruit disease and insect pest monitoring. The grapefruit disease and insect pest monitoring method based on the Internet of Things technology predicts the grapefruit pest and disease damage occurrence trend through the steps of image preprocessing, denoising, feature extraction, SVM model construction and the like, grapefruit pest and disease damage is effectively monitored, and the method can be used through combination of image processingand SVM. Image processing technologies such as image filtering and feature extraction are used for carrying out label setting and scientific classification on features, the number of independent variables during pest and disease damage prediction is effectively reduced under the condition that the accuracy is not affected, and pest and disease damage dynamic prediction affecting grapefruit growthis achieved.

Description

technical field [0001] The invention relates to the field of monitoring of pomelo diseases and insect pests, and more specifically relates to a method for monitoring diseases and insect pests of pomelo huyou based on Internet of Things technology. Background technique [0002] Huyou planting areas are widely distributed. In recent years, the yield and quality of Huyou in some areas has declined. Occurrence will cause a large number of huyou leaves to fall, the tree will weaken, and huyou fruit will form spots, which will seriously affect the quality, yield and appearance of citrus. [0003] Traditional huyou pest information relies solely on manual collection and identification, which has low measurement and control accuracy, high labor intensity, and high cost. It lacks quantitative indicators and supporting integration technologies, and cannot accurately measure huyou's disease and pest information during the growth process. The environment is difficult to effectively sup...

AI Technical Summary

Problems solved by technology

[0002] Huyou planting areas are widely distributed. In recent years, the yield and quality of Huyou in some areas has declined. It will cause a large number of Huyou leaves to fall, the tree will weaken, and Huyou fruit will form spots, which will seriously affect the quality, yield and appearance of citrus.

[0003] Traditional huyou pest information relies solely on manual collection and identification, which has low measurement and control accuracy, high labor intensity, and high cost. It lacks quantitative indicators and supporting integration technologies, and cannot accurately measure huyou's disease and pest information during the growth process. The environment is difficult to effectively supervise, which greatly affects the yield and quality of Huyou

[0004] The existing pest monitoring methods based on the Internet of Things technology are realized through various classification algorithms, but the accuracy of the classification depends entirely on the structural characteristics of the specific classification algorithm. Since each crop has its own characteristic information, each crop has its own characteristics. Crop diseases and insect pests also have their own characteristic information. Generally, the existing classification algorithms can only be effective for one type of crop during training. Once images of different crop diseases and insect pests are used as samples, the classification effect (accuracy) will drop significantly. The classification efficiency is reduced when the intelligent recognition technology of disease and insect pest image based on network technology is applied to the field of Huyou disease and insect pest monitoring

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