离心式变量撒肥颗粒肥料分布检测系统研究

Development of Particle Distribution Detection System for Centrifugal Variable-rate Fertilizer

在农业生产中,施肥是提高作物产量和质量的重要手段之一。然而过量的施肥会引发各种环境问题,如陆地和水体污染,影响人们的健康和生态系统的平衡。因此,研究变量式施肥机的作业施肥精度具有重要的意义。施肥精度是农业生产中非常重要的一环,直接关系到农作物的产量和品质。合理施肥能够提高作物的养分利用率,减少肥料浪费,同时还能避免肥料过量使用对环境带来的负面影响。本研究以肥料颗粒为研究对象,以基于深度学习的目标检测为技术手段,运用Python、NumPy库实现图像HOG特征提取,将可视化技术与深度学习相结合,旨在对田间工况下的肥料颗粒进行目标检测。

In agricultural production,fertilization is one of the important means to improve crop yield and quality.However,excessive fertilization can cause various environmental problems,such as land and water pollution,affecting people’s health and the balance of the ecosystem.Therefore,it is of great significance to study the operating accuracy of variable type fertilizer applicator.The precision of fertilization is a very important link in agricultural production,which is directly related to the yield and quality of crops.Studies have shown that rational fertilization can improve nutrient utilization of crops,reduce fertilizer waste,and avoid the negative impact of excessive fertilizer use on the environment.In this study,fertilizer particles are taken as the research object,deep learning-based target detection is taken as the technical means,Python and NumPy library are used to realize HOG feature extraction,and visualization technology is combined with deep learning,aiming at target detection of fertilizer particles under field conditions.