ABSTRACT

In order to improve the harvesting efficiency of lodging crops, starting from the reel system, this paper combines intelligent image processing algorithms to identify the characteristics of lodging crops. Moreover, this paper optimises the data set, chooses L2 norm regularisation to reduce overfitting, uses exponential decay method to set learning rate, uses moving average model to speed up training convergence speed, improves classification accuracy, and simplifies the structure of the neural network to improve the real-time image recognition effect. A moving average is a statistical estimation that is used to evaluate sets of data by comparing the amounts of various subsets of the entire data set. Simultaneously, this paper improves the traditional reel structure and adds image recognition equipment to recognise crop lodging in real time during harvesting. In addition, this paper reasonably controls the reel system for harvesting, sets the reel working status by identifying lodging crops, and combines image technology to achieve efficient operation of the reel system. Finally, this paper designs experiments to verify the system performance. The research results show that the performance of the system constructed in this paper is good.

摘要

为了提高倒伏作物的收获效率，本文从卷轴系统入手，结合智能图像处理算法，对倒伏作物的特性进行识别。而且，本文对数据集进行了优化，选择L2范数正则化减少过拟合，使用指数衰减法设置学习率，使用移动平均模型加快训练收敛速度，提高分类精度，简化神经网络结构提高实时图像识别效果。移动平均值是一种统计估计，用于通过比较整个数据集的各个子集的数量来评估数据集。同时，本文改进了传统的卷轴结构，增加了图像识别设备，在收获过程中实时识别作物倒伏。此外，本文合理控制收割时的卷轴系统，通过识别倒伏作物来设置卷轴工作状态，并结合图像技术实现卷轴系统的高效运行。最后，本文设计了实验来验证系统性能。研究结果表明，本文构建的系统性能良好。