基于深度卷积神经模型的智能苹果采摘设想

Idea of intelligent apple picking based on deep convolutional neural model

在经济发展和科技进步的双重背景下,苹果种植受到了人们的重视,这对乡村振兴至关重要。然而,收获季节的劳动力短缺问题日益凸显,使得采摘作业开始从劳动密集型向机器自动化型转变。针对苹果的智能识别采摘,文章利用数学模型解决了复杂果园环境下的机器化采摘问题,提高了图像识别率、速度和准确率,对智能化快速采摘意义重大。其中,首先收集了大量标注苹果图像数据,用CNN模型估计成熟度,并按7:3的比例对数据进行划分以确保其具有泛化能力;其次通过深度学习提取特征并结合分类模型来评估成熟度,同时采用双边滤波、颜色空间转换、形态滤波预处理等增强图像分割能力;接着利用YOLOv4定位苹果位置并实现可视化;最终通过CNN模型分析成熟度分布,旨在提高智能采摘效率和品质评价精度,从而助力农业现代化和乡村振兴。

Under the dual background of economic development and technological progress,apple cultivation has received peoples attention,which is crucial for rural revitalization.However,the shortage of labor during the harvest season is becoming increasingly prominent,leading to a shift in picking operations from labor-intensive to machine automated.Regarding the intelligent recognition and picking of apples,this article uses mathematical models to solve the problem of mechanized picking in complex orchard environments,improving image recognition rate,speed,and accuracy,which is of great significance for intelligent and rapid picking.Firstly,a large amount of annotated apple image data was collected,and the maturity was estimated using a CNN model.The data was then divided in a 7∶3 ratio to ensure its generalization ability.Secondly,deep learning is used to extract features and combine them with classification models to evaluate maturity,while enhancing image segmentation capabilities through bilateral filtering,color space conversion,morphological filtering preprocessing,and other methods.Then use YOLOv4 to locate the location of the apple and achieve visualization.The final analysis of maturity distribution through CNN model aims to improve the efficiency and quality evaluation accuracy of intelligent harvesting,thereby assisting in agricultural modernization and rural revitalization.