The growing development of the agricultural e-commerce industry is promoting the Digital transformation of agricultural production and logistics. Aiming at the problem of labor shortage in apple picking, this paper proposes a performance control method for robot apple picking based on a nonlinear wide convolutional neural network (NLWCNN) and multi-objective cooperative distance transformation algorithm (MOCDTA). This method utilizes deep learning algorithms and visual sensing technology to enable robots to accurately recognize apples and perform autonomous picking operations based on their maturity and position. We have designed a machine learning-based algorithm that enables robots to accurately recognize and distinguish apples of different maturity levels through training on a large amount of sample data. At the same time, we also combined the motion control algorithm of the robot to enable efficient picking operations based on the position and maturity information of the apples. The experimental results indicate that the NLWCNN method can significantly improve the efficiency and accuracy of apple picking, ensure the safety and traceability of agricultural products, increase consumer trust, and promote agricultural product transactions.

农业电商行业的不断发展，正在推动农业生产和物流的数字化转型。针对苹果采摘中劳动力短缺的问题，提出一种基于非线性宽卷积神经网络（NLWCNN）和多目标协作距离变换算法（MOCDTA）的机器人苹果采摘性能控制方法。该方法利用深度学习算法和视觉传感技术，使机器人能够准确识别苹果，并根据苹果的成熟度和位置进行自主采摘作业。我们设计了一种基于机器学习的算法，通过大量样本数据的训练，使机器人能够准确识别和区分不同成熟度的苹果。同时，我们还结合机器人的运动控制算法，根据苹果的位置和成熟度信息实现高效的采摘作业。实验结果表明，NLWCNN方法能够显着提高苹果采摘的效率和准确性，保证农产品的安全性和可追溯性，增加消费者信任度，促进农产品交易。