The optimisation of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialisation. Nowadays, the utilisation of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of convolutional neural networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The you only look once algorithmic framework is employed with traditional rectangular bounding boxes for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.

翻译：针对常见农作物的采收过程优化对实现农业工业化具有重要意义。当前，机器视觉技术已能实现作物自动识别并提升采收效率，但仍存在技术挑战。本研究提出一种新型框架，通过融合两种独立的卷积神经网络架构，在仿真环境中同步完成作物检测与采收（机器人操作）任务。通过对仿真环境中的作物图像施加随机旋转、裁剪、亮度及对比度调整，生成增强图像数据集。采用基于传统矩形边界框的YOLO算法框架实现作物定位。随后，所提方法通过视觉几何组模型对获取的图像数据进行处理，以确定机器人操作的抓取位置。