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.

该研究提出了一种新的框架，将两种不同的卷积神经网络结构相结合，以在模拟环境中同时完成作物检测和收获（机器人操控）任务。利用机器视觉实现作物自动识别，提高收获效率，但仍面临挑战。通过随机旋转、裁剪、亮度和对比度调整来生成增强图像以进行数据集生成。使用一次性算法框架进行作物定位，以及使用视觉几何组模型来确定机器人操控的抓取位置。