Real-time object detection and robotic manipulation for agriculture
  using a YOLO-based learning approach
Hongyu Zhao Zezhi Tang Zhenhong Li Yi Dong Yuancheng Si Mingyang Lu George Panoutsos
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.