Longsheng Fu

College of Mechanical & Electronic Engineering, Northwest A&F University, China

Kiwifruit is popular all over the world because of its unique taste and high nutritional value. Traditional kiwifruit production approach almost relies on manual operations, which is not only labor-intensive, but also easily affected by human factors, resulting in low production efficiency and unstable output. As the demand for kiwifruit continues to rise, inefficient manual operation is highly desired to be replaced by intelligent production with advanced technologies. Precision operation robots supported by artificial intelligence and automation technology have been proposed and made contributes to improve agriculture production efficiency and reduce labor usage. Therefore, this research is focused on key technologies for fully intelligent production of kiwifruit, to improve overall productivity in bud thing, flower pollination, yield estimation, and fruit picking.

1) Bud thinning: Buds of kiwifruit are usually grown in clusters, consisting of a main bud and multiple side buds. An automated robotic precision bud thing robot based on machine vision and laser was developed to automatic detect side buds and destroy them to prevent their growth. A multi-class buds labeling and detection strategy was proposed according to its different growth conditions and spatial location. A fixed-focus laser was selected as bud thing actuator for robots to destroy side buds by emit timed high energy laser pulses.

2) Flower pollination: As a typical cross-pollinated plant, kiwifruit does not have ability to achieve autonomous self-pollination. A multi-class flower detection method based on YOLOv5l was employed for detecting and determining which flowers of the canopy were in the best pollination periods. After that, a selection strategy based on Euclidean distance matching method was applied to obtain its distribution in the canopy for suitable flowers selection, which combing the agronomic characteristics of kiwifruit growth for optimal nutrients partition with quality and yield assurance. An air assisted liquid pollination approach was designed and built to collaborate with a robotic arm for targeting pollination, which achieved quantified pollination by controlling spraying time.

3) Yield estimation: Fruit yield estimation before the harvest is a crucial step to predict the required resources for workers such as packing and storage houses. A multi-target tracking and counting algorithm based on the trained YOLOv5 kiwifruit detection model and the ByteTrack tracking framework is proposed, which achieved accurate kiwifruit counting by restricting the single-row region based on the column positions fed back from the detection model.

4) Fruit picking: Kiwifruits are labeled, trained, and detected in multi-classes based on their occlusions to avoid detecting fruits occluded by branches or wires as pickable targets. Fruit nondestructive picking is separate the fruit from stem and hold the fruit to prevent it dropping. Then, it was verified by a special designed separation test of fruit and stem. After that, an end-effector was designed and manufactured, which approached a fruit from the bottom, enveloped and grabbed the fruit from two sides, and then rotated up to separate the fruit from stem. Furthermore, to investigate the drop distance of kiwifruit end-effector to container without damage, a low-damage crash study on kiwifruit was conducted.