Advance of Target Visual Information Acquisition Technology for Fresh Fruit Robotic Harvesting: A Review

Abstract

In view of the continuous increase in labor costs for complex picking tasks, there is an urgent demand for intelligent harvesting robots in the global fresh fruit cultivation industry. Fruit visual information is essential to guide robotic harvesting. However, obtaining accurate visual information about the target is critical in complex agricultural environments. The main challenges include the image color distortion under changeable natural light, occlusions from the interlaced plant organs (stems, leaves, and fruits), and the picking point location on fruits with variable shapes and poses. On top of summarizing the current status of typical fresh fruit harvesting robots, this paper outlined the state-of-the-art advance of visual information acquisition technology, including image acquisition in the natural environment, fruit recognition from the complex backgrounds, target stereo locating and measurement, and fruit search among the plants. It then analyzed existing problems and raised future potential research trends from two aspects, multiple images fusion and self-improving algorithm model.

Nighttime Harvesting of OrBot (Orchard roBot)

“…Recognizing the fruit is the first step for a successful harvest. This is the reason why most of the research has been devoted to fruit recognition [14]. The unstructured nature of the orchard plus the variability of the lighting conditions is a big challenge.…”

Nighttime Harvesting of OrBot (Orchard RoBot)

“…Recognizing the fruit is the first step to a successful harvest. This is the reason why most of the research has been devoted to fruit recognition [14]. The unstructured nature of the orchard, plus the variability of the lighting conditions, is a big challenge.…”

Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning

“…The proliferation and advancement of industrial greenhouse cultivation models have significantly boosted the mass and continuous production of fruits and vegetables, with cherry tomatoes yielding 48-57 kg/m 2 [1,2]. In such high-yield environments, the robotic farming equipment has become essential for complex and labor-intensive horticultural tasks, enhancing efficiency and optimizing production processes [3]. Commercial fruitharvesting robots have been developed and deployed using common fresh fruits such as tomatoes [4], strawberries [5], and cucumbers [6] as targets.…”