Robots for Fruit Crops: Harvesting, Pruning, and Phenotyping
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".
Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 949
Special Issue Editor
Interests: machine learning; artificial intelligence; service robotics; perception; autonomous navigation; agriculture; robot motion planning; computer vision
Special Issue Information
Dear Colleagues,
Manual harvesting and pruning are labor-intensive and represent up to 25% of annual labor costs in fruit production, mostly in apple orchards and vineyards where operational challenges and cost constraints limit the adoption of large-scale machinery. In response, robotic solutions for harvesting, pruning, and phenotyping delicate crops are emerging as pivotal enablers of sustainable, precision agriculture. Unlike traditional field machinery, these robots must navigate complex plant architectures, reliably identify plant structure, ripeness, or stress indicators, and manipulate fragile fruits and flowers without causing damage.
Novel research contributions should focus on end effectors for fruit picking or plant pruning alongside deep-learning-based vision systems, utilizing developed solutions from controlled settings such as labs and greenhouses in real outdoor environments. Key challenges include robust perception under changing illumination and features, real-time decision-making, and the seamless integration of multi-modal sensors for detailed phenotypic trait analysis. Existing solutions for robotic pruning currently lack a unified robust solution for machine vision, plant skeletonization, and pruning strategies.
This Special Issue invites contributions that tackle unified frameworks, blending soft robotics, machine learning, and open-source datasets collected in the field, which can unlock advancements in the robotics community and develop novel capabilities in delicate crop management.
Dr. Mauro Martini
Guest Editor
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Keywords
- robotic harvesting
- robotic pruning
- machine vision for plant and fruit identification
- deep learning
- human-guided pruning policy
- soft end effector
- real-world dataset and testing
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