Artificial Intelligence and Machine Vision for Early-Stage Orchard Management

Dear Colleagues,

Early-stage management in fruit orchards is crucial for achieving high yields, maintaining fruit quality, and promoting long-term orchard sustainability. The timely and accurate monitoring of key phenological phases, such as flowering and fruit-setting, is essential to support informed decisions in pollination management, fertilization, and thinning practices. Traditional manual assessments of floral and fruit stages are often labor-intensive, subjective, and impractical for large-scale operations. With rapid advancements in artificial intelligence (AI), especially in computer vision (CV), automated and intelligent solutions have emerged as powerful tools to overcome these challenges. These technologies enable the precise identification of flowers and small fruits, as well as the accurate detection of developmental stages, facilitating early interventions within complex orchard environments.

This Special Issue aims to highlight cutting-edge research on the application of AI and machine vision to early-stage orchard management. Contributions addressing, but not limited to, the following topics are highly welcome:

• Early flower detection and blooming stage recognition;
• Phenological period monitoring;
• Small-fruit detection and segmentation in natural conditions;
• Early fruit growth stage classification;
• Intelligent sensor systems for real-time orchard monitoring;
• Development of orchard-specific annotated datasets;
• Deep learning models for early phenotypic analysis.

By showcasing recent innovations in AI and CV for orchard phenotyping and management, this Special Issue seeks to foster interdisciplinary collaboration among researchers in horticulture, agricultural engineering, plant phenotyping, and intelligent sensing technologies.

Dr. Weikuan Jia
Dr. Nan Wang
Dr. Danyan Chen
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• early-stage management
• fruit orchards
• phenological phases
• artificial intelligence
• computer vision
• early flower detection
• small-fruit detection and segmentation
• deep learning models