A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards.

Area of Science:

• Agricultural Engineering
• Forestry Science
• Geospatial Technology

Background:

• Chestnut (Castanea spp.) and its byproducts are globally significant, necessitating efficient monitoring techniques.
• Advancements in simultaneous localization and mapping (SLAM) and user accessibility have increased handheld mobile laser scanning (HHLS) adoption in precision agriculture.

Purpose of the Study:

• To develop and validate a tree growth monitoring methodology using HHLS point cloud data.
• To assess the accuracy and reliability of HHLS for quantifying sweet chestnut tree growth.

Main Methods:

• A novel methodology was developed based on processing HHLS point clouds to calculate branch length via spatial discretization.
• The method was validated by comparing point clouds from two near-simultaneous scans of sweet chestnut trees.
• Longitudinal growth was monitored over 37 weeks, from spring to winter.

Main Results:

• The HHLS methodology demonstrated reliability and accuracy in monitoring sweet chestnut tree growth.
• Analysis of scans from week 0 to week 37 revealed an approximate mean growth of 0.22 m.
• A standard deviation of approximately 0.16 m indicated heterogeneous growth patterns among the trees.

Conclusions:

• HHLS provides an efficient and accurate tool for monitoring tree growth in precision agriculture applications.
• The proposed point cloud processing technique enables precise quantification of tree development over time.
• This method supports sustainable management and yield prediction for chestnut orchards.