Data-driven leaf pruning based on weekly light integral: importance of dynamic defoliation strategy.

Area of Science:

• Horticulture
• Plant Physiology
• Agricultural Engineering

Background:

• High-wire tomato production involves labor-intensive tasks like leaf pruning, crucial for balancing plant growth.
• Current leaf pruning practices lack defined optimal timing for labor efficiency and crop performance.
• Understanding light dynamics within the canopy is key to refining pruning strategies.

Purpose of the Study:

• To introduce and evaluate a data-driven leaf pruning method based on weekly light integral (WLI).
• To compare the efficacy of WLI-based pruning with conventional methods in terms of labor demand and crop performance.
• To assess the impact of different photosynthetically active radiation (PAR) ranges on WLI monitoring.

Main Methods:

• Developed a WLI-based pruning method removing lower leaves when WLI below the canopy drops below a set threshold.
• Evaluated pruning intervals (1 week) and number of leaves pruned (3) per event.
• Compared WLI-based pruning (using PAR and ePAR ranges) against a conventional control method in a commercial greenhouse setting.

Main Results:

• WLI-based pruning reduced pruning events by 35-42% compared to the control, irrespective of PAR range.
• This method resulted in more leaves per plant due to supplemental lighting, without significant differences in stem growth or cumulative yield.
• The WLI-based method significantly increased the total soluble solid content of harvested tomatoes.

Conclusions:

• Leaf pruning strategies should adapt to in-canopy light availability, influenced by solar and supplemental lighting.
• Monitoring WLI below the canopy is an effective strategy for optimizing leaf pruning timing.
• This data-driven approach reduces labor costs and enhances fruit quality without compromising tomato yield.