Abstract

Aim/Background:
Intercropping and agroforestry have become key components of modern land management systems, aimed at optimizing crop yields and strengthening ecosystems against climate change and unpredictable weather. Traditionally, developing intercropping methods has been limited to controlled environments with a narrow range of plant combinations or specific regional focus.

Methods:
This paper proposes a novel approach that utilizes fuzzy logic and geospatial mapping techniques to autonomously generate optimal intercropping layouts tailored to specific environments. Users can select various trees, plants, and shrubs, and the system will consider multiple environmental factors.

Results:
Membership functions are developed based on the attributes each selected plant contributes to the environment—such as shade provision, nitrogen fixation, nitrogen uptake, and pest prevention—drawing on expert insights and statistical analyses.

Conclusion:
Using these membership functions, the system calculates and displays a 2D optimized layout, ensuring the selected plants are arranged efficiently to meet each plant's needs and thrive in the given ecosystem.

Key words: Apple Tree Intercropping Planner Application; Agroforestry; Fuzzy Logic; Intercropping; Food Forest; Fuzzy Inference System (FIS)