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Pre-harvest mango yield prediction using artificial neural networks based on leaf nutrient variability
Abdullah Alebidi1, Khalid F Almutairi1, Rashid S Al-Obeed1
1Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.
Peerj
|July 10, 2026
Summary
Accurate mango yield prediction is possible using leaf nutrient data. An artificial neural network (ANN) model accurately forecasts yield, aiding agricultural planning and reducing waste.
Area of Science:
- Agricultural Science
- Plant Physiology
- Data Science
Background:
- Accurate pre-harvest mango yield prediction is crucial for optimizing agricultural practices, minimizing food waste, and ensuring food security.
- Variability in mango yield is influenced by seasonal factors and leaf nutrient status, necessitating advanced predictive models.
Purpose of the Study:
- To develop and validate an artificial neural network (ANN) model for accurate pre-harvest mango yield prediction.
- To investigate the relationship between leaf nutrient concentrations and mango yield across different agricultural practices and seasons.
Main Methods:
- Selected nine mango orchards with diverse agricultural practices to collect leaf samples and yield data.
- Analyzed leaf concentrations of nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), chlorophyll a (Chl a), chlorophyll b (Chl b), and total carbohydrates (Carbs).
- Developed an ANN model using eight leaf nutrient parameters to predict mango yield, evaluating its performance with R² and mean absolute percentage error (MAPE).
Main Results:
- The ANN model demonstrated high accuracy in predicting mango yield, achieving an R² of 0.975 and a MAPE of 3.02% on the testing dataset.
- Leaf concentrations of Chl a, Chl b, and the carbohydrate fraction were identified as the most significant contributors to yield prediction.
- A reverse trend was observed between the carbohydrate to nitrogen (C/N) ratio and yield during the growing season, particularly between ON and OFF seasons.
Conclusions:
- The developed ANN model effectively predicts mango yield by capturing the complex, non-linear interactions between leaf nutrient status and productivity.
- The findings highlight the potential of using leaf nutrient analysis and ANN modeling for precise agricultural management in mango cultivation.
- This approach supports enhanced decision-making for farmers, contributing to improved crop management and resource utilization.
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