Nutritional Monitoring of Rhodena Lettuce via Neural Networks and Point Cloud Analysis

In traditional farming, fertilizers are often used without precision, resulting in unnecessary expenses and potential damage to the environment. This study introduces a new method for accurately identifying macronutrient deficiencies in Rhodena lettuce crops. We have developed a four-stage process....

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Bibliographic Details
Published in:AgriEngineering 2024-09, Vol.6 (3), p.3474-3493
Main Authors: Ramírez-Pedraza, Alfonso, Salazar-Colores, Sebastián, Terven, Juan, Romero-González, Julio-Alejandro, González-Barbosa, José-Joel, Córdova-Esparza, Diana-Margarita
Format: Article
Language:English
Subjects:
Artificial intelligence
Color imagery
Damage detection
Decision trees
diseased
Fertilizers
Greenhouses
Image processing
Machine learning
macronutrient
Neural networks
Nitrogen
Nutrition monitoring
point clouds
Potassium
Rhodena lettuce
Seedlings
Soil fertility
Three dimensional models
Traditional farming
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Summary:In traditional farming, fertilizers are often used without precision, resulting in unnecessary expenses and potential damage to the environment. This study introduces a new method for accurately identifying macronutrient deficiencies in Rhodena lettuce crops. We have developed a four-stage process. First, we gathered two sets of data for lettuce seedlings: one is composed of color images and the other of point clouds. In the second stage, we employed the interactive closest point (ICP) method to align the point clouds and extract 3D morphology features for detecting nitrogen deficiencies using machine learning techniques. Next, we trained and compared multiple detection models to identify potassium deficiencies. Finally, we compared the outcomes with traditional lab tests and expert analysis. Our results show that the decision tree classifier achieved 90.87% accuracy in detecting nitrogen deficiencies, while YOLOv9c attained an mAP of 0.79 for identifying potassium deficiencies. This innovative approach has the potential to transform how we monitor and manage crop nutrition in agriculture.
ISSN:2624-7402
2624-7402
DOI:10.3390/agriengineering6030198