Machine Vision-Based Assessment of Fall Color Changes and its Relationship with Leaf Nitrogen Concentration

2024-04-23

AI Summary

This study addresses the challenge of non-destructive, field-based assessment of nitrogen status in apple trees. A ground-based, vehicle-mounted stereo vision system was developed to acquire multi-temporal color and 3D point cloud imagery, enabling quantitative characterization of canopy chlorosis dynamics during autumn. We propose a novel Normalized Yellowness Index (NYI), ranging from −1 to +1, and for the first time apply a gradient boosting regression model to estimate NYI (R² = 0.72), outperforming conventional K-means clustering in both accuracy and computational efficiency. Results reveal that nitrogen-deficient trees exhibit earlier onset of chlorosis, and NYI exhibits a significant negative correlation with laboratory-measured leaf nitrogen concentration (p < 0.01). The framework delivers a practical, real-time, and non-invasive visual diagnostic tool for precision nitrogen management in orchards.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Intelligent Robots: State Estimation
• Machine Learning: Ensemble Methods

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Apple( extit{Malus domestica} Borkh.) trees are deciduous, shedding leaves each year. This process is preceded by a gradual change in leaf color from green to yellow as chlorophyll is degraded prior to abscission. The initiation and rate of this color change are affected by many factors including leaf nitrogen (N) concentration. We predict that leaf color during this transition may be indicative of the nitrogen status of apple trees. This study assesses a machine vision-based system for quantifying the change in leaf color and its correlation with leaf nitrogen content. An image dataset was collected in color and 3D over five weeks in the fall of 2021 and 2023 at a commercial orchard using a ground vehicle-based stereovision sensor. Trees in the foreground were segmented from the point cloud using color and depth thresholding methods. Then, to estimate the proportion of yellow leaves per canopy, the color information of the segmented canopy area was quantified using a custom-defined metric, extit{yellowness index} (a normalized ratio of yellow to green foliage in the tree) that varied from -1 to +1 (-1 being completely green and +1 being completely yellow). Both K-means-based methods and gradient boosting methods were used to estimate the extit{yellowness index}. The gradient boosting based method proposed in this study was better than the K-means-based method (both in terms of computational time and accuracy), achieving an $R^2$ of 0.72 in estimating the extit{yellowness index}. The metric was able to capture the gradual color transition from green to yellow over the study duration. Trees with lower leaf nitrogen showed the color transition to yellow earlier than the trees with higher nitrogen. Keywords: Fruit Tree Nitrogen Management, Machine Vision, Point Cloud Segmentation, Precision Nitrogen Management

Problem

• Assessing leaf color changes in apple trees using machine vision
• Correlating leaf color transition with nitrogen concentration
• Developing a yellowness index to quantify foliage color changes

Innovation

• Machine vision system for leaf color analysis
• Gradient boosting method for yellowness index
• 3D stereovision sensor for canopy segmentation