Abstract
To minimize pesticide dosage and its adverse environmental impact, Unmanned Aerial Vehicle (UAV) spraying requires precise individual canopy information. Branches from neighboring trees may overlap, preventing image-based artificial intelligence analysis from correctly identifying individual trees. To solve this problem, this paper proposes a segmentation and evaluation method for mingled fruit tree canopies with irregular shapes. To extract the individual trees from mingled canopies, the study fitted the projection curve distribution of the interlacing tree with Gaussian Mixture Model (GMM) and solved the matter of segmentation by estimating the GMM parameters. For the intermingling degree assessment, the Gaussian parameters were used to quantify the characteristics of the mingled fruit trees and then as the input for Extreme Gradient Boosting (XGBoost) model training. The proposed method was tested on the aerial images of cherry and apple trees. Results of the experiments show that the proposed method can not only accurately identify individual trees, but also estimate the intermingledness of the interlacing canopies. The root mean squares (R) of the over-segmentation rate (Ro) and under-segmentation rate (Ru) for individual trees counting were less than 10%. Moreover, the Intersection over Union (IoU), used to evaluate the integrity of a single canopy area, was greater than 88%. An 84.3% Accuracy (ACC) with a standard deviation of 1.2% was achieved by the assessment model. This method will supply more accurate data of individual canopy for spray volume assessments or other precision-based applications in orchards.
Highlights
Aerial spraying by Unmanned Aerial Vehicles (UAV) has great application potential in China for conventional orchard farms, especially those with trees growing on hills [1]
An receiver operating characteristic curve (ROC) curve [43] is the most commonly used way to visualize the performance of a binary classifier, and area under the curve (AUC) [44] is a criterion used to quantitatively evaluate the training effect of the classification models
EvaluaTtiootnestotfhPe leerafronrinmgaanbiclietyooffDouirffmeroednelt, tMheopdeerflosrmance of XGBoost was compared with the other methods: Gradient Boosting Decision Tree (GBDT) + Random Forest (RF) model based on ensemble
Summary
Aerial spraying by Unmanned Aerial Vehicles (UAV) has great application potential in China for conventional orchard farms, especially those with trees growing on hills [1]. UAV images have become more commonly used within remote sensing studies focused on tree extraction [11,12,13]. The Hough transform is used to isolate individual trees from UAV images by applying a canopy shadow, generated from optical data and a circular matching template [14]. It is used to identify lines in images and positions of arbitrary shapes, such as circles or ellipses This method, is not ideal for interlaced orchards. GMM segregates the contiguous trees and outputs Gaussian parameters to quantify the features of mingled canopies, providing training data for Extreme Gradient Boosting (XGBoost) to evaluate intermingledness. The findings from this study will contribute to crop feature acquisition based on machine vision in general
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