Abstract
Yield monitoring systems in fruit production mostly rely on color features, making the discrimination of fruits challenging due to varying light conditions. The implementation of geometric and radiometric features in three-dimensional space (3D) analysis can alleviate such difficulties improving the fruit detection. In this study, a light detection and range (LiDAR) system was used to scan apple trees before (TL) and after defoliation (TD) four times during seasonal tree growth. An apple detection method based on calibrated apparent backscattered reflectance intensity (RToF) and geometric features, capturing linearity (L) and curvature (C) derived from the LiDAR 3D point cloud, is proposed. The iterative discretion of apple class from leaves and woody parts was obtained at RToF > 76.1%, L < 15.5%, and C > 73.2%. The position of fruit centers in TL and in TD was compared, showing a root mean square error (RMSE) of 5.7%. The diameter of apples estimated from the foliated trees was related to the reference values based on the perimeter of the fruits, revealing an adjusted coefficient of determination (R2adj) of 0.95 and RMSE of 9.5% at DAFB120. When comparing the results obtained on foliated and defoliated tree’s data, the estimated number of fruit’s on foliated trees at DAFB42, DAFB70, DAFB104, and DAFB120 88.6%, 85.4%, 88.5%, and 94.8% of the ground truth values, respectively. The algorithm resulted in maximum values of 88.2% precision, 91.0% recall, and 89.5 F1 score at DAFB120. The results point to the high capacity of LiDAR variables [RToF, C, L] to localize fruit and estimate its size by means of remote sensing.
Highlights
In the quest of decreasing farming costs and increasing sustainability, leaf area and yield monitoring are considered as one of the most important steps when implementing precision agriculture technologies in orchards [1,2]
An apple detection method based on calibrated apparent backscattered reflectance intensity (RToF) and geometric features, capturing linearity (L) and curvature (C) derived from the light detection and range (LiDAR) 3D point cloud, is proposed
Due to increased spectral resolution compared to RGB imaging, spectral imaging can potentially be utilized for fruit detection based on the reflectance intensity altered by the fruit pigments [19] by means of either multispectral [20,21] or hyperspectral cameras [22,23]
Summary
In the quest of decreasing farming costs and increasing sustainability, leaf area and yield monitoring are considered as one of the most important steps when implementing precision agriculture technologies in orchards [1,2]. Color, extracted from two-dimensional RGB images, was utilized as an indicative factor of fruit detection in apple [9,10], citrus [11,12], mango [13], and grapes [14]. Due to increased spectral resolution compared to RGB imaging, spectral imaging can potentially be utilized for fruit detection based on the reflectance intensity altered by the fruit pigments [19] by means of either multispectral [20,21] or hyperspectral cameras [22,23].
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