Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows.

The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigation. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy. To address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. First, the fruit tree trunk is identified based on the improved YOLOv4 model; second, the camera coordinates of the tree trunk are calculated using the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined, the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall rate of the improved YOLOv4 model for fruit tree trunk detection are 5.92% and 7.91% higher, respectively, than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows and provide a reference for the autonomous visual navigation of orchard robots.

Automatic Recognition of Ripening Tomatoes by Combining Multi-Feature Fusion with a Bi-Layer Classification Strategy for Harvesting Robots.

Automatic recognition of ripening tomatoes is a main hurdle precluding the replacement of manual labour by robotic harvesting. In this paper, we present a novel automatic algorithm for recognition of ripening tomatoes using an improved method that combines multiple features, feature analysis and selection, a weighted relevance vector machine (RVM) classifier, and a bi-layer classification strategy. The algorithm operates using a two-layer strategy. The first-layer classification strategy aims to identify tomato-containing regions in images using the colour difference information. The second classification strategy is based on a classifier that is trained on multi-medium features. In our proposed algorithm, to simplify the calculation and to improve the recognition efficiency, the processed images are divided into 9 × 9 pixel blocks, and these blocks, rather than single pixels, are considered as the basic units in the classification task. Six colour-related features, namely the Red (R), Green (G), Blue (B), Hue (H), Saturation (S) and Intensity (I) components, respectively, colour components, and five textural features (entropy, energy, correlation, inertial moment and local smoothing) were extracted from pixel blocks. Relevant features and their weights were analysed using the iterative RELIEF (I-RELIEF) algorithm. The image blocks were classified into different categories using a weighted RVM classifier based on the selected relevant features. The final results of tomato recognition were determined by combining the block classification results and the bi-layer classification strategy. The algorithm demonstrated the detection accuracy of 94.90% on 120 images, this suggests that the proposed algorithm is effective and suitable for tomato detection.

Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm.

Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.