Grape yield analysis with 3D cameras and ultrasonic phased arrays : a thesis by publications presented in fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Albany, New Zealand - Parr, Baden (Massey University, 2024)
Accurate and timely estimation of vineyard yield is crucial for the profitability of vineyards. It enables better management of vineyard logistics, precise application of inputs, and optimization of grape quality at harvest for higher returns. However, the traditional manual process of yield estimation is prone to errors and subjectivity. Additionally, the financial burden of this manual process often leads to inadequate sampling, potentially resulting in sub-optimal insights for vineyard management. As such, there is a growing interest in automating yield estimation using computer vision techniques and novel applications of technologies such as ultrasound. Computer vision has seen significant use in viticulture. Current state-of-the-art 2D approaches, powered by advanced object detection models, can accurately identify grape bunches and individual grapes. However, these methods are limited by the physical constraints of the vineyard environment. Challenges such as occlusions caused by foliage, estimating the hidden parts of grape bunches, and determining berry sizes and distributions still lack clear solutions. Capturing 3D information about the spatial size and position of grape berries has been presented as the next step towards addressing these issues. By using 3D information, the size of individual grapes can be estimated, the surface curvature of berries can be used as identifying features, and the position of grape bunches with respect to occlusions can be used to compute alternative perspectives or estimate occlusion ratios. Researchers have demonstrated some of this value with 3D information captured through traditional means, such as photogrammetry and lab-based laser scanners. However, these face challenges in real-world environments due to processing time and cost. Efficiently capturing 3D information is a rapidly evolving field, with recent advancements in real-time 3D camera technologies being a significant driver. This thesis presents a comprehensive analysis of the performance of available 3D camera technologies for grape yield estimation. Of the technologies tested, we determined that individual berries and concave details between neighbouring grapes were better represented by time-of-flight based technologies. Furthermore, they worked well regardless of ambient lighting conditions, including direct sunlight. However, distortions of individual grapes were observed in both ToF and LiDAR 3D scans. This is due to subsurface scattering of the emitted light entering the grapes before returning, changing the propagation time and by extension the measured distance. We exploit these distortions as unique features and present a novel solution, working in synergy with state-of-the-art 2D object detection, to find and reconstruct in 3D, grape bunches scanned in the field by a modern smartphone. An R2 value of 0.946 and an average precision of 0.970 was achieved when comparing our result to manual counts. Furthermore, our novel size estimation algorithm was able accurately to estimate berry sizes when manually compared to matching colour images. This work represents a novel and objective yield estimation tool that can be used on modern smartphones equipped with 3D cameras. Occlusion of grape bunches due to foliage remains a challenge for automating grape yield estimation using computer vision. It is not always practical or possible to move or trim foliage prior to image capture. To this end, research has started investigating alternative techniques to see through foliage-based occlusions. This thesis introduces a novel ultrasonic-based approach that is able to volumetrically visualise grape bunches directly occluded by foliage. It is achieved through the use of a highly directional ultrasonic phased array and novel signal processing techniques to produce 3D convex hulls of foliage and grape bunches. We utilise a novel approach of agitating the foliage to enable spatial variance filtering to remove leaves and highlight specific volumes that may belong to grape bunches. This technique has wide-reaching potential, in viticulture and beyond.
Listed in 2024 Dean's List of Exceptional Theses
Keywords: Grapes; Yields; Analysis; Computer vision; Industrial applications; Three-dimensional imaging; Ultrasonic imaging; agritech; agriculture; viticulture; machine learning; 3D cameras; yield estimation; ultrasonic; Dean's List of Exceptional Theses; 400907 Industrial electronics; 460304 Computer vision