RGB camera-based monocular stereo vision applied in plant phenotype: A survey - Yin, Hua; Luo, Shan; Tang, Jianjun; Hua, Jing (Computers and electronics in agriculture, 2024)
•The applications of RGB camera-based monocular stereo vision techniques in the plant phenotype are reviewed.•The advantages and disadvantages of various methods applied in current studies are analyzed.•The guide of different application scenarios, image acquisition mode and monocular vision algorithms is summarized.•Deep learning shows great potential in RGB camera-based monocular stereo vision domain. The breeding of plants with superior traits and the improvement of cultivation means are two essential ways to achieve yield growth and quality promotion. Phenotype, which is the result of the interaction between genes and the environment, plays a key role in understanding plant geometry, growth and development. However, inefficient manual phenotypic measurement has become the main bottleneck restricting the advancement of related technologies. The monocular stereo vision system based on an RGB camera is considered as a promising approach for achieving high-throughput three-dimensional phenotypic data acquisition. This approach is cost-effective, highly efficient, and accurate. This work presents a comprehensive summary of the eight commonly used three-dimensional reconstruction methods in monocular stereo vision, along with three common image acquisition methods (circular, fixed, and straight) applied in plant phenotyping. Through a systematic review of the literature published in the past decade, this paper highlights the application of these systems and matching methods in three-dimensional plant phenotypic research. Additionally, this paper provides a discussion on the advantages and disadvantages of different approaches. At present, monocular stereo vision systems based on a single RGB camera are widely utilized to acquire diverse plant traits due to their affordability and convenience. Different application scenarios have corresponding mechanical structure and data processing methods. Deep learning-based three-dimensional reconstruction methods have demonstrated promising results and significant potential across all three common image acquisition methods. However, the current effectiveness of deep learning in reconstruction requires further validation in the absence of datasets. Moreover, limitations exist in utilizing the results of 3D reconstruction and in the selection of experimental subjects, such as vertical farming. To advance modern breeding and intelligent cultivation, it is imperative to promote dataset collection, diversify the range of research subjects (such as edible fungi and diseased plants), and develop a novel, automated, high-throughput, four-dimensional phenotype platform. As such, monocular stereo vision systems based on an RGB camera, coupled with expanded applications and the development of more efficient reconstruction algorithms, will undoubtedly emerge as a focal point for future researches.