Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.

Our results suggest that there are different types of pathogenesis at different stages of POAG. Atrophy and degeneration of the visual-related cortex existed in the dorsal and ventral visual pathways in the advanced-late stage of POAG but were not found in the early stage of POAG using VBM. Such GM density changes are likely associated with the pathogenesis of POAG.

This paper provides an overview of the principles and theory of measuring optical properties of biological materials. It then presents the instrumentation and data analysis procedures for implementing several emerging optical techniques, including spatially resolved, time-resolved, and spatial-frequency domain, along with the standard integrating sphere method. Applications of these techniques for optical property measurement, maturity and quality assessment, and defect detection of fruits and vegetables are then reviewed, followed with discussions on issues and challenges that still need to be addressed for these emerging optical techniques. While these optical techniques are overall more sophisticated in instrumentation and computation, they are based on solid radiative transfer theory or diffusion approximation theory. Hence, measurement of optical absorption and scattering properties has the potential of providing more complete, objective information for quality evaluation of horticultural products. At present, these techniques are still slow in measurement, and prone to errors due to modeling and instrumentation deficiencies. Further research is therefore needed in using a better mathematical modeling approach, improving data acquisition accuracy and speed, and developing more robust inverse algorithms for optical property estimations.

Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of −4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.

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