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In plant phenotyping, there is a demand for high-throughput, non-destructive systems that can accurately analyse various plant traits by measuring features such as plant volume, leaf area, and stem length. Existing vision-based systems either focus on speed using 2D imaging, which is consequently inaccurate, or on accuracy using time-consuming 3D methods. In this paper, we present a computer-vision system for seedling phenotyping that combines best of both approaches by utilizing a fast three-dimensional (3D) reconstruction method. We developed image processing methods for the identification and segmentation of plant organs (stem and leaf) from the 3D plant model. Various measurements of plant features such as plant volume, leaf area, and stem length are estimated based on these plant segments. We evaluate the accuracy of our system by comparing the measurements of our methods with ground truth measurements obtained destructively by hand. The results indicate that the proposed system is very promising.
Most bottom-up models that predict human eye fixations are based on contrast features. The saliency model of Itti, Koch and Niebur is an example of such contrast-saliency models. Although the model has been successfully compared to human eye fixations, we show that it lacks preciseness in the prediction of fixations on mirror-symmetrical forms. The contrast model gives high response at the borders, whereas human observers consistently look at the symmetrical center of these forms. We propose a saliency model that predicts eye fixations using local mirror symmetry. To test the model, we performed an eye-tracking experiment with participants viewing complex photographic images and compared the data with our symmetry model and the contrast model. The results show that our symmetry model predicts human eye fixations significantly better on a wide variety of images including many that are not selected for their symmetrical content. Moreover, our results show that especially early fixations are on highly symmetrical areas of the images. We conclude that symmetry is a strong predictor of human eye fixations and that it can be used as a predictor of the order of fixation.
Purpose of Review
The world-wide demand for agricultural products is rapidly growing. However, despite the growing population, labor shortage becomes a limiting factor for agricultural production. Further automation of agriculture is an important solution to tackle these challenges.
Recent Findings
Selective harvesting of high-value crops, such as apples, tomatoes, and broccoli, is currently mainly performed by humans, rendering it one of the most labor-intensive and expensive agricultural tasks. This explains the large interest in the development of selective harvesting robots. Selective harvesting, however, is a challenging task for a robot, due to the high levels of variation and incomplete information, as well as safety.
Summary
This review paper provides an overview of the state of the art in selective harvesting robotics in three different production systems; greenhouse, orchard, and open field. The limitations of current systems are discussed, and future research directions are proposed.
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