![]() ![]() This leads to a limitation in distinguishing different types of deficiencies. Most of the sensors used in agriculture have limited resolution or dimensionality and are not able to acquire the full scope of available information about plants, such as their structure and leaf texture. The critical elements of such systems are the sensors that help to automate phenotyping and contribute knowledge to the final understanding of this complex relationship. ![]() There is a need for developing novel, field-deployable systems with semi- or fully-automatic processing of plant phenotypes for a suite of vegetative traits that can aid in our understanding of the relationships between genetic information and food productivity. Phenotyping of new and old varieties under varying environmental conditions to assess their suitability presents a challenge. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance.Īutomation is necessary in the agricultural industry to help accelerate the rate of increased crop productivity through genetic improvement techniques, in order to help cope with the rapid increase in human population and future demands on worldwide food security. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. ![]()
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