Abstract
Nowadays, 3D imaging of plants not only contributes to monitoring and managing plant growth, but is also becoming an essential part of high-throughput plant phenotyping. In this paper, an inexpensive (less than 70 USD) and portable platform with binocular stereo vision is established, which can be controlled by a laptop. In the stereo matching step, an efficient cost calculating measure—AD-Census—is integrated with the adaptive support-weight (ASW) approach to improve the ASW’s performance on real plant images. In the quantitative assessment, our stereo algorithm reaches an average error rate of 6.63% on the Middlebury datasets, which is lower than the error rates of the original ASW approach and several other popular algorithms. The imaging experiments using the proposed stereo system are carried out in three different environments including an indoor lab, an open field with grass, and a multi-span glass greenhouse. Six types of greenhouse plants are used in experiments; half of them are ornamentals and the others are greenhouse crops. The imaging accuracy of the proposed method at different baseline settings is investigated, and the results show that the optimal length of the baseline (distance between the two cameras of the stereo system) is around 80 mm for reaching a good trade-off between the depth accuracy and the mismatch rate for a plant that is placed within 1 m of the cameras. Error analysis from both theoretical and experimental sides show that for an object that is approximately 800 mm away from the stereo platform, the measured depth error of a single point is no higher than 5 mm, which is tolerable considering the dimensions of greenhouse plants. By applying disparity refinement, the proposed methodology generates dense and accurate point clouds of crops in different environments including an indoor lab, an outdoor field, and a greenhouse. Our approach also shows invariance against changing illumination in a real greenhouse, as well as the capability of recovering 3D surfaces of highlighted leaf regions. The method not only works on a binocular stereo system, but is also potentially applicable to a SFM-MVS (structure-from-motion and multiple-view stereo) system or any multi-view imaging system that uses stereo matching.
Highlights
Greenhouse cultivation, which is a kind of highly integrated facility agriculture, is becoming increasingly important in raising the efficiency of agricultural production and solving the world’s food shortage
Low-cost and efficient 3D imaging methods facilitate the development of high-throughput plant phenotyping, and the obtained 3D features of plants are helpful in variety selection and the breeding process in modern agriculture
We evaluate the performance of AD-Census cost measure with adaptive support-weight (ASW) against several other stereo matching techniques, including the ASW algorithm [39], Graph Cuts algorithm (GC) [37], and Semi-global Matching (SGBM) algorithm [38] on the Middlebury datasets [48]
Summary
Greenhouse cultivation, which is a kind of highly integrated facility agriculture, is becoming increasingly important in raising the efficiency of agricultural production and solving the world’s food shortage. Modern greenhouse cultivation still faces challenges to increase output and reduce energy consumption. For this purpose, an economic, efficient, and intelligent greenhouse environment control method is needed to guarantee a temperate environment for each plant during its whole growth period, and to accomplish high yields and economic benefits. As a branch of remote sensing, 3D imaging uses the most direct manner to fully present the continuous growth of greenhouse plants via imaged shapes and spatial structures. Low-cost and efficient 3D imaging methods facilitate the development of high-throughput plant phenotyping, and the obtained 3D features of plants are helpful in variety selection and the breeding process in modern agriculture. A 3D imaging system is able to classify better shaped ornamentals that are later sold at a higher price
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