Multi-phenotypic parameters extraction and biomass estimation for lettuce based on point clouds

Li, Maowei; Li, Guixin; Li, Jinsong; Zheng, Lihua; Zhang, Man; Wang, Minjuan

doi:10.1016/j.measurement.2022.112094

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…The product of the multiplied area per lettuce head with the maximum height resulted in the highest correlation coefficient with fresh weight ( Appendix A Table A3 ). Three papers using the [ 50 , 51 , 52 ] dataset had a RMSE up to 25.3. As indicated, we obtained a lower accuracy, however, we should take into account that the datasets are not fully comparable.…”

Section: Discussionmentioning

confidence: 99%

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Lettuce Production in Intelligent Greenhouses—3D Imaging and Computer Vision for Plant Spacing Decisions

Πετροπούλου

Marrewijk

Zwart

et al. 2023

Sensors

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Recent studies indicate that food demand will increase by 35–56% over the period 2010–2050 due to population increase, economic development, and urbanization. Greenhouse systems allow for the sustainable intensification of food production with demonstrated high crop production per cultivation area. Breakthroughs in resource-efficient fresh food production merging horticultural and AI expertise take place with the international competition “Autonomous Greenhouse Challenge”. This paper describes and analyzes the results of the third edition of this competition. The competition’s goal is the realization of the highest net profit in fully autonomous lettuce production. Two cultivation cycles were conducted in six high-tech greenhouse compartments with operational greenhouse decision-making realized at a distance and individually by algorithms of international participating teams. Algorithms were developed based on time series sensor data of the greenhouse climate and crop images. High crop yield and quality, short growing cycles, and low use of resources such as energy for heating, electricity for artificial light, and CO2 were decisive in realizing the competition’s goal. The results highlight the importance of plant spacing and the moment of harvest decisions in promoting high crop growth rates while optimizing greenhouse occupation and resource use. In this paper, images taken with depth cameras (RealSense) for each greenhouse were used by computer vision algorithms (Deepabv3+ implemented in detectron2 v0.6) in deciding optimum plant spacing and the moment of harvest. The resulting plant height and coverage could be accurately estimated with an R2 of 0.976, and a mIoU of 98.2, respectively. These two traits were used to develop a light loss and harvest indicator to support remote decision-making. The light loss indicator could be used as a decision tool for timely spacing. Several traits were combined for the harvest indicator, ultimately resulting in a fresh weight estimation with a mean absolute error of 22 g. The proposed non-invasively estimated indicators presented in this article are promising traits to be used towards full autonomation of a dynamic commercial lettuce growing environment. Computer vision algorithms act as a catalyst in remote and non-invasive sensing of crop parameters, decisive for automated, objective, standardized, and data-driven decision making. However, spectral indexes describing lettuces growth and larger datasets than the currently accessible are crucial to address existing shortcomings between academic and industrial production systems that have been encountered in this work.

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Section: Discussionmentioning

confidence: 99%

“…The ability of networks to learn plant features from single lettuce images can be determined by the recently published lettuce dataset [ 49 ]. At the moment, three papers have been published, obtaining high accuracy to estimate fresh weight from the images with a Root Mean Squared Error (RMSE) up to 25.3 g [ 50 , 51 , 52 ].…”

Section: Introductionmentioning

confidence: 99%

Lettuce Production in Intelligent Greenhouses—3D Imaging and Computer Vision for Plant Spacing Decisions

Πετροπούλου

Marrewijk

Zwart

et al. 2023

Sensors

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“…However, in the context of the inability to fuse multi-band reflectivity and threedimensional structural information of plants through a single sensor, achieving organ-level high-precision three-dimensional digital identification of plant physical and chemical phenotypic parameters faces huge challenges, as shown in the literature [38][39][40][41], scholars use existing optical sensors to obtain point cloud information of soybeans, lettuce and small potted plants to achieve plant morphological phenotype information assessment such as segmentation and counting of organs between plants. However, physical and chemical parameters such as chlorophyll, nitrogen content, and water content are difficult to detect.…”

Section: Discussionmentioning

confidence: 99%

Three-Dimensional Quantification and Visualization of Leaf Chlorophyll Content in Poplar Saplings under Drought Using SFM-MVS

Tian,

Zhang,

Bian

et al. 2023

Forests

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As global temperatures warm, drought reduces plant yields and is one of the most serious abiotic stresses causing plant losses. The early identification of plant drought is of great significance for making improvement decisions in advance. Chlorophyll is closely related to plant photosynthesis and nutritional status. By tracking the changes in chlorophyll between plant strains, we can identify the impact of drought on a plant’s physiological status, efficiently adjust the plant’s ecosystem adaptability, and achieve optimization of planting management strategies and resource utilization efficiency. Plant three-dimensional reconstruction and three-dimensional character description are current research hot spots in the development of phenomics, which can three-dimensionally reveal the impact of drought on plant structure and physiological phenotypes. This article obtains visible light multi-view images of four poplar varieties before and after drought. Machine learning algorithms were used to establish the regression models between color vegetation indices and chlorophyll content. The model, based on the partial least squares regression (PLSR), reached the best performance, with an R2 of 0.711. The SFM-MVS algorithm was used to reconstruct the plant’s three-dimensional point cloud and perform color correction, point cloud noise reduction, and morphological calibration. The trained PLSR chlorophyll prediction model was combined with the point cloud color information, and the point cloud color was re-rendered to achieve three-dimensional digitization of plant chlorophyll content. Experimental research found that under natural growth conditions, the chlorophyll content of poplar trees showed a gradient distribution state with gradually increasing values from top to bottom; after being given a short period of mild drought stress, the chlorophyll content accumulated. Compared with the value before stress, it has improved, but no longer presents a gradient distribution state. At the same time, after severe drought stress, the chlorophyll value decreased as a whole, and the lower leaves began to turn yellow, wilt and fall off; when the stress intensity was consistent with the duration, the effect of drought on the chlorophyll value was 895 < SY-1 < 110 < 3804. This research provides an effective tool for in-depth understanding of the mechanisms and physiological responses of plants to environmental stress. It is of great significance for improving agricultural and forestry production and protecting the ecological environment. It also provides decision-making for solving plant drought problems caused by global climate change.

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“…These algorithms need to transform 3D point cloud data into 2D raster data and detect the building contours using image processing algorithms. Since many unreal points are generated in the interpolation process of original point cloud data, the edge tracked after the point cloud data are converted into the depth image is just a rough boundary of discrete point sets, which leads to the low accuracy [10][11][12]. In addition, the resolution of the depth image depends on the grid size [13].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands

Wang,

Zang,

et al. 2024

Remote Sensing

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Because of the complex structure and different shapes of building contours, the uneven density distribution of airborne LiDAR point clouds, and occlusion, existing building contour extraction algorithms are subject to such problems as poor robustness, difficulty with setting parameters, and low extraction efficiency. To solve these problems, a building contour extraction algorithm based on multidirectional bands was proposed in this study. Firstly, the point clouds were divided into bands with the same width in one direction, the points within each band were vertically projected on the central axis in the band, the two projection points with the farthest distance were determined, and their corresponding original points were regarded as the roof contour points; given that the contour points obtained based on single-direction bands were sparse and discontinuous, different banding directions were selected to repeat the above contour point marking process, and the contour points extracted from the different banding directions were integrated as the initial contour points. Then, the initial contour points were sorted and connected according to the principle of joining the nearest points in the forward direction, and the edges with lengths greater than a given threshold were recognized as long edges, which remained to be further densified. Finally, each long edge was densified by selecting the noninitial contour point closest to the midpoint of the long edge, and the densification process was repeated for the updated long edge. In the end, a building roof contour line with complete details and topological relationships was obtained. In this study, three point cloud datasets of representative building roofs were chosen for experiments. The results show that the proposed algorithm can extract high-quality outer contours from point clouds with various boundary structures, accompanied by strong robustness for point clouds differing in density and density change. Moreover, the proposed algorithm is characterized by easily setting parameters and high efficiency for extracting outer contours. Specific to the experimental data selected for this study, the PoLiS values in the outer contour extraction results were always smaller than 0.2 m, and the RAE values were smaller than 7%. Hence, the proposed algorithm can provide high-precision outer contour information on buildings for applications such as 3D building model reconstruction.

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Multi-phenotypic parameters extraction and biomass estimation for lettuce based on point clouds

Cited by 10 publications

References 24 publications

Lettuce Production in Intelligent Greenhouses—3D Imaging and Computer Vision for Plant Spacing Decisions

Lettuce Production in Intelligent Greenhouses—3D Imaging and Computer Vision for Plant Spacing Decisions

Three-Dimensional Quantification and Visualization of Leaf Chlorophyll Content in Poplar Saplings under Drought Using SFM-MVS

Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands

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