Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging

Abstract

Measurement of plant nitrogen (N), phosphorus (P), and potassium (K) levels are important for determining precise fertilization management approaches for crops cultivated in greenhouses. To accurately, rapidly, stably, and nondestructively measure the NPK levels in tomato plants, a nondestructive determination method based on multispectral three-dimensional (3D) imaging was proposed. Multiview RGB-D images and multispectral images were synchronously collected, and the plant multispectral reflectance was registered to the depth coordinates according to Fourier transform principles. Based on the Kinect sensor pose estimation and self-calibration, the unified transformation of the multiview point cloud coordinate system was realized. Finally, the iterative closest point (ICP) algorithm was used for the precise registration of multiview point clouds and the reconstruction of plant multispectral 3D point cloud models. Using the normalized grayscale similarity coefficient, the degree of spectral overlap, and the Hausdorff distance set, the accuracy of the reconstructed multispectral 3D point clouds was quantitatively evaluated, the average value was 0.9116, 0.9343 and 0.41 cm, respectively. The results indicated that the multispectral reflectance could be registered to the Kinect depth coordinates accurately based on the Fourier transform principles, the reconstruction accuracy of the multispectral 3D point cloud model met the model reconstruction needs of tomato plants. Using back-propagation artificial neural network (BPANN), support vector machine regression (SVMR), and gaussian process regression (GPR) methods, determination models for the NPK contents in tomato plants based on the reflectance characteristics of plant multispectral 3D point cloud models were separately constructed. The relative error (RE) of the N content by BPANN, SVMR and GPR prediction models were 2.27%, 7.46% and 4.03%, respectively. The RE of the P content by BPANN, SVMR and GPR prediction models were 3.32%, 8.92% and 8.41%, respectively. The RE of the K content by BPANN, SVMR and GPR prediction models were 3.27%, 5.73% and 3.32%, respectively. These models provided highly efficient and accurate measurements of the NPK contents in tomato plants. The NPK contents determination performance of these models were more stable than those of single-view models.

Four-Dimensional Plant Phenotyping Model Integrating Low-Density LiDAR Data and Multispectral Images

“…Additionally, it has leveling screws on the four legs of the platform, in order to adjust the inclination of the mechanical structure. In comparison with the structures in other investigations (Thapa et al [19], Sun et al [12,20], Zhang et al [15]), the proposed structure (shown in Figure 2) is able to freely move both the sensors and the rotating base over the rails system allowing the phenotyping of plants with a height up to 2.5 m, regardless of their structure. ), the proposed structure (shown in Figure 2) is able to freely move both, the sensors and the rotating base over the rails system allowing the phenotyping of plants with a height up to 2.5m, regardless their structure.…”

“…x + y = 0 (11) With Equation ( 11) of the XY plane of reference of the sensors, the unit vector B that is normal to the plane can also be found, as presented in Equation (12).…”

“…In [12], Sun et al used RGB-D (RGB and depth) images from a Kinect camera and images from the SOC710 hyperspectral camera to perform sensory fusion between these two sensors and obtain distributed biochemical parameters of plants. For the fusion of information between these two sensors, the authors first performed a registration between the RGB-D images and the hyperspectral images, according to the principles of the Fourier transform, calculating the translation, the rotation matrix and the scale coefficients of the images to be registered.…”

Biodegradable Food Packaging Materials and Prospects of the Fourth Industrial Revolution for Tomato Fruit and Product Handling

“…Other detection systems include and optical and thermal sensors. The optical sensors which include absorbance, reflectance, luminescence, and surface plasmon resonance techniques [ 138 ] are nondestructive methods based on multispectral three-dimensional (3D) imaging [ 139 ]. The fertilizer application and irrigation water were optimized based on the reflectance characteristics of the canopy such as leaf temperature, leaf relative water content, and leaf chlorophyll content in the field of tomato [ 138 ].…”

Point cloud registration method for maize plants based on conical surface fitting—ICP

“…Vazquez-Arellano et al 15 considered ICP algorithm to reconstruct the 3D model of maize plants, and its reconstruction effect is pleasing, but the amount of calculation is too large during registration. Sun et al 16 chose ICP algorithm for the precise registration and reconstruction of multi-spectral 3D point cloud model of plants. However, the direct use of the ICP algorithm for point cloud registration will increase the amount of calculation and make the ICP algorithm become a local optimization, and eventually lead to the registration error being too large to meet the accuracy requirements 17 .…”