Search

Copyright of Smart Agriculture is the property of Smart Agriculture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to propose a universal method to detect the chlorophyll content of green plant leaves using different smart phones, thereby improving the prediction accuracy and practicability of RGB（Red, Green, Blue） images. A low-cost and portable auxiliary shoot device was also designed with a built-in active light source to reduce the influence of environment light and camera angle on the imaging quality. A color correction was used to reduce the color difference of the collected images. Three smart phones with different brands and prices were selected to carry out the experiments on sugarcane leaves. After that, some prediction models were established for the leaf chlorophyll content. A remote diagnose system under the WeChat applet was then developed to realize in situ, real-time and nondestructive detection of chlorophyll content in plant leaves. An auxiliary shoot device was designed in a shape of semi-sphere, with an inside coating of white diffuse reflective material, particularly for the even white light during image acquisition. A small shooting window was opened on the top, where eight white LEDs were distributed inside. The plant leaf was placed flat at the bottom base to capture a well-controlled leaf image in the enclosed chamber. A color checker with the 24 standard colors (X-Rite Colorchecker Classic Nano) was needed in the chamber in advance to calculate the color correction matrix using polynomial regression for each smart phone. An automatic image processing procedure was conducted to extract the RGB values of the color blocks and the leaves. Subsequently, a total of 39 color features were extracted from the leaves. A Pearson correlation analysis was implemented between the leaf color features and chlorophyll content. The results showed that most of the color features presented a strong correlation greater than 0.8 for the individual smart phone in the original and color-corrected images, indicating a relatively fewer improvement. But, the color correction of hybrid images with the chlorophyll content better improved than that of the original. The color feature (B-G-R)/(B+G) was a correlation of 0.84, indicating the highest improvement of 89% before correction. A Principal Component Analysis (PCA) was then adopted to reduce the dimension of the color features. The first five and fifteen principal components were used to build the Multivariate Linear Regression (MLR) and the Support Vector Regression (SVR) models for the individual and all smart phones. The color correction showed that there were only a few contributions to predict the chlorophyll content with the individual smart phone. More importantly, no color correction was needed if only one sensor was used to capture the RGB images. The iPhone 8P behaved the highest of R2 to reach about 0.76 and 0.79 for MLR and SVR models, respectively. But, an outstanding improvement was made for the hybrid image set with all smart phones. The SVR model performed better prediction accuracy and stability. The SVR model reached a mean R2 of 0.721 4 under the five-fold cross-validation, with an increase of 14.6%, while the mean RMSE was 0.328 8 mg/g, with a decrease of 13.3% before the color correction. Meanwhile, the lowest standard deviation of R2 was only 0.0042 in the general SVR model, compared with the rest, indicating the highest stability. The prediction was integrated with a remote server, where a WeChat applet was developed to provide a convenient way of leaf chlorophyll content prediction for the users with their own smart phones. This model can be further extended to various green plants. [ABSTRACT FROM AUTHOR]

An accurate modeling of three-dimensional (3D) point cloud has been a crucial step to extract the physical phenotyping parameters of plants, such as the plant height, leaf quantity, and leaf area. In this study, an automatic registration of point clouds with multiple calibration balls was proposed to realize the 3D modeling of plants under a complex background in the field. A low-cost depth sensor (such as Kinect V3) was also selected to capture the images. The performance of the registration was then evaluated using the multiple angles of several plants under the indoor and in-field scenes. Four procedures were included the point cloud filtering and down sampling, multiple calibration balls extraction, correspondence points matching, as well as the calculation and registration of transformation matrix. 1) The pass-through filtering with only several boundary thresholds was used to reduce the noises, while the bounding box compression was used to down sampling the point clouds. 2) A Random Sampling Consistency (RANSAC) was also used in the multiple calibration balls extraction. Furthermore, a concept of point cloud subtraction was proposed to combine with the RANSAC, in order to form an automatic extraction of multiple calibration balls. Among them, the RANSAC was utilized to extract one single object at one time. As such, the center coordinates of each calibration ball were then calculated as the featured points. 3) The distances of any two feature points were calculated in the correspondence points matching. An automatic matching of correspondence points using the distance information was also proposed to realize the self-matching of the calibration balls. 4) The singular value decomposition was adopted to solve the transformation (rotation and translation) matrix, and then the registration of two pieces of point clouds was realized eventually. The experiments were carried out in two scenes: one was the indoor scene with the flat and clean surface, and another was the field scene with the uneven and complex conditions. Five plants were chosen as the research objects, including one sugarcane (indoor), one sorghum (indoor), and three young banana plants (in-field). The Kinect V3 sensor was utilized to capture the point clouds of each object in five orientations (the horizontal orientation of 0º, 90º, 180º, 270º, and one vertical orientation). A commercial point cloud processing software named LiDAR360 was adopted for comparison with the man-aid registration. After that, the object point clouds (horizontal 90°, 180°, 270°, and vertical) of each plant was used to implement the registration into the coordinate system of the source point cloud (horizontal 0°). The registration performance was evaluated to calculate the axial errors and point errors of the transformed coordinates, as well as the source coordinates of the centers of all the calibration balls. The results showed that the registration of different point clouds was successfully implemented in the different orientations, no matter in the indoor or in-field environment. Particularly, the generated 3D model of the plant was clear in shape. Specifically, there were an average axial error of 5.8-17.4 mm and an average point position error of 13.1-28.9 mm for the registration in the different scenes, similar to the registration in the manual registration software LiDAR360. In the required calculation time, the automatic matching took about 50 s on average for the process from the extraction of calibration sphere to the registration, whereas, the LiDAR360 took about 150 s with the manual selection of the correspondence points, indicating the efficiency increased by 67%. Consequently, it can be widely expected to serve a high-precision and automatic registration of plant point clouds acquired by low-cost depth cameras in complex field environments. The finding can provide a low-cost feasible solution for the 3D modeling and the extraction of phenotyping parameters of field plants. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)