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In recent years, plant phenotyping technologies have been widely applied to evaluate complex plant traits such as morphology, physiology, ecology, biochemistry, tolerance, growth and yield. Hyperspectral/multispectral cameras, artificial lighting sources, mechanisms and computers together capture images of different species of plants. Due to the non-uniform intensity of lighting sources in different wavelengths, raw images need to be calibrated using white references. Flat white panels are typically scanned as a white reference. However, geometrical factors such as leaf tilt angles cannot be calibrated by flat white references. In this publication, the effectiveness of using angled white reference to calibrate corresponding raw images was first demonstrated. Furthermore, a 3D white referencing library integrating different angles and spatial positions in the system of a hyperspectral camera and a Kinect V2 depth sensor was created. Thus, a pixel on the leaf surface can be calibrated by a point with the nearest tilt angle and spatial position in the 3D referencing library. The validating samples for this referencing library were soybean leaves grown in a greenhouse. The results showed that the reflectance spectra after 3D calibration were closer to the standard calibration (flat leaf calibrated by flat white reference) than the conventional flat white referencing calibration. Furthermore, the pixel-level normalized difference vegetation index (NDVI) distribution over the soybean leaf surface after 3D calibration was also closer to the standard calibration. This proposed 3D white referencing method had the potential to improve calibration quality of plant images. Integrating with LiDAR sensors, this new approach has an opportunity to be applied in field environments.

This paper proposed a quaternion and complementary filter-based attitude estimation algorithm for agricultural implements in dynamic conditions. Built a minimal attitude estimation system using micro-electro-mechanical-system (MEMS) based sensors and STM32F446RC core-processor. The inertial measurement unit (IMU) ADIS 16445 is composed of a triaxial gyroscope and a triaxial accelerometer. Utilizing quaternion for attitude representation while combining the complementary filtering algorithm, the estimation error from the gyroscope was corrected during the data processing by the accelerometer. The experiments were conducted both on the SGT320E triaxial turntable platform and the ZP9500 self-propelled boom sprayer with the assistance of antenna positioning and attitude module BD982 in the field. The triaxial turntable platform dynamic experiment result showed that the average error of the attitude estimation was 0.15° and the maximal measurement error was 0.45°. Meanwhile the results in the field showed that the average error was 0.42° and the maximal measurement error was 0.62°, which satisfied the precise operation requirement of the agricultural equipment. The method proposed in this paper helps to reduce the costs of resources and improve the quality of precision farming especially the performance of agricultural implements.

In this paper, a PI path tracking controller based on look-ahead point information for a differential-drive crawler-type robot was developed. The robot is equipped with Global Navigation Satellite System (GNSS) antennas and a receiver module which are used as navigation sensors to obtain real-time estimates of the position and heading errors of the robot. To control the position of the robot with respect to the target path, a look-ahead point was generated by using Euler Transformation. The look-ahead point lateral errors were then controlled by the designed controller. Forward and backward path tracking tests with different look-ahead distances were conducted to examine the controller performance of the robot and the effect of changing look-ahead distance. Through experiment results, it was noted that the lateral errors mean value was within 0.05 m for forward path tracking and 0.06 m for backward path tracking while the heading errors mean value was within 0.5 degree for forward path tracking and 1 degree for backward path tracking. The proposed algorithms were suitable for the differential-drive robot platform for forward and backward path tracking with a reasonable look-ahead distance.

Hyperspectral sensors, especially the close-range hyperspectral camera, have been widely introduced to detect biological processes of plants in the high-throughput phenotyping platform, to support the identification of biotic and abiotic stress reactions at an early stage. However, the complex geometry of plants and their interaction with the illumination, severely affects the spectral information obtained. Furthermore, plant structure, leaf area, and leaf inclination distribution are critical indexes which have been widely used in multiple plant models. Therefore, the process of combination between hyperspectral images and 3D point clouds is a promising approach to solve these problems and improve the high-throughput phenotyping technique. We proposed a novel approach fusing a low-cost depth sensor and a close-range hyperspectral camera, which extended hyperspectral camera ability with 3D information as a potential tool for high-throughput phenotyping. An exemplary new calibration and analysis method was shown in soybean leaf experiments. The results showed that a 0.99 pixel resolution for the hyperspectral camera and a 3.3 millimeter accuracy for the depth sensor, could be achieved in a controlled environment using the method proposed in this paper. We also discussed the new capabilities gained using this new method, to quantify and model the effects of plant geometry and sensor configuration. The possibility of 3D reflectance models can be used to minimize the geometry-related effects in hyperspectral images, and to significantly improve high-throughput phenotyping. Overall results of this research, indicated that the proposed method provided more accurate spatial and spectral plant information, which helped to enhance the precision of biological processes in high-throughput phenotyping.