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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.

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.