Aiming at the problems such as excessive redundant information, small proportion of target area and randomness of target location in the collected corn grain images, a new corn grain image slicing method based on color space (HSV) threshold segmentation was proposed in this paper, which improved the recognition accuracy of damaged and mildewed corn   grain. Firstly, the segmentation threshold was determined, the corn grain contour was extracted, and the cropping  coordinates were determined and the image was clipped according to the vertex coordinates of the minimum external rectangle frame of the contour. Secondly, the weight of GoogLeNet model without image slicing processing and with image slicing processing were obtained. After the first, second and third convolution layer and inception5b module of GoogLeNet, the Grad-CAM visualization method was used to visualize the features extracted from different convolutional layers. Finally, based on the accuracy of verification set, the ability of the two models to extract intact, damaged and mildewed corn grain features was evaluated. The results showed that the method proposed in this paper could improve the verification set accuracy to 93.74%, which was 7.99% higher than that of the data set without image slicing processing on GoogLeNet. The models concerned areas were displayed by the Grad-CAM visualization method, and the features extracted from the network were interpreted visually, which verified the effectiveness of this method and provided a new idea for the pretreatment and identification of corn kernel image.

 To effectively solve the problems of missed seeding, reseeding and uneven seeding in seeding, as well as to improve seeding quality and increase grain yield, intelligent detection technology was applied to the seeding process. This paper introduced the research progress of the existing seeding detection technology, underground seed noncontact detection technology and variable seeding technology. It focused on the analysis of the working principles and structural characteristics of various types of sensors used in intelligent detection technology of planters, and summarized the advantages and disadvantages of various types of sensors, as well as the problems and shortcomings in actual use. It was concluded that the photoelectric sensors were easily polluted and had detection blind areas, the capacitive sensor was susceptible to parasitic capacitance, and the output impedance was large. The piezoelectric sensor easily caused seed congestion, and the machine vision sensor is antiinterference poor ability. Based on the analysis of various sensors, it summarized the problems existing in the intelligent detection of domestic planters: the low detection accuracy and antiinterference ability of sensors, the single type of agricultural sensors, the low level of intelligence, the mismatch between agricultural machinery and detection devices, etc. Additionally, the application of machine vision technology in intelligent detection of planter, the application of infrared detection and machine vision detection in underground noncontact detection, the development direction of intelligent control system and the combination of variable seeding technology and 3S system were prospected. It provides a reference for further optimization of domestic planters intelligent detection technology.

As a multi-excitation source vibration system in a complex farming environment, the vibration mechanism of the silage corn harvester is difficult to be thoroughly described theoretically. To investigate the analysis methods suitable for studying its vibration characteristics, a silage corn harvester test bench was built in this paper. A 24-bit INV3062-C1(S) general-purpose dynamic test and acquisition instrument was used to test the vibrations of the kneading roll, the fixed knife, and the frame position at different speeds. The mean, variance, and RMS values of the vibration amplitude of the test bench were analyzed. The vibration signal could be approximated as conforming to the characteristics of smooth random vibration. The time-domain characteristics and vibration frequency distribution law of the vibration under different working conditions were obtained. The results show that with the increase of motor speed, the vibration intensity of the whole machine increases, among which the vibration amplitude of the kneading roller position is the largest, the frame is the second, and the fixed knife is the smallest. Under the straw feeding condition, with the increase of speed, the rate of the frame amplitude is higher than that of the kneading roller and fixed knife, and the frame is the most sensitive to the change of motor speed. The influence of corn straw feeding on the vibration of the test bench is larger at low speed (900 r/min) and medium speed (2 000-4 000 r/min), and less at high speed (4 500 r/min). The vibration frequency of the test stand is concentrated in the range of 166.7-185 Hz, 250.7-269.6 Hz, 527-559 Hz, 746.8-776.2 Hz, and 872.9-904.8 Hz, which is mainly the multi-frequency component of the motor rotation frequency. At medium and high speed, the vibration of the motor has a greater impact on the vibration of the test stand and has less impact in the low-speed state. In the design of silage harvester, consideration can be given to arranging reinforcement bars at the frame position and adding vibration isolation between the silage harvester frame and the engine to reduce the impact of vibration on the silage corn harvester. The study results can provide a reference for reducing the vibration of the whole machine of the silage harvester and for the design and optimization of the harvesting machine in the complex farming environment.