In response to the problems of low fertilizer application efficiency, poor operation quality, and uneven application of fertilizer by domestic tea garden trenching and fertilizing machines, an automatic depth-adjusting double screw trenching and fertilizing machine was designed. The machine uses a double spiral furrowing and fertilizer application method, which can complete the integrated operation of furrowing, fertilizer application, and mulching at one time. The key components of the machine such as the screw-type fertilizer discharger, trenching, and fertilizer application mechanism are designed using theoretical analysis, and the trenching depth is automatically adjusted through the hydraulic control system to maintain a consistent depth. A single-factor test and a quadratic regression rotary orthogonal test were conducted to select the diameter of the spiral fertilizer discharger, the rotational speed of the spiral fertilizer discharger, and the rotational speed of the trenching and fertilizer application mechanisms. Based on these tests, the fertilizer application performance of the fertilizer machine was evaluated, and a mathematical model of the fertilizer application volume and coefficient of variation was established. The influence of the test factors on the coefficient of variation was also analyzed. In the study, 58.36 and 480.35 r/min were found to be the optimal rotational speeds for spiral fertilizer discharge and trenching and fertilizer application, respectively, while 88.90 mm was found to be the optimal diameter for spiral fertilizer discharge. The coefficient of variation for the spiral fertilizer discharge was 3.95%, which meets the tea plantation’s fertilizer application requirements.
Near infrared spectroscopy (NIRS) combined with various chemometrics methods was tried to identify the fresh tea leaves at different altitudes quickly and nondestructively. Three kinds of samples were collected, then scanning NIRS, conducting spectral preprocessing to remove noise information, using backward interval partial least squares to screen characteristic spectral intervals, going on principal component analysis, respectively. Finally, least squares support vector machine method (LS-SVM) was applied to establish NIRS models, whose robustness was tested by prediction set samples. The best pretreated method was the combination of multivariate scattering correction and the first derivative. Six characteristic spectral intervals were screened, and the corresponding spectral wavenumbers were 4821.
Aiming at the problems of low fertilization efficiency, mainly the process operation and inconsistent fertilization depth of domestic tea garden fertilizer machines, a single-spiral fixed depth ditching and fertilizing machine is appropriately designed. This machine is capable of performing the integrated operation of ditching, fertilization, and covering soil at the same time through the operation mode of single-spiral ditching and fertilization. The theoretical analysis and design of the structure of the main components are properly carried out. The fertilization depth can be adjusted through the established depth control system. The performance test reveals that the single-spiral ditching and fertilizing machine exhibits a maximum stability coefficient of 96.17% and a minimum of 94.29% in terms of trenching depth and a maximum of 94.23% and a minimum of 93.58% in terms of fertilization uniformity, meeting the production requirements of tea plantations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.