Fig 2 - uploaded by Andreas Heiß
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Satellite view with the georeferenced measuring points (green) of the second N application (ESRI Inc., 2020).
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Increasing environmental concerns are a driving force in the search for ways to improve the efficiency of mineral nitrogen (N) fertilization. Spectral sensors to determine the crop's supply status are among the most mature precision agriculture technologies to adapt the N dose site-specifically to the crop's need. By using artificial intelligence t...
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... were of interest for the present study. Furthermore, the TR, the SNref, as well as the cutoff SN (SNcut) were considered. The latter is defining a biomass threshold, below which the system is strongly decreasing the DRYNS. The measurements took place on three fields with winter wheat (Triticum aestivum L.) during the N fertilizing season 2019 (Fig. 2). The fields were Table 1. Key data of the measurements Lammwirt 01 May 2019 31 54.3 20 30 21.7-75.2 N2B Binsensee 25 April 2019 31 80.4 20 40 26.9-103.7 ...
Citations
... Later on, in 2005 N-Sensor ALS (Active Light Source) was developed to overcome the dependency of the previous sensor on an external light source. It is a tractor roof-mounted wide area coverage sensor with two diode array spectrometers on either side of the tractor, a fiber optics and a microprocessor (Reusch 2005;Heiß et al. 2020;Kushwah, Chouriya, et al. 2024). ...
Nitrogen (N) is the key macronutrient for sustainable growth and higher productivity of crops. Optimized nitrogen-use-efficiency (NUE) enhances productivity and reduces the environmental impacts as well as input cost of production. NUE can be improved by the site-specific application of nitrogen fertilizer. Variable rate nitrogenous fertilizer can be applied after assessing the real-time status of the plant nitrogen. The assessment of the plant nitrogen can be obtained by various destructive and nondestructive techniques. This article aims to provide a thorough overview of the approaches available for determining plant N levels. The destructive techni-ques (Kjeldahl method and Dumas combustion) are tedious and time-con-suming but more accurate. The most adopted nondestructive methods such as the use of Leaf Color Chart (LCC), digital sensors (SPAD meter, CCM-200, GreenSeeker, Crop Circle and Yara N-Sensor, OptRxVR Crop Sensor, ISARIA, CropSpec, MultiplexVR) and digital imaging-based techni-ques of plants are rapid in N level estimation. Nondestructive techniques utilize the optical property of leaves to assess the N level of plants. However, digital sensor-based estimation has certain limitations, including chlorophyll saturation, illumination effect, less accuracy and the high initial cost of instruments. Furthermore, more research should be carried out to overcome these limitations and improve their efficiency.
... This paper describes in a first step, how the agronomic algorithms of the Yara N-Sensor system can be imitated with a fuzzy logic-based model in an automated way. Initial approaches to that are presented in Heiß et al. (2020). The aim is to use this as the basis for including more parameters towards developing a multi-parametric system. ...
Nitrogen (N) excess due to mineral fertilization in conventional crop farming has a significant negative impact on the environment. Variable rate N application (VRNA) is a promising tool to increase N recovery rates in spatially heterogeneous fields. Real-time sensor systems for VRNA usually consider only the crop's N status and their fertilization algorithms are abundantly deterministic. Due to their education and professional experience, farmers have a considerable knowledge base that should be used to describe the dynamic and non-deterministic interactions of multiple parameters for a locally adapted N fertilization. Fuzzy systems present an effective way to integrate expert knowledge into an automated multi-parametric control. This paper describes, how fuzzy logic can be used to fuse the plant-related information from a real-time sensor system with further parameters to create a multi-parametric system for VRNA. Using sets of input-output data acquired with a Yara N-Sensor ALS2 system, an adaptive, fuzzy logic-based model of its agronomic algorithms was identified, optimized and validated. The results indicated high accordance with the N-Sensor algorithms and good automated adaptability to different calibrations with values of the Pearson correlation coefficient higher than 0.99 and a maximum percentage root mean square error of 0.14%. In a case study, the model was combined with the apparent soil electrical conductivity (ECa) as an indicator for spatially varying soil productivity, as well as a case distinction for different weather conditions. Simulations with historic ECa data and N-Sensor recordings have shown the high flexibility of the multi-parametric fuzzy expert system. With the presented method, specific deficiencies of one-parametric approaches can be moderated and the application can be adapted to the prevailing conditions in a straightforward manner. Also, the target orientation could be influenced based on the specific preferences of the expert.
