Abstract
In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.
Highlights
Traditional agricultural practices consider fields as homogeneous management units, under which farm operations assume no within-field variability in the soil or crop
We proposed a clustering and smoothing pipeline (CaSP) for management zones (MZs) delineation, which gives a smoothed scheme of MZs and is applicable in practice by the variable rate agricultural machinery
The clustering algorithms included k-means, fuzzy C-means (FCM), mean shift, hierarchical, and density-based spatial clustering of applications with noise (DBSCAN). These algorithms were evaluated in scenarios with and without range normalization, geographical coordinates, and feature selection
Summary
Traditional agricultural practices consider fields as homogeneous management units, under which farm operations assume no within-field variability in the soil or crop. Agricultural soils are often extremely variable in space and time, and understanding its variability is essential to successfully manage farming inputs site- and dynamically at a field scale [1]. To address within-field variability, variable management solutions are adopted using precision agriculture (PA) technologies, which aim at the sitespecific application of farm inputs (e.g., seeds, fertilizers, manure, pesticides, and water) according to the soil and crop requirements [2]. Variable rate applications— referred to as site-specific applications—are implemented in practice as map-based, sensor-based, or a combination of both approaches [3]. For both map-based and map-sensor-based solutions, the within-field variability should be classified into a few zones with similar characteristics. The accurate delineation of MZ maps is the key requirement for the successful implementation of map-based and map-sensor-based variable rate applications
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