A Cluster-Based Approach to Support the Delineation of Management Zones in Precision Agriculture

Abstract

In this paper we propose a cluster-based approach for the delineation of management zones in precision agriculture. The proposed approach was built following the steps of data mining for the clustering task, resulting in a computer application that generates maps of management zones and yield areas, allowing to compare them using known statistical indexes. The basis for this implementation was a model previously published in the literature that uses only historical productivity, soil electrical conductivity and relief data to generate the maps. The main difference of our work with respect to the previous model is the clustering algorithms used in the step of extracting patterns. While the original model uses only the fuzzy c-means algorithm, the model developed in this study uses the GKCluster extension to this algorithm, able to detect clusters with different geometrical shapes. From the tests performed with the new proposed model, we achieved about 76% of correlation between maps of yield and management zones from kappa index, and about 85% of correlation from overall accuracy. The original model reached, according to the authors, a maximum correlation of 49% from kappa index, and 70% from overall accuracy.

Algorithms for improving the process of modeling complex systems based on big data: On the example of regional agricultural production

“…The effectiveness of analyzing the optimal management laws of production in the agrosystem based on big data systematization was justified by researchers Nabila Chergui and others (2022) [4], New approaches to multispectral imaging based on big data in the development of production processes in the agricultural network are covered in the scientific works of Charles Weiss (2019) [5], "A Cluster-Based Approach to Support the Delineation of Management Zones in Precision Agriculture" Eduardo Antonio Speranza et al (2014) [6], "Big data analytics in Agriculture" by Debdeep Bose. (2020) [7], "Data Science in Agriculture -Advancing Togetherand Benefiting Farmers".…”

Data analytics for crop management: a big data view

“…In addition, the recently collected data integrates knowledge of experts and farmers experiences on their fields, which improves significantly the quality of the data [84,141]. Advanced imaging enhancement techniques improve further the data quality, and they offer the ability to track the development of crops and provide a Geo-referenced data that can describe the spatial and the temporal variability of soil and crops variables at high resolution, covering large areas [17,84,101,132,133,141,151].…”

QUALITY MANAGEMENT IN THE AGRO-INDUSTRIAL COMPLEX ON THE BASIS OF TERRITORIAL-SECTORAL PLACEMENT AND SCIENTIFIC & TECHNICAL DEVELOPMENT

“…The perspectives of optimization of territorial-sectoral placement of the AIC companies with the help of clustering on the basis of cases that reflect successful experience of various countries of the world are substantiated in D' Urso et al (2019) by the example of Sardinia, in Chiapparino and Morettini (2018) by the example of Italy, in Zhang et al (2017) by the example of China, and in Chatterjee and Ganesh-Kumar (2016) by the example of India. Speranza et al (2014) emphasizes the expedience of usage of clustering for optimization of territorial-sectoral placement of the AIC companies that conduct precision farming. Bogoviz et al (2019b) notes the necessity for using not only domestic but also foreign economic opportunities for optimization of territorial-sectoral placement of the AIC companies.…”