Machine Learning for Dynamic Management Zone in Smart Farming

TL;DR

This paper introduces a machine learning-based method for dynamically delineating management zones in smart farming, leveraging crop yield, soil, and NDVI data to optimize resource deployment and improve sustainability.

Contribution

It presents a novel dynamic zone delineation approach using clustering algorithms that incorporate multiple data sources for precise, adaptive management in agriculture.

Findings

Effective in analyzing spatial yield variations

Enables targeted variable-rate fertilization

Captures dynamic seasonal issues

Abstract

Digital agriculture is growing in popularity among professionals and brings together new opportunities along with pervasive use of modern data-driven technologies. Digital agriculture approaches can be used to replace all traditional agricultural system at very reasonable costs. It is very effective in optimising large-scale management of resources, while traditional techniques cannot even tackle the problem. In this paper, we proposed a dynamic management zone delineation approach based on Machine Learning clustering algorithms using crop yield data, elevation and soil texture maps and available NDVI data. Our proposed dynamic management zone delineation approach is useful for analysing the spatial variation of yield zones. Delineation of yield regions based on historical yield data augmented with topography and soil physical properties helps farmers to economically and sustainably deploy site-specific management practices identifying persistent issues in a field. The use of frequency maps is capable of capturing dynamically changing incidental issues within a growing season. The proposed zone management approach can help farmers/agronomists to apply variable-rate N fertilisation more effectively by analysing yield potential and stability zones with satellite-based NDVI monitoring.