Abstract

The establishment and management of cover crops are common practices widely used in irrigated viticulture around the world, as they bring great benefits not only to protect and improve the soil, but also to control vine vigor and improve the yield quality, among others. However, these benefits are often reduced when cover crops are infested by Cynodon dactylon (bermudagrass), which impacts crop production due to its competition for water and nutrients and causes important economic losses for the winegrowers. Therefore, the discrimination of Cynodon dactylon in cover crops would enable site-specific control to be applied and thus drastically mitigate damage to the vineyard. In this context, this research proposes a novel, automatic and robust image analysis algorithm for the quick and accurate mapping of Cynodon dactylon growing in vineyard cover crops. The algorithm was developed using aerial images taken with an Unmanned Aerial Vehicle (UAV) and combined decision tree (DT) and object-based image analysis (OBIA) approaches. The relevance of this work consisted in dealing with the constraint caused by the spectral similarity of these complex scenarios formed by vines, cover crops, Cynodon dactylon, and bare soil. The incorporation of height information from the Digital Surface Model and several features selected by machine learning tools in the DT-OBIA algorithm solved this spectral similarity limitation and allowed the precise design of Cynodon dactylon maps. Another contribution of this work is the short time needed to apply the full process from UAV flights to image analysis, which can enable useful maps to be created on demand (within two days of the farmer´s request) and is thus timely for controlling Cynodon dactylon in the herbicide application window. Therefore, this combination of UAV imagery and a DT-OBIA algorithm would allow winegrowers to apply site-specific control of Cynodon dactylon and maintain cover crop-based management systems and their consequent benefits in the vineyards, and also comply with the European legal framework for the sustainable use of agricultural inputs and implementation of integrated crop management.

Highlights

  • Farmersadoption of Precision Viticulture (PV) practices has been progressively growing in grape production, with the aim of optimizing crop production and increasing profitability through a moreRemote Sens. 2020, 12, 56; doi:10.3390/rs12010056 www.mdpi.com/journal/remotesensingRemote Sens. 2020, 12, 56 efficient use of farm inputs and, reducing potential environmental impacts caused by the over-application of inputs [1]

  • Unmanned Aerial Vehicle (UAV) allow necessary data to be taken at the desired time and place with ultra-high spatial resolution, which has not been feasible with traditional airborne or satellite imagery [5,6]

  • Eight VIs were tested, which increased the likelihood of selecting a well-functioning VI, and proved to be more suitable than textural information, possibly due to the larger number of classes involved in this experiment. These results showed that ExR and VEG are suitable for the accurate discrimination of C. dactylon, bare soil, and cover crop

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Summary

Introduction

Farmersadoption of Precision Viticulture (PV) practices has been progressively growing in grape production, with the aim of optimizing crop production and increasing profitability through a moreRemote Sens. 2020, 12, 56; doi:10.3390/rs12010056 www.mdpi.com/journal/remotesensingRemote Sens. 2020, 12, 56 efficient use of farm inputs (e.g., pesticides, fertilizers, water, labor, fuel, etc.) and, reducing potential environmental impacts caused by the over-application of inputs [1]. Directives included in the Common Agricultural Policy concerning both the digitizing of agriculture and the sustainable use of agricultural inputs, which foster the development of alternative strategies that limit or optimize their usage. One of the most innovative technologies that can be employed to quantify this variability is the use of Unmanned Aerial Vehicles (UAVs), due to their high spatial resolution and flexibility of flight scheduling, which are essential characteristics for accurate and timely crop monitoring [3,4]. UAVs allow necessary data to be taken at the desired time and place with ultra-high spatial resolution, which has not been feasible with traditional airborne or satellite imagery [5,6]. UAVs can acquire images with high overlaps that allow Digital Surface Models (DSMs) to be generated by using photogrammetry techniques [7]

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