Abstract
Climate change and competition among water users are increasingly leading to a reduction of water availability for irrigation; at the same time, traditionally non-irrigated crops require irrigation to achieve high quality standards. In the context of precision agriculture, particular attention is given to the optimization of on-farm irrigation management, based on the knowledge of within-field variability of crop and soil properties, to increase crop yield quality and ensure an efficient water use. Unmanned Aerial Vehicle (UAV) imagery is used in precision agriculture to monitor crop variability, but in the case of row-crops, image post-processing is required to separate crop rows from soil background and weeds. This study focuses on the crop row detection and extraction from images acquired through a UAV during the cropping season of 2018. Thresholding algorithms, classification algorithms, and Bayesian segmentation are tested and compared on three different crop types, namely grapevine, pear, and tomato, for analyzing the suitability of these methods with respect to the characteristics of each crop. The obtained results are promising, with overall accuracy greater than 90% and producer’s accuracy over 85% for the class “crop canopy”. The methods’ performances vary according to the crop types, input data, and parameters used. Some important outcomes can be pointed out from our study: NIR information does not give any particular added value, and RGB sensors should be preferred to identify crop rows; the presence of shadows in the inter-row distances may affect crop detection on vineyards. Finally, the best methodologies to be adopted for practical applications are discussed.
Highlights
According to the most recent projections presented by the Intergovernmental Panel on Climate Change (IPCC), the variation in precipitation is altering hydrological systems in many agricultural areas of the planet, affecting water resources in terms of both quantity and quality [1]
This study focuses on the crop row detection and extraction by analyzing and post-processing images acquired through a Unmanned Aerial Vehicle (UAV)
This study demonstrates the feasibility to perform crop row detection from high-resolution UAV imagery, for different crop types, including vineyards, orchards, and horticultural crops
Summary
According to the most recent projections presented by the Intergovernmental Panel on Climate Change (IPCC), the variation in precipitation is altering hydrological systems in many agricultural areas of the planet, affecting water resources in terms of both quantity and quality [1]. Variable rate irrigation is aimed at managing water inputs to match the spatial variability of the water requirements found in the field, by providing irrigation in different amounts depending on real crop requirements. This approach represents a valid solution to increase water use efficiency and water savings, and for certain crops, variable water application might lead to an increase of yield and product quality [5,6,7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
