The use of UAVs in monitoring yellow sigatoka in banana

• Published in: Biosystems engineering Vol. 193; pp. 115 - 125
• Main Authors: Calou, Vinícius Bitencourt Campos, Teixeira, Adunias dos Santos, Moreira, Luis Clenio Jario, Lima, Cristiano Souza, de Oliveira, Joaquim Branco, de Oliveira, Marcio Regys Rabelo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-05-2020
• Subjects: Machine learning; Musa spp; Mycosphaerella musicola; PhotoScan; Remote sensing; Unmanned aerial vehicle; Musa spp; Mycosphaerella musicola; PhotoScan; Unmanned aerial vehicle; Remote sensing; Machine learning
• ISSN: 1537-5110; 1537-5129
• DOI: 10.1016/j.biosystemseng.2020.02.016

Monitoring pests and diseases is an extremely important activity for increasing productivity in agriculture. In this scenario, remote sensing, coupled with techniques of machine learning, offer new prospects for monitoring and identifying characteristic specific patterns, such as manifestations of diseases, pests, and water and nutritional stress. The aim was to use high spatial resolution aerial images to monitor the extent of an attack of yellow sigatoka in a banana crop, following the basic assumptions of identification, classification, quantification and prediction of phenotypic factors. Monthly flights were carried out on a commercial banana plantation using an unmanned aerial vehicle, equipped with a 16-megapixel RGB camera (GSD of 0.016781 m pixel−1). Five classification algorithms were used to identify and quantify the disease while field evaluations were also made following traditional methodology. The results showed that, for September 2017, the Support Vector Machine algorithm achieved the best performance (99.28% overall accuracy and 97.13 Kappa Index), followed by the Artificial Neural Network and Minimum Distance algorithms. In quantifying the disease, the SVM algorithm was more effective than other algorithms compared to the conventional methodology used to estimate the extent of yellow sigatoka, demonstrating that the tools used for monitoring leaf spots can be handled by remote sensing, machine learning and high spatial-resolution RGB images.
•High spatial resolution RGB images were obtained using a UAV.•Yellow sigatoka was identified in banana leaves using UAV RGB images.•Machine learning algorithms were successfully applied to foliar disease assessment.•The degree of severity of yellow sigatoka was assessed using images from a UAV.