Linking the Laboratory and the Field in Potato Early Dying Detection: From Spectral Signatures to Vegetation Indices Obtained with Multispectral Cameras Coupled to Drones

Abstract

Potato production systems present various phytosanitary problems. Among these, potato early dying (PED) caused by Verticillium spp. is a disease that is difficult to detect in its early stages and whose expression occurs in critical growing phases of the crop, such as tuber filling, generating a high economic impact. The objective of this work was to use spectral data to classify potato plants and identify the degree of severity of PED using spectral signatures and multispectral images captured on potato plants under greenhouse and commercial production conditions. Methods such as principal component analysis (PCA), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) algorithms were implemented. All algorithms performed well; however, the RF was more accurate after iteration. The RF had a good capacity for indirect detection of PED, with an average accuracy of 60.9%. The wavelengths related to the red and red edges, especially from 710 to 735 nm, proved to be highly informative. As a result of the congruence between field and greenhouse data, the RECI, NDRE, VWI, and GRVI spectral indices were consistent with the discrimination of symptoms and PED severity levels. Identified wavelengths can be applied in the design of optical sensors that, together with the use of ML algorithms, can be implemented in the remote detection of early death in potato crops.

Keywords

• remote sensing tools
• machine learning
• spectral signatures
• indirect disease detection