A new comprehensive index for monitoring maize lodging severity using UAV-based multi-spectral imagery

Abstract

Lodging significantly reduces crop yield and grain quality. Timely and accurately monitoring crop lodging severity to help farmers to adjust the field management and settle reasonable insurance claim. The purpose of this paper is to effectively monitor maize lodging severity using unmanned aerial vehicle (UAV) with a multi-spectral camera. Considering the plant height (PH), lodging angle (LA) and SPAD, each agronomic trait was transformed into a membership matrix by giving each factor a weight. The comprehensive superiority of each evaluation result based on agronomic trait (PH, LA, SPAD) was calculated by using the fuzzy comprehensive evaluation (FCE) method, and the comprehensive lodging evaluation index (CLEI) was constructed. The relationships between the spectral reflectance (SR), texture feature (TF), vegetation index (VI) and PH, LA, SPAD, CLEI were established. The CLEI was calculated and mapped by using the sensitive features derived from UAV-based multi-spectral imagery. According to the distribution of CLEI, the research area was divided into five grades, including non-lodging (NL), light lodging (LL), moderate lodging (ML), severe lodging (SL) and very severe lodging (VSL). The results showed that among the CLEI models constructed by single feature, VI performed best and the R2 of cross-validation was 0.66 which was higher than agronomic trait models (the R2 of PH, LA, and SPAD models were 0.56, 0.58, 0.41, respectively). The R2 and nRMSE of cross-validation of the CLEI model constructed by the top three sensitive features (CIre, NDRE, Re_Mea) were 0.80 and 23.78%, while the R2 of the PH, LA, and SPAD models were 0.74, 0.71, 0.46, respectively. The accuracy of the new comprehensive index (CLEI) model was better than that of single agronomic trait (PH, LA and SPAD) model. The CLEI proposed in this paper using UAV-based multi-spectral imagery can effectively monitor the lodging severity of maize.