Garcia Furuya, D. E., Ma, L., Faita Pinheiro, M. M., Georges Gomes, F. D., Gonçalvez, W. N., Junior, J. M., de Castro Rodrigues, D., Blassioli-Moraes, M. C., Furtado Michereff, M. F., Borges, M., Alaumann, R. A., Ferreira, E. J., Osco, L. P., Marques Ramos, A. P., Li, J., & de Castro Jorge, L. A. (2021). Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. In International Journal of Applied Earth Observation and Geoinformation (Vol. 105, p. 102608). Elsevier BV. https://doi.org/10.1016/j.jag.2021.102608
Garcia Furuya, D. E., Ma, L., Faita Pinheiro, M. M., Georges Gomes, F. D., Gonçalvez, W. N., Junior, J. M., de Castro Rodrigues, D., Blassioli-Moraes, M. C., Furtado Michereff, M. F., Borges, M., Alaumann, R. A., Ferreira, E. J., Osco, L. P., Marques Ramos, A. P., Li, J., & de Castro Jorge, L. A. (2021). Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. In International Journal of Applied Earth Observation and Geoinformation (Vol. 105, p. 102608). Elsevier BV. https://doi.org/10.1016/j.jag.2021.102608
Garcia Furuya, D. E., Ma, L., Faita Pinheiro, M. M., Georges Gomes, F. D., Gonçalvez, W. N., Junior, J. M., de Castro Rodrigues, D., Blassioli-Moraes, M. C., Furtado Michereff, M. F., Borges, M., Alaumann, R. A., Ferreira, E. J., Osco, L. P., Marques Ramos, A. P., Li, J., & de Castro Jorge, L. A. (2021). Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. In International Journal of Applied Earth Observation and Geoinformation (Vol. 105, p. 102608). Elsevier BV. https://doi.org/10.1016/j.jag.2021.102608
Garcia Furuya, D. E., Ma, L., Faita Pinheiro, M. M., Georges Gomes, F. D., Gonçalvez, W. N., Junior, J. M., de Castro Rodrigues, D., Blassioli-Moraes, M. C., Furtado Michereff, M. F., Borges, M., Alaumann, R. A., Ferreira, E. J., Osco, L. P., Marques Ramos, A. P., Li, J., & de Castro Jorge, L. A. (2021). Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. In International Journal of Applied Earth Observation and Geoinformation (Vol. 105, p. 102608). Elsevier BV. https://doi.org/10.1016/j.jag.2021.102608
Abstract
A strategy to reduce qualitative and quantitative losses in crop-yields refers to early and accurate detection of insect-damage caused in plants. Remote sensing systems like hyperspectral proximal sensors are a promising strategy for managing crops. In this aspect, machine learning predictions associated with clustering techniques may be an interesting approach mainly because of its robustness to evaluate high dimensional data. In this paper, we model the spectral response of insect-herbivory-damage in maize plants and propose an approach based on machine learning and a clustering method to predict whether the plant is herbivore-attacked or not using leaf reflectance measurements. We differentiate insect-type damage based on the spectral response and indicate the most contributive wavelengths to perform it. For this, we used a maize experiment in semi-field conditions. The maize plants were submitted to three different treatments: control (health plants); plants submitted to Spodoptera frugiperda herbivory-damage, and; plants submitted to Dichelops melacanthus herbivory-damage. The leaf spectral response of all plants (controlled and submitted to herbivory) was measured with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We evaluated the performance of different learners like random forest (RF), support vector machine (SVM), extreme gradient boost (XGB), neural networks (MLP), and measured the impact of a day-by-day analysis into the prediction. We proposed a novel framework with a ranking strategy, based on the accuracy returned by predictions, and a clusterization method based on a self-organizing map (SOM) to identify important regions in the reflectance measurement. Our results indicated that the RF-based framework algorithm is the overall best learner to deal with this type of data. After the 5th day of analysis, the accuracy of the algorithm improved substantially. It separated the three treatments into different groups with an F-measure equal to 0.967, 0.917, and 0.881, respectively. We also verified that the most contributive spectral regions are situated in the near-infrared domain. We conclude that the proposed approach with machine learning methods is adequate to monitor herbivory-damage of S. frugiperda and stink bugs like Dichelops melacanthus in maize, differentiating the types of insect-attack early on. We also demonstrate that the framework proposed for the analysis of the most contributive wavelengths is suitable to highlight spectral regions of interest.
Keywords
proximal hyperspectral sensing; precision agriculture; random forest
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.