Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements

Abstract

infer the insect-damaged on cotton plants based on multispectral bands from other sensors, being a useful tool for future research that intends to evaluate it in other areas and at different field scales.

Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging

“…Other recent studies used machine or deep learning methods to detect pests based on plant responses, achieving high classification accuracy [17,70,[72][73][74]. However, these studies could not classify the level of infestation, which is the great challenge.…”

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

“…Dimensionality reduction algorithms (e.g., PCA, PLS-DA) is also often applied to transform large datasets into a lower dimensional space to facilitate further analysis. This approach was used at the disease domain, e.g., for early stage classification of anthracnose crown rot disease (by Colletotrichum fungus ) in strawberry crop with SDA, FLDA and k-NN algorithms ( Lu et al., 2017 ), classifying pre- and post- symptomatic fungal infestations of late blight ( Phytophthora infestans ) in potato leaves with PLS-DA and RF algorithms ( Gold et al., 2020 ), monitoring the rate of fungal powdery mildew ( Erysiphe graminis ) disease in wheat with PLSR, SVR and RFR algorithms ( Feng et al., 2022 ), and pre-symptomatic detection of tobacco mosaic virus in tobacco leaves with PLS-DA, RF, SVM, BPNN, ELM and LS-SVM ( Zhu et al., 2017 ); while at the plague domain was used, e.g., for predicting and classifying oat aphids ( Rhophalosiphum padi ) number in wheat cultivation with ANNs models applied to NIR and e-nose data ( Fuentes et al., 2021 ), and spectralmodelling of cotton plants against fall armyworm ( Spodoptera frugiperda ) attacks with RF, XGBoost, Naïve Bayes, LoR, SVM, MLP and k-NN algorithms ( Ramos et al., 2022 ). These tools have also shown effective in other more complex scenarios dealing with hyperspectral discrimination of various diseases or other stresses/deficiencies that may cause similar symptomatology, such as fungal Rhizoctonia root and crown rot ( Rhizoctonia solani ) diseases in sugar beet leaves with PLS, RF, k-NN, and SVM ( Barreto et al., 2020 ), bacterial spots ( Xanthomonas vesicatoria ) disease among other fungal diseases (late blight and target) in tomato leaves with PCA and k-NN algorithms ( Lu et al., 2018 ), fungal laurel wilt ( Raffaelea lauricola ) and Phytophthora root rot diseases in avocado trees with ANN-based MLP and RBF models ( De Castro et al., 2015 ), and laurel wilt disease against N and Fe nutrient deficiencies in avocado leaves with DT and MLP ( Abdulridha et al., 2018 ).…”

An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods

“…Here, we conducted one experiment with a highly redundant and complex dataset aiming to solve an agricultural-related problem. As such, while the practical value of said task was already discussed in previous research [18,8], it is still important to note that the appropriate approach to deal with these datasets necessitates a critical investigation. This paper aimed to highlight some of these aspects.…”