Identification of Eggplant Young Seedlings Infected by Root Knot Nematodes Using Near Infrared Spectroscopy

Abstract

In this paper, eggplant young seedlings infected by root knot nematodes were identified using near infrared spectroscopy. The main research on MSC and SG pretreatment method and PCA principal component extraction method with the combination of effects on model for classification. Results show:The best classification process is to do the first MSC after SG smoothing pretreatment, after using PCA extracted as the main component of the SIMCA input variables for classification, and achieved a classification average accuracy higher than 90%. It is an effective method to classify the degree of infection of the root knot nematodes by using the visible and near infrared spectral characteristics of the eggplant leaves.

Deep Network with Score Level Fusion and Inference-Based Transfer Learning to Recognize Leaf Blight and Fruit Rot Diseases of Eggplant

“…They have been widely used due to their high speed and better accuracy. However, few studies were conducted on eggplant disease recognition, and these works mostly focused on the detection or recognition of a small number of disease categories [24,25,27]. To fill the gap, this paper proposed deep learningbased identification of leaf blight and fruit rot diseases of eggplant according to nine distinct categories through two steps of improvement.…”

“…Wei et al [25] used infrared spectroscopy to identify young seedlings in eggplant infected by root knot nematodes using multiple scattering correction (MSC) and Savitzky-Golay (SG) smoothing pretreatment method. Their method gave 90% accuracy but worked well only in normal conditions.…”