Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity

Abstract

It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600 nm (16 bands) and Red-NIR: 600–860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes’ surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%.

Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content

“…Cho et al [38] developed a classification model for tomato maturity involving six stages using snapshot-type hyperspectral imaging and SVC. At the laboratory level, the highest classification accuracy and F1-score were 79% and 88%, respectively, while at the field level, classification accuracy and F1-scores were 75% and 86%, respectively.…”

Rapid identification of adulterated rice using fusion of near-infrared spectroscopy and machine vision data: the combination of feature optimization and nonlinear modeling

“…It can better fit nonlinear data and discover more complex relationships between variables, making it suitable for various complex prediction and classification problems. Typical nonlinear classification modeling methods include KNN algorithm [16] , Support Vector Classification (SVC) [17] , Random forest (RF) [18] , Gradient Boosting Tree (GBT) [19] and Extreme Learning Machine [20] .…”

Hyperspectral Imaging Using a Convolutional Neural Network with Transformer for the Soluble Solid Content and pH Prediction of Cherry Tomatoes

“…The uninformative variable elimination-successive projections algorithm (UVE-SPA) was used to extract 12 characteristic wavelength points for PLS modeling, resulting in a coefficient of determination (R 2 ) of 0.9385 and 0.9347 for the training set and prediction set, respectively. The application of hyperspectral imaging (HSI) in the detection of tomato quality indicators can be divided into two categories: (1) For the detection of qualitative indicators (damage identification, spoilage detection, and maturity detection), Cho et al [16] established the detection model of tomato maturity using the support vector machine (SVM) algorithm, and the classification accuracy reached more than 75.00%. (2) For the quantitative detection of the quality ingredients using HIS, Li Liu et al [17] predicted the SSC of cherry tomatoes using PLS and LS-SVM based on HIS; Anisur Rahman et al [14] determined the SSC, MC, and pH of cherry tomatoes using PLS based on HSI.…”