DEEP LEARNING-BASED MODEL FOR PADDY DISEASES CLASSIFICATION BY THERMAL INFRARED SENSOR: AN APPLICATION FOR PRECISION AGRICULTURE

Abstract. The rice plant is an extremely valuable food crop worldwide. Paddy diseases not only reduce the cultivation of rice but most significantly, they contribute to environmental damage. The identification of paddy diseases before the onset of any visible signs has gained consideration with the development of deep learning (DL) and thermal infrared sensors. According to previous investigations, certain internal alterations in the paddy occur before signs of the infection become apparent. Such modifications couldn't be seen by exterior visible light sensors. On the other hand, thermal infrared sensors may be able to detect these variations, which will aid in predicting illness at earlier phases. However, there are few research articles regarding this topic. This study suggests a DL-based model for identifying paddy diseases from thermal images. The proposed DL-based model, in contrast to earlier approaches for classifying plant diseases, uses three convolutional neural networks (CNNs) with distinctive configurations. In addition, it makes use of discrete wavelet transform (DWT) to give a time-frequency illustration of the spatial deep features gathered via the three CNNs to develop the classification models instead of relying on solely spatial data like current models. Furthermore, it merges the spatial-time-frequency features of the three CNNs and uses a feature selection method based on Relief-F to choose the most beneficial attributes and reduce the dimensionality of the feature space. The outcomes of the proposed DL-based model show that spatial-time-frequency demonstrations are preferable to spatial data. The results additionally demonstrate that integrating high-level features from various CNNs can improve classification performance reaching an accuracy of 96.5% using a cubic support vector machine (SVM) classifier. Furthermore, the findings attained in this study outperform current DL-based models for plant disease classification.