Optimal deep generative adversarial network and convolutional neural network for rice leaf disease prediction

Rice which is a staple food crop in most Asian countries mainly suffers from higher yield loss due to different factors, and one of the common factors that affect rice yield is rice leaf disease. Many countries do not allow the plantation of new rice varieties unless they are disease-resistant. Hence, disease control serves as an important factor for rice production and this can be effectively accomplished by identifying the rice disease at an early stage which is required for timely pesticide application and disease control. The rice leaf diseases are mainly inspected by the farmers with their naked eye which is error-prone, time-consuming, and often leads to huge yield loss if predicted wrong. This process is effectively handled by different researchers via the use of computer vision and machine learning techniques. To overcome the drawbacks associated with manual processing and low recognition accuracy of different statistical and machine learning techniques, this paper presents a novel concept that uses 3D 2D CNN for feature extraction and an improved backtracking search (IBS) algorithm-optimized deep generative adversarial network (DGAN) for classification. The 3D2D deep convolutional neural network (DCNN) is formed by integrating the 3D fast-learning block with a 2DCNN for extracting the rice disease features such as lesions and shape. The IBS algorithm-optimized GAN architecture is used for rice disease classification and the IBS algorithm is mainly adopted to overcome the instability and overfitting issues that arise in the DGAN. The proposed IBS-optimized GAN architecture offers improved accuracy of 98.7% which is relatively higher than the existing techniques such as extreme gradient boosting (XGBoost), transfer learning techniques, and support vector machine (SVM).