Automatic crop disease recognition by improved abnormality segmentation along with heuristic-based concatenated deep learning model

Abstract

Plant diseases detection based on machine learning and computer vision can produce a significant effect on the quality and production of crops. Any changes that occur in crop quality or crop productivity may greatly reduce the national economy. Thus, the detection of plant diseases should be done at the early stage before intensively affecting crop production. A new technique named smart farming is introduced to benefit in “high-ended application of modern farming” by obtaining multiple data through live streams, social media, sensors, robots, etc. The attained data from diverse sources are required to processunder amultilevel database, which becomes more challenging while detecting plant diseases in smart farming techniques. The demands for using the machine learning approaches with unsupervised or supervised methods are increased on utilizing it in real-world applications. The main intention of this paper is to focus on the development of a novel crop disease detection model using the modified deep learning architecture. The images from different datasets with several crop diseases are collected from the public benchmark sources, and it is initially subjected to pre-processing using filtering and contrast enhancement techniques. Once the image is enhanced, the novel Optimized K-means Clustering (O-KMC) is adopted for performing the abnormality segmentation. Then, the feature extraction of the abnormality segmented images is done by the edge features and texture features. These features are utilized for disease recognition, in which the Heuristic-based Convolutional Neural Network with Recurrent Neural Network (H-C-RNN) is developed. In both segmentation and classification, the parameter improvement is performed by the Adaptive Inertia Weighted-Dragonfly Algorithm (AIW-DA). The performance of the proposed model under the different datasets is evaluated with various conventional methods that ensure the accurate identification of crop diseases in the proposed model.