The Internet of Things (IoT) is an emerging domain in recent days as they provided a huge number of applications in day-to-day lives. In contrast to the agricultural sector, the automatic techniques for recognizing plant disease have different benefits and pose several issues. In addition, inappropriate diagnoses are ineffectual in treating the disease and may affect the crop yield. This paper presents a novel technique for plant health monitoring by estimating sulphur dioxide. Here, the simulation of IoT was performed for improved functioning. After that, the cluster head selection and routing are performed using the proposed invasive water cycle (IWC) algorithm, which is devised by integrating the water cycle algorithm (WCA) and invasive weed optimization (IWO) algorithm. Here, the fitness function is newly modeled using certain factors involving Energy, intra and intercluster distance, and delay. After the cluster head selection and routing, the sulphur dioxide content from the soil is estimated. For sulphur dioxide estimation, the soil data is considered the input data, and then the data transformation is performed to transform the data. After that, the feature selection is performed by Mahalanobis distance, and then sulphur dioxide from the soil is estimated using Deep Q-Network, where training is performed using the proposed IWC algorithm. The proposed IWC-based Deep Q-Network offered improved performance with the highest accuracy of 0.941, and the smallest root mean square error (RMSE) of 0.242. In addition, the minimal Energy and highest Throughput are computed by the proposed IWC-based Deep Q-Network.
Summary Rice is the major crop in India. Early prevention and timely identification of plant leaf diseases are important for increasing production. Hence, an effective sunflower earthworm algorithm and student psychology based optimization (SEWA‐SPBO) based deep maxout network is developed to classify different types of diseases in rice plant leaf. The SEWA is the combination of sunflower optimization (SFO) and earthworm algorithm (EWA). Initially, the network nodes simulated in the environment capture the plant leaf images and are routed to the sink node for disease classification. After receiving the plant images at the sink node, the image is preprocessed using a Gaussian filter. Next to preprocessing, segmentation using the black hole entropic fuzzy clustering (BHEFC) mechanism is performed. Then, data augmentation is applied to segmented image results and disease classification is done by a deep maxout network. The training of the deep maxout network is done using the proposed SEWA‐SPBO algorithm. The proposed method detects the leaf disease more accurately with limited time and shows higher accuracy. Moreover, the proposed method attains higher performance with metrics, like accuracy, sensitivity, and specificity as 93.626%, 94.626%, and 90.431%, respectively.
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