Botanical plants suffer from several types of diseases that must be identified early to improve the production of fruits and vegetables. Mango fruit is one of the most popular and desirable fruits worldwide due to its taste and richness in vitamins. However, plant diseases also affect these plants’ production and quality. This study proposes a convolutional neural network (CNN)-based metaheuristic approach for disease diagnosis and detection. The proposed approach involves preprocessing, image segmentation, feature extraction, and disease classification. First, the image of mango leaves is enhanced using histogram equalization and contrast enhancement. Then, a geometric mean-based neutrosophic with a fuzzy c-means method is used for segmentation. Next, the essential features are retrieved from the segmented images, including the Upgraded Local Binary Pattern (ULBP), color, and pixel features. Finally, these features are given into the disease detection phase, which is modeled using a Convolutional Neural Network (CNN) (deep learning model). Furthermore, to enhance the classification accuracy of CNN, the weights are fine-tuned using a new hybrid optimization model referred to as Cat Swarm Updated Black Widow Model (CSUBW). The new hybrid optimization model is developed by hybridizing the standard Cat Swarm Optimization Algorithm (CSO) and Black Widow Optimization Algorithm (BWO). Finally, a performance evaluation is undergone to validate the efficiency of the projected model.

We synthetically applied computer vision, genetic algorithm and artificial neural network technology to automatically identify the vegetables (tomatoes) that had physiological diseases. Initially tomatoes’ images were captured through a computer vision system. Then to identify cavernous tomatoes, we analyzed the roundness and detected deformed tomatoes by applying the variation of vegetable’s diameter. Later, we used a Genetic Algorithm (GA) based artificial neural network (ANN). Experiments show that the above methods can accurately identify vegetables’ shapes and meet requests of classification; the accuracy rate for the identification for vegetables with physiological diseases was up to 100%. [Nature and Science. 2005; 3(2):52-58].

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The wireless sensor network is an evergreen field of research. Everywhere we are using the sensor. Since the sensors are small in size and have less amount of initial energy, the energy saving becomes highly important and challenging. Wherever we deploy these sensors, it may or may not be accessible all the time. Hence, these should be implemented with a suitable algorithm to utilize energy efficiently. We have proposed an energy-saving algorithm by reducing the overheads of the cluster head (CH). In order to assist the CH, an assistant is selected called the supporting CH (SCH). Generally, this responsibility is rotational. Most of the nodes get a chance to serve CH so that the energy utilization is uniform. Through the proposed algorithm, the lifetime of the network in creased. This proposed algorithm is simulated using NS3 simulator and proves the energy-efficient clustering and increased lifetime as compared to other algorithms without the use of SCH.

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