“Melanoma is a serious form of skin cancer that begins in cells known as melanocytes and more dangerous due to its spreading ability to other organs more rapidly if it is not treated at an early stage”. This paper aims to propose a Melanoma detection methodology that includes four major phases: “(i) pre-processing (ii) segmentation (iii) the proposed feature extraction and (iv) classification”. Initially, pre-processing is performed, where the input image is subjected to processing like resizing and edge smoothening. Subsequently, segmentation is carried out by the Otsu thresholding process. In the feature extraction phase, the proposed Higher-Order Standardized Moment Induced-Local Binary Patterns (HOSMI-LBP)-based features are extracted. These features are then subjected to a classification process for classifying the disease. For this, it is planned to use a hybrid classification framework, where the Convolutional Neural Network (CNN) and the Neural Network (NN) are deployed. Two-phase of classification gets processed: the extracted features are subjected to NN; the input image is directly classified using an optimized CNN framework. Finally, the classified outputs from NN and optimized CNN are averaged and the final output is considered as detected output. Particularly, the weight and initial rate of CNN is optimized using the proposed algorithm known as the Sea Lion Integrated Grey Wolf Algorithm (SLI-GWO) method that hybrid the concepts of both Sea Lion Optimization (SLnO) and Grey Wolf Optimization (GWO) algorithm. At last, the proposed work performance is computed with traditional systems in terms of various measures.

Precision farming makes extensive use of information technology, which also aids agronomists in their work. Weeds typically grow alongside crops, lowering the production of those crops. Weeds are eliminated with the aid of herbicides. Without knowing what kind of weed it is, the pesticide may also harm the crop. The weeds from the farms must be categorized and identified in order to be controlled. Automatic control of weeds is essential to enlarge crop production and also to avoid rigorous hand weeding as labor scarcity has led to a surge in food manufacturing costs, especially in the developed countries such as India. On the other hand, the advancement of an intelligent, reliable automatic system for weed control in real time is still challenging. This paper intends to introduce a new crop/weed classification model that includes three main phases like pre-processing, feature extraction and classification. In the first phase, the input image is subjected to pre-processing, which deploys a contrast enhancement process. Subsequent to this, feature extraction takes place, where “the features based on gray-level co-occurrence matrix (GLCM) as well as gray-level run-length matrix (GLRM)” are extracted. Then, these extracted features along with the RGB image (totally five channels) are subjected to classification, where “optimized convolutional neural network” (CNN) is employed. In order to make the classification more accurate, the weight and the activation function of CNN are optimally chosen by a new hybrid model termed as the hybridized whale and sea lion algorithm (HW–SLA) model. Finally, the superiority of the adopted scheme is validated over other conventional models in terms of various measures.

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Crop and weed identification remains a challenge for unmanned weed control. Due to the small range between the chopping tine and the important crop location, weed identification against the annual crops must be extremely exact. This study endeavor included a literature evaluation, which included the most important 50 research publications in IEEE, Science Direct, and Springer journals. From 2012 until 2022, all of these papers are gathered. In fact, the diagnosis steps include: preprocessing, feature extraction, and crop/weed classification. This research analyzes the 50 research articles in several aspects, such as the dataset used for evaluations, different strategies used for pre-processing, feature extraction, and classification to get a clear picture of them. Furthermore, each work’s high performance in accuracy, sensitivity, and precision is demonstrated. Furthermore, the present hurdles in crop and weed identification are described, which serve as a benchmark for upcoming researchers.

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