Basudev Sharma

Bio: Basudev Sharma is an academic researcher from Jagannath University. The author has contributed to research in topics: Sequential minimal optimization & Optimization problem. The author has an hindex of 3, co-authored 4 publications receiving 101 citations.

Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm

Abstract: A proper soil prediction is one of the most important parameters to decide the suitable crop which is generally performed manually by the farmers. Therefore, the efficiency of the farmers may be increased by producing an automated tools for soil prediction. This paper presents an automated system for categorization of the soil datasets into respective categories using images of the soils which can further be used for the decision of crops. For the same, a novel Bag-of-words and chaotic spider monkey optimization based method has been proposed which is used to classify the soil images into its respective categories. The novel chaotic spider monkey optimization algorithm shows desirable convergence and improved global search ability over standard benchmark functions. Hence, it has been used to cluster the keypoints in Bag-of-words method for soil prediction. The experimental outcomes illustrate that the anticipated methods effectively classify the soil in comparison to other meta-heuristic based methods.

Sigmoidal Spider Monkey Optimization Algorithm

Abstract: Spider monkey optimization (SMO) algorithm is a recently developed optimizer that is stimulated by the extraordinary social activities of spider monkeys known as fission–fusion social structure. The SMO is developed to find solution of difficult optimization problems in real world, which are difficult to solve by the available deterministic strategies. During the solution search process in SMO, perturbation rate plays very important role. The convergence rate of SMO is highly affected by it. Usually, perturbation rate is defined by a simple function that is linearly in nature. But some application has nonlinear nature, thus a nonlinear function may improve the outcomes of SMO. For that reason, a non linear function, namely sigmoidal function used to decide perturbation in SMO and proposed strategy named as sigmoidal SMO. The investigational outcomes show the superiority of the anticipated technique over other meta-heuristics.

Comparative Analysis of Selected Variant of Spider Monkey Optimization Algorithm

Abstract: Spider Monkey Optimization (SMO) algorithm is recently developed optimiser that is stimulated by the extraordinary social activities of spider monkeys known as fission–fusion social structure. The SMO is developed to find the solution of difficult optimization problems in real world, which are difficult to solve by the available deterministic strategies. Here, three modifications in SMO algorithm are selected for the purpose of comparison, namely, exponential SMO, chaotic SMO and sigmoidal SMO. These modifications suggested new strategies for selecting perturbation rate in local leader and local leader decision phase. These strategies replaced linear approach of perturbation by nonlinear functions. The proposed strategies are tested over a set of benchmark functions.

Deep Learning Convolutional Neural Network for Apple Leaves Disease Detection

Abstract: Apple trees are perhaps one of the most popular plants to grow in large plantations and in-home gardens. At the same time, Apple plants are among the plants that are the most prone to diseases. Disease identification at an early stage and its prevention before spreading into other parts of the plant is a challenge even for the expert’s eye. Therefore, an adequate system is required to detect plant disease in the initial stage. This paper displays the prowess of Convolutional Neural Networks to automatically detect and address the issue. Images of Apple leaves, covering various diseases as well as healthy samples, from the Plant Village dataset are used to validate results. Image filtering, image compression, and image generation techniques are used to gain a large train-set of images and tune the system perfectly. The trained model achieves high accuracy scores in all the classes with a net accuracy of 98.54% on the entire dataset, sampled and generated from 2561-labelled images.

Particle Swarm Optimization Based Support Vector Machine (P-SVM) for the Segmentation and Classification of Plants

Abstract: With the rapid growth in urbanization and population, it has become an earnest task to nurture and grow plants that are both important in sustaining the nature and the living beings needs. In addition, there is a need for preserving the plants having global importance both economically and environmentally. Locating such species from the forest or shrubs having human involvement is a time consuming and costly task to perform. Therefore, in this paper, a novel method is presented for the segmentation and classification of the seven different plants, named Guava, Jamun, Mango, Grapes, Apple, Tomato, and Arjun, based on their leaf images. In the first phase, both real-time images and images from the crowdAI database are collected and preprocessed for noise removal, resizing, and contrast enhancement. Then, in the second phase, different features are extracted based on color and texture. The third phase includes the segmentation of images using a k-means algorithm. The fourth phase consists of the training of support vector machine, and finally, in the last phase, the testing is performed. Particle swarm optimization algorithm is used for selecting the best possible value of the initialization parameter in both the segmentation and classification processes. The proposed work achieves higher experimental results, such as sensitivity = 0.9581, specificity = 0.9676, and accuracy = 0.9759, for segmentation and classification accuracy = 95.23 when compared with other methods.

Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection

Abstract: Agriculture is the main source of wealth, and its contribution is essential to humans. However, several obstacles faced by the farmers are due to different kinds of plant diseases. The determination and anticipation of plant diseases are the major concerns and should be considered for maximizing productivity. This paper proposes an effective image processing method for plant disease identification. In this research, the input image is subjected to the pre-processing phase for removing the noise and artifacts present in the image. After obtaining the pre-processed image, it is subjected to the segmentation phase for obtaining the segments using piecewise fuzzy C-means clustering (piFCM). Each segment undergoes a feature extraction phase in which the texture features are extracted, which involves information gain, histogram of oriented gradients (HOG), and entropy. The obtained texture features are subjected to the classification phase, which uses the deep belief network (DBN). Here, the proposed Rider-CSA is employed for training the DBN. The proposed Rider-CSA is designed by integrating the rider optimization algorithm (ROA) and Cuckoo Search (CS). The experimental results proved that the proposed Rider-CSA-DBN outperformed other existing methods with maximal accuracy of 0.877, sensitivity of 0.862, and the specificity of 0.877, respectively.

Recognition of plant leaf diseases based on computer vision

Abstract: Agriculture is one of the most important sources of income for people in many countries. However, plant disease issues influence many farmers, as diseases in plants often naturally occur. If proper care is not taken, diseases can have hazardous effects on plants and influence the product quality, quantity or productivity. Therefore, the detection and prevention of plant diseases are serious concerns and should be considered to increase productivity. An effective identification technology can be beneficial for monitoring plant diseases. Generally, the leaves of plants show the first signs of plant disease, and most diseases can be detected from the symptoms that appear on the leaves. Therefore, this paper introduces a novel method for the detection of plant leaf diseases. The method is divided into two parts: image segmentation and image classification. First, a hue, saturation and intensity-based and LAB-based hybrid segmentation algorithm is proposed and used for the disease symptom segmentation of plant disease images. Then, the segmented images are input into a convolutional neural network for image classification. The validation accuracy obtained using this approach was approximately 15.51% higher than that for the conventional method. Additionally, the detection results showed that the average detection rate was 75.59% under complex background conditions, and most of the diseases were effectively detected. Thus, the approach of combined segmentation and classification is effective for plant disease identification, and our empirical research validates the advantages of the proposed method.