Agriculture plays a crucial role in India’s economy, supporting the livelihoods of 58 percent of the population and contributing 17-18 percent of the GDP. However, plant pests anddiseases pose significant challenges, leading to biotic stress that hampers yield potential and diminishes the quality and quantity of food. Safeguarding crops against diseases is imperative to meet the increasing food demand. Globally, the losses caused by pathogens, pests, and weeds account for 20-40 percent of agricultural productivity. The detection of diseases in cultivated plants is a vital and complex task in agricultural practices. Conventional methods of disease detection and classification are time-consuming and labor-intensive, making it difficult to find optimized solutions. This issue is particularly problematic as farmers and professionals in developing countries require efficient methods to monitor and identify diseases affecting their crops. The implementation of program-based identification for plant diseases offers advantages such as improved detection, reduced human effort, and time savings. In this article, a smart and efficient technique is proposed to detect and classify plant diseases with higher accuracy than existing methods. The pro- posed technique employs Convolutional Neural Networks (CNNs)and focuses on leaf diseases as the main area of interest.
N-Queen is a well-known problem which states that for a given N x N chessboard, place N queens in such a way that no two queens can attack each other. Thus, two Queens should not lie in the same row, column or diagonal to each other. There are various approaches to solve this problem like Brute Force, Backtracking, Branch and Bound, Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, Dynamic Programming Solution, etc. [1]. In this paper, a comparative study and analysis of computation time required to solve N-Queen problem by Brute Force Search and Backtracking approach is done. The corresponding graph of computational time required by aforementioned two algorithms is plotted to analyze their performance. Further, a constraint is added to N-Queen problem where the position of the first queen in the first row is kept fixed. Backtracking approach is applied to the problem after addition of the constraint and its results are compared with Backtracking algorithm without any explicitly defined constraint. The graphical analysis gives insight into their performance. Thus, this paper would also provide the impact of an explicit constraint on Backtracking algorithm. General TermsBrute Force Algorithm, Backtracking Algorithm.
As computers become additional pervasive in society, facilitating natural human–computer interaction (HCI) can have a positive impact on their use. Hence, there has been growing interest within the development of recent approaches and technologies for bridging the human-computer barrier. the last word aim is to bring HCI to a regime wherever interactions with computers are going to be as natural as associate degree interaction between humans, and to the current finish, incorporating gestures in HCI is a crucial analysis space. Gestures have long been thought-about as associate degree interaction technique that may doubtless deliver additional natural, creative, and intuitive strategies for human activity with our computers. Hand gesture recognition is one amongll|one amongst|one in every of} the systems that may notice the gesture of the hand in a period of time video. The gesture of hand is classed inside a definite space of interest. during this study, planning hand gesture recognition is one among the difficult jobs that involves 2 major issues. foremost is that the detection of the hand. Another drawback is to form an indication that's appropriate to be used one hand at a time. This project concentrates on however a system might notice, acknowledge and interpret hand gesture recognition through computer vision with the difficult factors that variability within the create, orientation, location, and scale. To perform well for developing this project, differing kinds of gestures like numbers and sign languages got to be created during this system. The image taken from the period of time video is analyzed via Haar-cascade Classifier to notice the gesture of hand before the image process is finished or in different words to notice the looks of hand in a very frame. during this project, the detection of hand are going to be done mistreatment the theories of Region of Interest (ROI) via Python programming. the reason of the results are going to be targeted on the simulation half since the distinction for the hardware implementation is that the ASCII text file to scan the period of time input video. the event of hand gesture recognition mistreatment Python, OpenCV, and YOLO V3 will be enforced by applying the theories of hand segmentation and also the hand detection system that uses the Haar-cascade classifier.
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