Monocropped plantations are unique to India and a handful of countries throughout the globe. Essentially, the FOREST approach of growing coffee along with in India has enabled the plantation to fight many outbreaks of pests and diseases. Mono cropped Plantations are under constant threat of pest and disease incidence because it favours the build up of pest population. To cope with these problems, an automatic pest detection algorithm using image processing techniques in MATLAB has been proposed in this paper. Image acquisition devices are used to acquire images of plantations at regular intervals. These images are then subjected to pre-processing, transformation and clustering.
The processing and analyzing of multimedia data has become a popular research topic due to the evolution of deep learning. Deep learning has played an important role in addressing many challenging problems, such as computer vision, image recognition, and image detection, which can be useful in many real-world applications. In this study, we analyzed visual features of images to detect advertising images from scanned images of various magazines. The aim is to identify key features of advertising images and to apply them to real-world application. The proposed work will eventually help improve marketing strategies, which requires the classification of advertising images from magazines. We employed convolutional neural networks to classify scanned images as either advertisements or non-advertisements (i.e., articles). The results show that the proposed approach outperforms other classifiers and the related work in terms of accuracy.
Introducing Computational Fluid Dynamics (CFD) to engineering students at the undergraduate level has become more common in recent years, although there are significant barriers for doing so using a generalized CFD solver. A common constraint is the quantity of material to be covered in a fixed amount of time in a given course, which leaves little time left for learning the use of a generalized CFD package. With this consideration in mind, FlowLab (www.flowlab.fluent.com) was introduced by Fluent Inc. FlowLab may be described as a virtual fluids laboratory—a computer based analysis and visualization package. Using FlowLab, students solve predefined CFD exercises. These predefined exercises facilitate teaching and provide students with hands-on CFD experience. Through the design of each FlowLab exercise, students are introduced to engineering problems and concepts as well as CFD via a structured learning process. In the fall 2003 semester at Purdue University, FlowLab was used in CHE 540, a transport phenomena course offered within the School of Chemical Engineering. This course is open to advanced undergraduate engineering students and graduate students. Students were exposed to eight separate FlowLab exercises in this course. This paper gives a detailed summary of one of these specific exercises, developing flow in a pipe with and without heat transfer. The paper emphasizes how the use of CFD via FlowLab enhanced the teaching of specific concepts in transport phenomena as well as concepts in CFD such as creating a parametric geometry, discretizing the geometry, specifying boundary conditions, material properties and operating conditions, numerical solution techniques and post-processing. Experiences from this course are that FlowLab is a positive force for creating student interest and excitement in the area of fluid mechanics and transport phenomena. Using FlowLab’s post-processing capabilities, students were able to visualize complex flow fields and make direct comparison to analytical theory and experimental correlation. In addition, FlowLab provided a structured learning experience which reinforced proper pedagogy for applying CFD to engineering problems. Upon completion of the course, a student survey was performed in CHE 540 focusing on FlowLab integration and usage, and survey responses are summarized in this paper.
Bacterial leaf scorch(BLS) is a common infection seen in different shade tree species such as American elm, red maple, sweet gum, sycamore and London plane, and a number of species of oak . The disease is caused by the bacterial pathogen Xylella fastidiosa. Spread of the disease may lead to early fall and determinate growth of the trees. The symptoms of the disease may first appear on branches and leaves and it spreads to other parts of the plant on a later stage. So detection of the infected areas of the plant leads to early analysis and elimination of disease spread and saves the plant from disease spread. Proposed scheme uses a novel approach to detect the infected areas of the plant by segmenting the plant leaves by clustering. K-means clustering is performed for different cluster centres to obtain different cluster groups of the region of interest (ROI).
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