Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19

Deep learning is currently the most important branch of machine learning, with applications in speech recognition, computer vision, image classification, and medical imaging analysis. Plant recognition is one of the areas where image classification can be used to identify plant species through their leaves. Botanists devote a significant amount of time to recognizing plant species by personally inspecting. This paper describes a method for dissecting color images of Swedish leaves and identifying plant species. To achieve higher accuracy, the task is completed using transfer learning with the help of pre-trained classifier VGG-19. The four primary processes of classification are image preprocessing, image augmentation, feature extraction, and recognition, which are performed as part of the overall model evaluation. The VGG-19 classifier grasps the characteristics of leaves by employing pre-defined hidden layers such as convolutional layers, max pooling layers, and fully connected layers, and finally uses the soft-max layer to generate a feature representation for all plant classes. The model obtains knowledge connected to aspects of the Swedish leaf dataset, which contains fifteen tree classes, and aids in predicting the proper class of an unknown plant with an accuracy of 99.70% which is higher than previous research works reported.Comment: Under review process in 'IETE Journal of Research

Dandelion Weed Detection and Recognition for a Weed Removal Robot

Current research in agricultural weeding automation attempts to develop accurate methods of distinguishing between crop and weed. Consequently, the use of computer vision has become a cornerstone in these endeavours. Some recent methods employ pattern recognition techniques that involve hierarchical feature groupings. The application generally applies some form of machine learning. Furthermore, using convolutional neural networks (CNN), many techniques implement complex architectures that not only classify but also detect and locate objects. These detection problems generally involve datasets taken under artificial or controlled lighting conditions where foreground elements (i.e. weed and crop) are easily distinguishable from the background (usually soil) by virtue of their distinct hue and textures. Plant overlap is generally limited to being between foreground elements. The research in this thesis addresses the challenges overlooked by agricultural weeding by focusing on weeding in lawn grass with two distinct approaches. First, a pattern recognition methodology is developed to distinguish dandelion weed centers from grass using the morphological attributes of binary (black-and-white) regions. This method is tested in lab settings with both artificial weeds and grass. However, practical limitations include a fragile performance in real-world applications in the field and a heavy reliance on parameter calibration. Next, a machine-learning approach is developed to address the shortcomings of the prior approach as well as to deal with the challenges specific to weeding in a domestic setting. A five-step process involving CNN structures proves successful at accurately detecting dandelion weeds within grass and other lawn vegetation. Extensive tests have been carried out on a wide array of real work images and the results demonstrate that the developed algorithm can detect and recognize dandelions in the grass within a reasonable range of natural lighting conditions

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Visual and Camera Sensors

This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors

Tactile Perception And Visuotactile Integration For Robotic Exploration

As the close perceptual sibling of vision, the sense of touch has historically received less than deserved attention in both human psychology and robotics. In robotics, this may be attributed to at least two reasons. First, it suffers from the vicious cycle of immature sensor technology, which causes industry demand to be low, and then there is even less incentive to make existing sensors in research labs easy to manufacture and marketable. Second, the situation stems from a fear of making contact with the environment, avoided in every way so that visually perceived states do not change before a carefully estimated and ballistically executed physical interaction. Fortunately, the latter viewpoint is starting to change. Work in interactive perception and contact-rich manipulation are on the rise. Good reasons are steering the manipulation and locomotion communities’ attention towards deliberate physical interaction with the environment prior to, during, and after a task. We approach the problem of perception prior to manipulation, using the sense of touch, for the purpose of understanding the surroundings of an autonomous robot. The overwhelming majority of work in perception for manipulation is based on vision. While vision is a fast and global modality, it is insufficient as the sole modality, especially in environments where the ambient light or the objects therein do not lend themselves to vision, such as in darkness, smoky or dusty rooms in search and rescue, underwater, transparent and reflective objects, and retrieving items inside a bag. Even in normal lighting conditions, during a manipulation task, the target object and fingers are usually occluded from view by the gripper. Moreover, vision-based grasp planners, typically trained in simulation, often make errors that cannot be foreseen until contact. As a step towards addressing these problems, we present first a global shape-based feature descriptor for object recognition using non-prehensile tactile probing alone. Then, we investigate in making the tactile modality, local and slow by nature, more efficient for the task by predicting the most cost-effective moves using active exploration. To combine the local and physical advantages of touch and the fast and global advantages of vision, we propose and evaluate a learning-based method for visuotactile integration for grasping

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Video Understanding: A Predictive Analytics Perspective

This dissertation includes a detailed study of video predictive understanding, an emerging perspective on video-based computer vision research. This direction explores machine vision techniques to fill in missing spatiotemporal information in videos (e.g., predict the future), which is of great importance for understanding real world dynamics and benefits many applications. We investigate this direction with depth and breadth. Four emerging areas are considered and improved by our efforts: early action recognition, future activity prediction, trajectory prediction and procedure planning. For each, our research presents innovative solutions based on machine learning techniques (deep learning in particular) and meanwhile pays special attention to their interpretability, multi-modality and efficiency, which we consider as critical for next-generation Artificial Intelligence (AI). Finally, we conclude this dissertation by discussing current shortcomings as well as future directions