Leaf Photo Extra Quality

So much for background. To make my gold leaf prints I need ,in addition to my ink-jet printer and all that, watercolor paper on which to print, some acrylic paint for the base which will receive the gold leaf, adhesive size, metal leaf, InkAid, and protective spray, some brushes.

Hi Craig, many thanks for your comment! I have not done any printing on gold leaf in ages and obviously have not kept up the links.

A cursory Google check revealed that Ink Aid is apparently still widely available, so you might want to check there to find a place that carries it.

Best wishes, christian

Leaf Photo

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I included a pretty bamboo-and-leaf photo on my previous post, eventhough it wasn't related to the post, just to have something pretty, but Itend to like to share stories/context instead of just photos, so this postfills in that gap for that photo.

Just outside the Ryoanji Temple is a path/sidewalk that leads to thenearby suburban sprawl, and along it was a nice little area lined bybamboo fences separating the pedestrians from a small area of leaf-coveredgrass. Here's a picture I happened to have snapped from the same location I took the bamboo/leaf photo:

This photo was a long time from concept to complete execution: about four months. The leaves needed to be preserved for a proper natural snowfall in the winter. This required a little creativity on my part, and reliving part of my childhood.

Even though Weston believed in presentation and not interpretation as being of artistic concern to him, it is his exquisite compositions of natural objects that frequently enthrall viewers because they evoke ambivalent interpretations by associations of forms. Technically, what enabled Weston to make close-up, razor sharp images was his use of an 8 x 10 inch view camera, his method of lush printing, and using various types of printing-out papers which could capture the copious amount of information that was in the negative. Later his negatives continued to be printed by two of his sons, Cole and Brett Weston, who became famous photographers in their own right.

Cabbage Leaf, along with Pepper No. 30, is included in the collections of such esteemed institutions as the Art Institute of Chicago, ICP at George Eastman House, the Nelson Atkins Museum of Art of Kansas City, and the San Francisco Museum of Modern. It remains one of the most important and exemplary images in not only the oeuvre of Edward Weston, but also in the lineage of fine art photography.

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

In addition, plant disease detection could be achieved by extracting shape features method. Patil and Bodhe applied this technique for disease detection in sugarcane leaves where they have used threshold segmentation to determine leaf area and triangle threshold for lesioning area, getting the average accuracy of 98.60% at the final experiments [18].

There are some approaches which apply the feed-forward back propagation of neural networks consisting of one input, one output, and one hidden layer for the needs of identifying the species of leaf, pest, or disease; this model was proposed by the authors in [21]. They developed a software model, to suggest remedial measures for pest or disease management in agricultural crops.

Another technique proposed by the authors in [22] incorporates the features extracted by Particle Swarm Optimization (PSO) [23] and forward neural network in direction of determining the injured leaf spot of cotton and improving the accuracy of the system with the final overall accuracy of 95%.

Likewise, there are methods that combine the feature extraction and Neural Network Ensemble (NNE) for plant disease recognition. Through training a definite number of neural networks and combining their results after that, NNE offers a better generalization of learning ability [25]. Such method was implemented only for recognizing tea leaf diseases with final testing accuracy of 91% [26].

Another approach based on leaf images and using ANNs as a technique for an automatic detection and classification of plant diseases was used in conjunction with -means as a clustering procedure proposed by the authors in [27]. ANN consisted of 10 hidden layers. The number of outputs was 6 which was the number of classes representing five diseases along with the case of a healthy leaf. On average, the accuracy of classification using this approach was 94.67%.

In our study, we exploit the deep learning method for plant disease recognition, driven by evolvement of deep learning techniques and their application in practice. Extensive search of the state-of-the-art literature yielded no evidence that researchers explored deep learning approach for plant diseases recognition from the leaf images. Our method of recognition by applying deep CNN is presented in the sections below.

Many resources can be found by searching across the Internet, but their relevance is often unreliable. In the interest of confirming the accuracy of classes in the dataset, initially grouped by a keywords search, agricultural experts examined leaf images and labelled all the images with appropriate disease acronym. As it is known, it is important to use accurately classified images for the training and validation dataset. Only in that way may an appropriate and reliable detecting model be developed. In this stage, duplicated images that were left after the initial iteration of gathering and grouping images into classes described in Section 3.1 were removed from the dataset.

In this paper, a new approach of using deep learning method was explored in order to automatically classify and detect plant diseases from leaf images. The developed model was able to detect leaf presence and distinguish between healthy leaves and 13 different diseases, which can be visually diagnosed. The complete procedure was described, respectively, from collecting the images used for training and validation to image preprocessing and augmentation and finally the procedure of training the deep CNN and fine-tuning. Different tests were performed in order to check the performance of newly created model.

The main goal for the future work will be developing a complete system consisting of server side components containing a trained model and an application for smart mobile devices with features such as displaying recognized diseases in fruits, vegetables, and other plants, based on leaf images captured by the mobile phone camera. This application will serve as an aid to farmers (regardless of the level of experience), enabling fast and efficient recognition of plant diseases and facilitating the decision-making process when it comes to the use of chemical pesticides.

Furthermore, future work will involve spreading the usage of the model by training it for plant disease recognition on wider land areas, combining aerial photos of orchards and vineyards captured by drones and convolution neural networks for object detection. By extending this research, the authors hope to achieve a valuable impact on sustainable development, affecting crop quality for future generations.

Check on the photo booth next to your museum to go inside. You have to pay 500 bells to go in.After that, there will be a countdown, during which you can tap on any emotions that Dr. Shrunk hastaught you. When the countdown reaches the end, the photo booth will take a photo and show you whatit looks like. If you're happy with it, say OK, and it will replace your ID Card picture. If you want to try again, choose Try Again, and the countdown will take place again. You cantry as many times as you want.

If you want to show an emotion in the photo, just tap an emotion. You can learn emotions from Dr. Shrunk in Club LOL.You can tap the emotion at any time during the countdown, so be sure to time it carefully to get thepicture to show the emotion at the right moment. You can try again as many times as you want.

With the development of plant phenomics, the identification of plant diseases from leaf images has become an effective and economic approach in plant disease science. Among the methods of plant diseases identification, the convolutional neural network (CNN) is the most popular one for its superior performance. However, CNN's representation power is still a challenge in dealing with small datasets, which greatly affects its popularization. In this work, we propose a new method, namely PiTLiD, based on pretrained Inception-V3 convolutional neural network and transfer learning to identify plant leaf diseases from phenotype data of plant leaf with small sample size. To evaluate the robustness of the proposed method, the experiments on several datasets with small-scale samples were implemented. The results show that PiTLiD performs better than compared methods. This study provides a plant disease identification tool based on a deep learning algorithm for plant phenomics. All the source data and code are accessible at -wbgcas/PiTLiD. 17dc91bb1f