scholarly journals In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3908
Author(s):  
Luca Ghiani ◽  
Alberto Sassu ◽  
Francesca Palumbo ◽  
Luca Mercenaro ◽  
Filippo Gambella
Keyword(s):  
Plant Disease ◽  
Current Practice ◽  
Yield Estimation ◽  
Average Precision ◽  
Specific Location ◽  
Viable Solution ◽  
Manual Counting ◽  
Training Samples ◽  
Cultivation Practices ◽  
Expensive Process

An early estimation of the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on manual counting of fruits or flowers by workers is a time consuming and expensive process and it is not feasible for large fields. Automatic yield estimation based on robotic agriculture provides a viable solution in this regard. In a typical image classification process, the task is not only to specify the presence or absence of a given object on a specific location, while counting how many objects are present in the scene. The success of these tasks largely depends on the availability of a large amount of training samples. This paper presents a detector of bunches of one fruit, grape, based on a deep convolutional neural network trained to detect vine bunches directly on the field. Experimental results show a 91% mean Average Precision.

scholarly journals Automatic Fruit Detection and Couting System Using Neural Network

2021 ◽  
pp. 164-169
Author(s):  
Vaishnavi R Padiyar ◽  
Nagaraja Hebbar N ◽  
Shreya G Shetty
Keyword(s):  
Neural Network ◽  
Web Application ◽  
Current Practice ◽  
Detection Methods ◽  
Yield Estimation ◽  
Counting System ◽  
The Neural Network ◽  
Manual Counting ◽  
Network Object ◽  
Expensive Process

In the field of agriculture, Identification and counting the number of fruits from the image helps the farmers in crop estimation. At present manual counting of fruits present in many places. The current practice of yield estimation based on the manual counting of fruits has many drawbacks as it is time consuming and expensive process. while considering the progress of fruit detection, estimating proper and accurate fruit counts from images in real-world scenarios such as orchards is still a challenging problem. The focus of this paper is on the web application of fruit yield estimation. This web application helps the farmers to count the number of fruits easily. This system provides an automated and efficient fruit counting system using computer vision techniques. This paper provides the progress towards in-field fruit counting using neural network object detection methods. So this process is done by recognizing each fruit in the image and taking the count. In the neural network, we have used YOLO architecture for recognizing the fruits.

scholarly journals Image-Based Plant Disease Identification by Deep Learning Meta-Architectures

Plants ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1451
Author(s):  
Muhammad Hammad Saleem ◽  
Sapna Khanchi ◽  
Johan Potgieter ◽  
Khalid Mahmood Arif
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
State Of The Art ◽  
Mean Average Precision ◽  
Single Shot ◽  
Average Precision ◽  
Crop Monitoring ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Agricultural Applications

The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment.

scholarly journals Rice grain disease identification using dual phase convolutional neural network based system aimed at small dataset.

2021 ◽  
Author(s):  
Tashin Ahmed ◽  
Chowdhury Rafeed Rahman ◽  
Md. Faysal Mahmud Abid
Keyword(s):  
Plant Disease ◽  
Cross Validation ◽  
Disease Classification ◽  
Phase Method ◽  
Rice Grain ◽  
Dual Phase ◽  
Rice Grains ◽  
Training Samples ◽  
Fold Cross Validation ◽  
Small Dataset

Abstract Although Convolutional neural networks (CNNs) are widely used for plant disease detection, they require a large number of training samples while dealing with wide variety of heterogeneous background. In this paper, a CNN based dual phase method has been proposed which can work effectively on small rice grain disease dataset with heterogeneity. At the first phase, Faster RCNN method is applied for cropping out the significant portion (rice grain) from an image. This initial phase results in a secondary dataset of rice grains devoid of heterogeneous background. Disease classification is performed on such derived and simplified samples using CNN architecture. Comparison of the dual phase approach with straight forward application of CNN on the small grain dataset shows the effectiveness of the proposed method which provides a 5 fold cross validation accuracy of 88.92%.

scholarly journals A Novel Framework Based on Mask R-CNN and Histogram Thresholding for Scalable Segmentation of New and Old Rural Buildings

2021 ◽  
Vol 13 (6) ◽  
pp. 1070
Author(s):  
Ying Li ◽  
Weipan Xu ◽  
Haohui Chen ◽  
Junhao Jiang ◽  
Xun Li
Keyword(s):  
Rural Areas ◽  
Deep Neural Networks ◽  
Training Data ◽  
The Novel ◽  
Remote Sensing Images ◽  
Average Precision ◽  
Training Samples ◽  
Rural Buildings ◽  
Segmentation Task ◽  
Instance Segmentation

Mapping new and old buildings are of great significance for understanding socio-economic development in rural areas. In recent years, deep neural networks have achieved remarkable building segmentation results in high-resolution remote sensing images. However, the scarce training data and the varying geographical environments have posed challenges for scalable building segmentation. This study proposes a novel framework based on Mask R-CNN, named Histogram Thresholding Mask Region-Based Convolutional Neural Network (HTMask R-CNN), to extract new and old rural buildings even when the label is scarce. The framework adopts the result of single-object instance segmentation from the orthodox Mask R-CNN. Further, it classifies the rural buildings into new and old ones based on a dynamic grayscale threshold inferred from the result of a two-object instance segmentation task where training data is scarce. We found that the framework can extract more buildings and achieve a much higher mean Average Precision (mAP) than the orthodox Mask R-CNN model. We tested the novel framework’s performance with increasing training data and found that it converged even when the training samples were limited. This framework’s main contribution is to allow scalable segmentation by using significantly fewer training samples than traditional machine learning practices. That makes mapping China’s new and old rural buildings viable.

Class Hierarchical Structure-Based Text Classification

2011 ◽  
Vol 255-260 ◽  
pp. 2233-2237
Author(s):  
Xiao Yun Chen ◽  
Jin Hua Chen
Keyword(s):  
Hierarchical Structure ◽  
Text Classification ◽  
Correct Position ◽  
Class Hierarchy ◽  
Viable Solution ◽  
Training Samples ◽  
The Hierarchical Structure ◽  
The Cost ◽  
Number Of Classes ◽  
Sample Set

There is a problem that the difficulty in text classification will increase when the number of classes increases, to which hierarchical structure is a viable solution. Well, a document’s hierarchical structure is usually maintained only by hand, which require substantial manpower to find the correct position of a document in the class hierarchy or to reconstruct the hierarchy. Constructing the hierarchical structure automatically by clustering the training samples can effectively reduce the cost of manual maintenance, and at the same time, it can avoid the conflict between the prior knowledge and the statistical properties of the sample set caused by artificial maintenance of the hierarchy.

scholarly journals Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning

2019 ◽  
Vol 11 (19) ◽  
pp. 2209 ◽  
Author(s):  
Ethan L. Stewart ◽  
Tyr Wiesner-Hanks ◽  
Nicholas Kaczmar ◽  
Chad DeChant ◽  
Harvey Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Plant Disease ◽  
Ground Truth ◽  
Leaf Blight ◽  
Northern Leaf Blight ◽  
Average Precision ◽  
Global Food Security ◽  
Serious Disease ◽  
Uav Images

Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight (NLB) is a serious disease affecting maize and is responsible for significant yield losses. A Mask R-CNN model was trained to segment NLB disease lesions in unmanned aerial vehicle (UAV) images. The trained model was able to accurately detect and segment individual lesions in a hold-out test set. The mean intersect over union (IOU) between the ground truth and predicted lesions was 0.73, with an average precision of 0.96 at an IOU threshold of 0.50. Over a range of IOU thresholds (0.50 to 0.95), the average precision was 0.61. This work demonstrates the potential for combining UAV technology with a deep learning-based approach for instance segmentation to provide accurate, high-throughput quantitative measures of plant disease.

scholarly journals Tobacco Plant Detection in RGB Aerial Images

Agriculture ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 57
Author(s):  
Xingping Sun ◽  
Jiayuan Peng ◽  
Yong Shen ◽  
Hongwei Kang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tobacco Plant ◽  
Aerial Images ◽  
Yield Estimation ◽  
Average Precision ◽  
Tobacco Plants ◽  
Two Stages

Tobacco is an essential economic crop in China. The detection of tobacco plants in aerial images plays an important role in the management of tobacco plants and, in particular, in yield estimations. Traditional yield estimation is based on site inspections, which can be inefficient, time-consuming, and laborious. In this paper, we proposed an algorithm to detect tobacco plants in RGB aerial images automatically. The proposed algorithm is comprised of two stages: (1) A candidate selecting algorithm extracts possible tobacco plant regions from the input, (2) a trained CNN (Convolutional Neural Network) classifies a candidate as either a tobacco-plant region or a nontobacco-plant one. This proposed algorithm is trained and evaluated on different datasets. It demonstrates good performance on tobacco plant detection in aerial images and obtains a significant improvement on AP (Average Precision) compared to faster R-CNN (Regions with CNN features) and YOLOv3 (You Only Look Once v3).

Microbulldozing and Microchiseling

1969 ◽  
Vol 27 ◽  
pp. 134-135
Author(s):  
J.N. Ramsey ◽  
D.P. Cameron ◽  
F.W. Schneider
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Material Handling ◽  
Microprobe Analysis ◽  
Foreign Matter ◽  
Specific Location ◽  
Potential Problems ◽  
Knoop Indenter ◽  
Scanning Electron ◽  
Microhardness Tester

As computer components become smaller the analytical methods used to examine them and the material handling techniques must become more sensitive, and more sophisticated. We have used microbulldozing and microchiseling in conjunction with scanning electron microscopy, replica electron microscopy, and microprobe analysis for studying actual and potential problems with developmental and pilot line devices. Foreign matter, corrosion, etc, in specific locations are mechanically loosened from their substrates and removed by “extraction replication,” and examined in the appropriate instrument. The mechanical loosening is done in a controlled manner by using a microhardness tester—we use the attachment designed for our Reichert metallograph. The working tool is a pyramid shaped diamond (a Knoop indenter) which can be pushed into the specimen with a controlled pressure and in a specific location.

Use of TEM in Plant Disease Diagnosis: A Xylem-Limited Bacterium Associated with Diseased ‘Toronto’ Creeping Bentgrass

1981 ◽  
Vol 39 ◽  
pp. 414-415
Author(s):  
Karen K. Baker ◽  
David L. Roberts
Keyword(s):  
Osmium Tetroxide ◽  
Plant Disease ◽  
Leaf Tissue ◽  
Infectious Agent ◽  
Creeping Bentgrass ◽  
Disease Diagnosis ◽  
Unknown Etiology ◽  
Agrostis Palustris ◽  
Putting Greens ◽  
Plant Disease Diagnosis

Plant disease diagnosis is most often accomplished by examination of symptoms and observation or isolation of causal organisms. Occasionally, diseases of unknown etiology occur and are difficult or impossible to accurately diagnose by the usual means. In 1980, such a disease was observed on Agrostis palustris Huds. c.v. Toronto (creeping bentgrass) putting greens at the Butler National Golf Course in Oak Brook, IL.The wilting symptoms of the disease and the irregular nature of its spread through affected areas suggested that an infectious agent was involved. However, normal isolation procedures did not yield any organism known to infect turf grass. TEM was employed in order to aid in the possible diagnosis of the disease.Crown, root and leaf tissue of both infected and symptomless plants were fixed in cold 5% glutaraldehyde in 0.1 M phosphate buffer, post-fixed in buffered 1% osmium tetroxide, dehydrated in ethanol and embedded in a 1:1 mixture of Spurrs and epon-araldite epoxy resins.

Localization of DNA in chromatin using ESI and osmium ammine staining

1990 ◽  
Vol 48 (3) ◽  
pp. 116-117
Author(s):  
C.L. Woodcock ◽  
R.A. Horowitz ◽  
D. P. Bazett-Jones ◽  
A.L. Olins
Keyword(s):  
Spectroscopic Imaging ◽  
Energy Losses ◽  
Electron Spectroscopic Imaging ◽  
Feulgen Reaction ◽  
Specific Location ◽  
Eukaryotic Nucleus ◽  
Chromatin Fibers ◽  
Patiria Miniata ◽  
Model Structures

In the eukaryotic nucleus, DNA is packaged into nucleosomes, and the nucleosome chain folded into ‘30nm’ chromatin fibers. A number of different model structures, each with a specific location of nucleosomal and linker DNA have been proposed for the arrangment of nucleosomes within the fiber. We are exploring two strategies for testing the models by localizing DNA within chromatin: electron spectroscopic imaging (ESI) of phosphorus atoms, and osmium ammine (OSAM) staining, a method based on the DNA-specific Feulgen reaction.Sperm were obtained from Patiria miniata (starfish), fixed in 2% GA in 150mM NaCl, 15mM HEPES pH 8.0, and embedded In Lowiciyl K11M at -55C. For OSAM staining, sections 100nm to 150nm thick were treated as described, and stereo pairs recorded at 40,000x and 100KV using a Philips CM10 TEM. (The new osmium ammine-B stain is available from Polysciences Inc). Uranyl-lead (U-Pb) staining was as described. ESI was carried out on unstained, very thin (<30 nm) beveled sections at 80KV using a Zeiss EM902. Images were recorded at 20,000x and 30,000x with median energy losses of 110eV, 120eV and 160eV, and a window of 20eV.

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