scholarly journals Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2504
Author(s):  
Marlies Lauwers ◽  
Benny De Cauwer ◽  
David Nuyttens ◽  
Simon R. Cool ◽  
Jan G. Pieters
Keyword(s):  
Crop Production ◽  
Image Data ◽  
Classification Model ◽  
Cyperus Esculentus ◽  
Yellow Nutsedge ◽  
Spectral Bands ◽  
Hyperspectral Classification ◽  
Multispectral Sensing ◽  
Better Than

Cyperus esculentus (yellow nutsedge) is one of the world’s worst weeds as it can cause great damage to crops and crop production. To eradicate C. esculentus, early detection is key—a challenging task as it is often confused with other Cyperaceae and displays wide genetic variability. In this study, the objective was to classify C. esculentus clones and morphologically similar weeds. Hyperspectral reflectance between 500 and 800 nm was tested as a measure to discriminate between (I) C. esculentus and morphologically similar Cyperaceae weeds, and between (II) different clonal populations of C. esculentus using three classification models: random forest (RF), regularized logistic regression (RLR) and partial least squares–discriminant analysis (PLS–DA). RLR performed better than RF and PLS–DA, and was able to adequately classify the samples. The possibility of creating an affordable multispectral sensing tool, for precise in-field recognition of C. esculentus plants based on fewer spectral bands, was tested. Results of this study were compared against simulated results from a commercially available multispectral camera with four spectral bands. The model created with customized bands performed almost equally well as the original PLS–DA or RLR model, and much better than the model describing multispectral image data from a commercially available camera. These results open up the opportunity to develop a dedicated robust tool for C. esculentus recognition based on four spectral bands and an appropriate classification model.

scholarly journals Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3995 ◽  
Author(s):  
Ning Liu ◽  
Ruomei Zhao ◽  
Lang Qiao ◽  
Yao Zhang ◽  
Minzan Li ◽  
...  
Keyword(s):  
Growth Stage ◽  
Spectral Response ◽  
Classification Model ◽  
Growth Stages ◽  
Potato Field ◽  
Spectral Bands ◽  
Svm Model ◽  
Selection Algorithms ◽  
Potato Crops

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

scholarly journals Investigating Deep Learning Approaches on the Security Analysis of Cryptographic Algorithms

Cryptography ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 30
Author(s):  
Bang Yuan Chong ◽  
Iftekhar Salam
Keyword(s):  
Deep Learning ◽  
Security Analysis ◽  
Data Encryption ◽  
Classification Model ◽  
Learning Approaches ◽  
Secret Key ◽  
Data Encryption Standard ◽  
Random Guess ◽  
Better Than

This paper studies the use of deep learning (DL) models under a known-plaintext scenario. The goal of the models is to predict the secret key of a cipher using DL techniques. We investigate the DL techniques against different ciphers, namely, Simplified Data Encryption Standard (S-DES), Speck, Simeck and Katan. For S-DES, we examine the classification of the full key set, and the results are better than a random guess. However, we found that it is difficult to apply the same classification model beyond 2-round Speck. We also demonstrate that DL models trained under a known-plaintext scenario can successfully recover the random key of S-DES. However, the same method has been less successful when applied to modern ciphers Speck, Simeck, and Katan. The ciphers Simeck and Katan are further investigated using the DL models but with a text-based key. This application found the linear approximations between the plaintext–ciphertext pairs and the text-based key.

Control of Yellow Nutsedge(Cyperus esculentus)and Other Weeds Before Summer Planting of Alfalfa(Medicago sativa)

Weed Science ◽  
1978 ◽  
Vol 26 (4) ◽  
pp. 399-402 ◽  
Author(s):  
D. L. Linscott ◽  
R. D. Hagin ◽  
T. Tharawanich
Keyword(s):  
Medicago Sativa ◽  
Late Summer ◽  
Cyperus Esculentus ◽  
Annual Grass ◽  
Yellow Nutsedge ◽  
Combination Treatments ◽  
Herbicide Treatments ◽  
Annual Weeds ◽  
Better Than

After land was plowed in the spring and prepared for planting, yellow nutsedge(Cyperus esculentusL.) was allowed to develop to heights of 10 to 12 and 20 to 25 cm. Either glyphosate [N-(phosphonomethyl)glycine] or paraquat (1,1′-dimethyl-4,4′-bipyridinium ion) was applied to emerged yellow nutsedge and other annual weeds at rates of 0.5, 1, 2, and 4 kg/ha. Half of the plots were double-disked 3 days after herbicide applications, and then all plots were planted with alfalfa(Medicago sativaL. ‘Cayuga’). Disking alone and application of glyphosate or paraquat alone did not satisfactorily control all weeds. However, the combination of a herbicide and disking controlled weeds enough to allow excellent establishment of alfalfa. In the year after establishment, the first cutting yields from the better combination treatments ranged from 3000 to 4400 kg/ha, which is normal for mid- to late-summer plantings in the region. Glyphosate was better than paraquat for control of grass weeds. Paraquat approached glyphosate in effectiveness when a supplemental disking treatment was added. Disking was as effective as the herbicide treatments for control of yellow nutsedge but not for control of broadleaf or annual grass weeds.

scholarly journals Multiclassification of license plate based on deep convolution neural networks

2021 ◽  
Vol 11 (6) ◽  
pp. 5266
Author(s):  
Masar Abed Uthaib ◽  
Muayad Sadik Croock
Keyword(s):  
Data Augmentation ◽  
Image Data ◽  
Cross Entropy ◽  
Classification Model ◽  
License Plate ◽  
Layer Model ◽  
Augmentation Techniques ◽  
Four Blocks ◽  
Total Accuracy

In the classification of license plate there are some challenges such that the different sizes of plate numbers, the plates' background, and the number of the dataset of the plates. In this paper, a multiclass classification model established using deep convolutional neural network (CNN) to classify the license plate for three countries (Armenia, Belarus, Hungary) with the dataset of 600 images as 200 images for each class (160 for training and 40 for validation sets). Because of the small numbers of datasets, a preprocessing on the dataset is performed using pixel normalization and image data augmentation techniques (rotation, horizontal flip, zoom range) to increase the number of datasets. After that, we feed the augmented images into the convolution layer model, which consists of four blocks of convolution layer. For calculating and optimizing the efficiency of the classification model, a categorical cross-entropy and Adam optimizer used with a learning rate was 0.0001. The model's performance showed 99.17% and 97.50% of the training and validation sets accuracies sequentially, with total accuracy of classification is 96.66%. The time of training is lasting for 12 minutes. An anaconda python 3.7 and Keras Tensor flow backend are used.

Yellow Nutsedge (Cyperus esculentus) Control POST With Acetolactate Synthase-Inhibiting Herbicides

1996 ◽  
Vol 10 (3) ◽  
pp. 576-580 ◽  
Author(s):  
John A. Ackley ◽  
Henry P. Wilson ◽  
Thomas E. Hines
Keyword(s):  
Sandy Loam ◽  
Acetolactate Synthase ◽  
Cyperus Esculentus ◽  
Yellow Nutsedge ◽  
Control Post ◽  
Better Than

Efficacy of several acetolactate synthase (ALS)-inhibiting herbicides was evaluated for yellow nutsedge control over a 3-yr period on a State sandy loam and in the greenhouse. Herbicides were applied POST to yellow nutsedge at actual or anticipated commercial rates. Yellow nutsedge control was above 77% from MON 12000 and chlorimuron. Control ranged from 41 to 89% from primisulfuron, pyrithiobac, and rimsulfuron. Control from nicosulfuron and imazethapyr was 58% or less while thifensulfuron and CGA-152005 had almost no activity on yellow nutsedge. In greenhouse studies, MON 12000 controlled yellow nutsedge better than did chlorimuron, and imazethapyr gave better control than in the field.

scholarly journals Facial Age Estimation Method Based on Fusion Classification and Regression Model

2018 ◽  
Vol 232 ◽  
pp. 02021
Author(s):  
Fengbing Jiang ◽  
Yu Zhang ◽  
GuoLiang Yang
Keyword(s):  
Regression Model ◽  
Age Estimation ◽  
Age Distribution ◽  
Estimation Method ◽  
Classification Model ◽  
Large Individual ◽  
Face Images ◽  
Classification And Regression ◽  
Better Than

Due to the large individual differences in the facial features of each person and the fact that the age has a certain time sequence, the age estimation based on face images faces certain difficulties. This article proposes a method based on fusion classification and regression model: A classification model and a regression model are added to the convolutional neural network to train the network under the premise of sharing convolutional layer parameters. The classification of the age of the label is used to code the age distribution, and the age is regressed using the Euclidean distance. The final predicted value of the model is the average of the two. Experiments show that the effect of fusion classification and regression model is better than that of a single model, which improves the accuracy of age estimation.

Preemergence and Postemergence Yellow Nutsedge (Cyperus esculentus) Control with MON 12000 in Nursery Crops

1996 ◽  
Vol 10 (1) ◽  
pp. 95-99 ◽  
Author(s):  
Jeffrey F. Derr ◽  
Rakesh S. Chandran ◽  
William D. Ward
Keyword(s):  
Cyperus Esculentus ◽  
Fresh Weight ◽  
Shoot Fresh Weight ◽  
Yellow Nutsedge ◽  
Nursery Crops ◽  
Better Than

Yellow nutsedge is a common and troublesome weed in the nursery industry. A selective postemergence herbicide is not available for yellow nutsedge control in most nursery crops. The effectiveness of MON 12000 for PRE and POST control of yellow nutsedge was evaluated in selected field-grown nursery crops. Preemergence control of yellow nutsedge 4 weeks after treatment (WAT) increased from 68% to 95% as MON 12000 rate increased from 0.03 to 0.28 kg ai/ha. At 9 WAT, control ranged from 16 to 73%. MON 12000 at 0.14 kg/ha provided similar PRE control of yellow nutsedge as metolachlor at 2.2 kg/ha. Four WAT, MON 12000 applied POST at 0.03 kg/ha controlled 73% and controlled 86% with the 0.28 kg/ha rate. MON 12000 at 0.14 and 0.28 kg/ha applied POST controlled yellow nutsedge better than bentazon at 1.12, chlorimuron at 0.01, imazaquin at 0.14, or glyphosate at 3.33 kg/ha. MON 12000 injured the foliage of azalea, crape myrtle, cotoneaster, and Japanese holly. Injury was most severe to cotoneaster. MON 12000 reduced azalea, cotoneaster, and crape myrtle shoot fresh weight compared to hand-weeded plots in at least one study. Metolachlor at 2.2 and 4.5 kg/ha caused little injury to the nursery species tested.

Evaluation of Imazosulfuron for Yellow Nutsedge (Cyperus esculentus) and Broadleaf Weed Control in Potato

2010 ◽  
Vol 24 (4) ◽  
pp. 471-477 ◽  
Author(s):  
Joel Felix ◽  
Rick A. Boydston
Keyword(s):  
Weed Control ◽  
Field Studies ◽  
Cyperus Esculentus ◽  
Common Lambsquarters ◽  
Yellow Nutsedge ◽  
Hairy Nightshade ◽  
Soil Persistence ◽  
Potato Foliage ◽  
Sequential Treatments ◽  
Better Than

Field studies were conducted in 2007 and 2008 near Nyssa, OR, and Pasco and Paterson, WA to evaluate yellow nutsedge and broadleaf weed control and potato tolerance to imazosulfuron. No injury symptoms from imazosulfuron were evident on potato at Nyssa, whereas in Washington, imazosulfuron caused some chlorosis of potato foliage ranging from 6 to 15% and < 4% at 6 and 15 d after POST application, respectively. Sequential applications of imazosulfuron controlled yellow nutsedge better than a single PRE application. Sequential applications of imazosulfuron or imazosulfuron in combination withs-metolachlor controlled yellow nutsedge > 92 and 89% at 21 and 42 d after POST applications, respectively. Imazosulfuron controlled ≥ 98% of common lambsquarters and 100% of pigweed species. Imazosulfuron provided season-long control of common mallow at Nyssa. However, imazosulfuron failed to control Russian thistle at Paterson, and only partially controlled hairy nightshade. Yield of U.S. no. 1 potato at Nyssa ranged from 44 to 54 T ha−1and 42 to 52 T ha−1for imazosulfuron PRE and imazosulfuron sequential treatments in 2007 and 2008, respectively. U.S. no. 1 potato yield following imazosulfuron PRE and sequential treatments at Pasco ranged from 49 to 57 T ha−1in 2007, and at Paterson from 36 to 54 T ha−1in 2008. Lower yields in 2008 were attributed to poor control of hairy nightshade. Imazosulfuron has potential to become a valuable tool for yellow nutsedge management in potato. Studies are needed to evaluate the soil persistence for imazosulfuron in order to determine safety to crops grown in rotation with potato.

Biology and Life Cycle of Purple and Yellow Nutsedges (Cyperus rotundusandC. esculentus)

1987 ◽  
Vol 1 (1) ◽  
pp. 66-73 ◽  
Author(s):  
E. W. Stoller ◽  
R. D. Sweet
Keyword(s):  
Crop Production ◽  
Crop Yields ◽  
Cyperus Esculentus ◽  
Purple Nutsedge ◽  
Yellow Nutsedge ◽  
Perennial Weeds ◽  
Local Environments ◽  
Extended Range ◽  
Production Areas ◽  
Herbaceous Perennial

Yellow and purple nutsedges (Cyperus esculentusL. # CYPES andC. rotundusL. # CYPRO) are herbaceous perennial weeds that are among the worst pests known. Holm et al. list purple nutsedge as the world's worst weed and yellow nutsedge as the sixteenth worst weed. Both weeds infest crop production areas in tropical and temperate climates, causing large losses in crop yields. While both species proliferate in the warm regions of the world, yellow nutsedge inhabits a wider range than purple nutsedge in the temperate areas, primarily because yellow nutsedge can tolerate colder temperatures. With such an extended range of habitation, many ecotypic variations of these species would be expected since they likely have adjusted to a multitude of local environments.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

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