scholarly journals Efficacy of Deep Learning Algorithm in Classifying Chilli Plant Growth Stages

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Danial Mirza Muammar Rozilan ◽  
Marsyita Hanafi ◽  
Roslizah Ali ◽  
Mohd Adib Razak ◽  
Cui Hairu
Keyword(s):  
Deep Learning ◽  
Plant Growth ◽  
Growth Stage ◽  
Learning Algorithm ◽  
Automatic Identification ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Learning Approaches ◽  
Learning Methods ◽  
Plant Growth Stages

Automatic plant growth monitoring has received considerable attention in recent years. The demand in this field has created various opportunities, especially for automatic classification using deep learning methods. In this paper, the efficiency of deep learning algorithms in classifying the growth stage of chili plants is studied. Chili is one of the high cash value crops, and automatic identification of chili plant growth stages is essential for crop productivity. Nevertheless, the study on automatic chili plant growth stage classification using deep learning approaches is not widely explored, and this is due to the unavailability of public datasets on the chili plant growth stages. Various deep learning methods, namely Inception V3, ResNet50, and VGG16, were used in the study, and the results have shown that these methods performed well in terms of accuracy and stability when tested on a dataset that consists of 2,320 images of Capsicum annum 'Bird's Eye' plants of various growth stages and imaging conditions. Nevertheless, the results have also shown that the deep learning methods have difficulty classifying images with a complex background where more than one chili plant was captured in an image.

scholarly journals Hormesis with glyphosate depends on coffee growth stage

2013 ◽  
Vol 85 (2) ◽  
pp. 813-822 ◽  
Author(s):  
LEONARDO B. DE CARVALHO ◽  
PEDRO L.C.A. ALVES ◽  
STEPHEN O. DUKE
Keyword(s):  
Plant Growth ◽  
Weed Management ◽  
Growth Stage ◽  
Growth Stages ◽  
Coffee Plant ◽  
Herbicide Application ◽  
Coffee Plantations ◽  
Plant Growth Stages ◽  
Coffee Plants ◽  
Almost All

Weed management systems in almost all Brazilian coffee plantations allow herbicide spray to drift on crop plants. In order to evaluate if there is any effect of the most commonly used herbicide in coffee production, glyphosate, on coffee plants, a range of glyphosate doses were applied directly on coffee plants at two distinct plant growth stages. Although growth of both young and old plants was reduced at higher glyphosate doses, low doses caused no effects on growth characteristics of young plants and stimulated growth of older plants. Therefore, hormesis with glyphosate is dependent on coffee plant growth stage at the time of herbicide application.

scholarly journals Dosage of 2,6-Bis (1.1-Dimethylethyl)-4-Methylphenol (BHT) in the Plant ExtractMesembryanthemum crystallinum

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
Bouftira Ibtissem ◽  
Mgaidi Imen ◽  
Sfar Souad
Keyword(s):  
Antioxidant Activity ◽  
Plant Growth ◽  
Plant Extract ◽  
Growth Stage ◽  
Mesembryanthemum Crystallinum ◽  
Growth Stages ◽  
Naturally Occurring ◽  
Plant Growth Stages

A naturally occurring BHT was identified in the leaves of the halophyte plantMesembryanthemum crystallinum. This phenol was extracted in this study by two methods at the different plant growth stages. One of the methods was better for BHT extraction; the concentration of this phenol is plant growth stage dependent. In this study, the floraison stage has the highest BHT concentration. The antioxidant activity of the plant extract was not related to BHT concentration. The higher antioxidant activity is obtained at seedlings stage.

Dinitrogen fixation in Phaseolus vulgaris at low temperatures: interaction of temperature, growth stage, and time of inoculation

1982 ◽  
Vol 60 (8) ◽  
pp. 1423-1427 ◽  
Author(s):  
R. J. Rennie ◽  
G. A. Kemp
Keyword(s):  
Phaseolus Vulgaris ◽  
Plant Growth ◽  
Root Growth ◽  
Low Temperatures ◽  
Growth Stage ◽  
Growth Stages ◽  
Trifoliate Leaf ◽  
Temperature Growth ◽  
Southern Alberta ◽  
Plant Growth Stages

Nodulation and N2 fixation have not been reported in beans (Phaseolus vulgaris L.) below a temperature of 13 °C but, in southern Alberta, temperatures at planting may be as low as 10 °C. Two varieties of pea beans, 'Aurora' and 'Kentwood,' were inoculated at three growth stages (seeding, primary leaf horizontal, or first trifoliate leaf open) and grown at 10, 12, 14, or 16 °C. Nodulation and acetylene (C2H2) reduction occurred in both varieties at temperatures as low as 10 °C. At the lower temperatures, cold adaptability of the plant for early root growth determined the ability for nodulation and N2 fixation. At higher temperatures, plant-growth stage was a determining factor. 'Aurora' was superior to 'Kentwood' at 10 °C in nodulation, dry matter (DM), N yield, and N2 fixation because of its tolerance to low temperatures during early root growth. Inoculation with Rhizobium phaseoli at more advanced plant-growth stages decreased the time for nodulation at all four temperatures but resulted in higher yield and more N2 fixation in 'Aurora' only at 14 and 16 °C. At 10 °C, inoculation at seeding was more effective than at the other two growth stages for both varieties. Thus plant growth stages and growth temperature both determined the ability of a bean variety to support N2 fixation at various low temperatures.

Efficacy of insecticidal applications based on calendar intervals and plant growth stages for control of insect pests on cowpea

1995 ◽  
Vol 16 (1) ◽  
pp. 109-112 ◽  
Author(s):  
Aliyageen M. Alghali
Keyword(s):  
Plant Growth ◽  
Growth Stage ◽  
Insect Pests ◽  
Growth Stages ◽  
Grain Yields ◽  
Plant Growth Stages ◽  
Cowpea Varieties

AbstractStudies were carried out to compare the efficacy of insecticide schedules based on calendar intervals with those based on plant growth stages for the control of cowpea insect pests. Spray schedules based on calendar intervals and plant growth stage gave reasonable control of the pest complex and increased grain yields significantly. However, spray schedules based on plant growth stages alone were more efficient for two of the three cowpea varieties tested. This was attributed to differences in maturity periods which made spraying by regular calendar intervals slightly out of phase with attack by key insect pests. Thus, spray schedules based on plant growth stages are recommended for the control of insect pests of cowpea.

scholarly journals IBA Formulation, Concentration, and Stock Plant Growth Stage Affect Rooting of Stem Cuttings of Viburnum rufidulum

2008 ◽  
Vol 26 (1) ◽  
pp. 1-3
Author(s):  
Jason J. Griffin
Keyword(s):  
Plant Growth ◽  
Growth Stage ◽  
Adventitious Rooting ◽  
Growth Stages ◽  
Stem Cuttings ◽  
Stock Plant ◽  
Indolebutyric Acid ◽  
Rooted Cutting ◽  
Landscape Plant ◽  
Plant Growth Stages

Abstract Viburnum rufidulum Raf. (southern or rusty blackhaw) has potential to be a popular landscape plant as it is an attractive large shrub tolerant of many common landscape stresses. However, propagation difficulties have thus far limited wide scale use. Therefore, the influence of IBA formulation and concentration on adventitious rooting of stem cuttings of southern blackhaw taken at different stock plant growth stages throughout the year were investigated. Liquid formulations of the potassium salt (K-salt) of indolebutyric acid (K-IBA) at 0, 3000, 6000, or 9000 ppm (0, 0.3, 0.6, or 0.9%) as well as talc formulations of IBA at 1000, 3000, or 8000 ppm (0.1, 0.3 or 0.8%) were utilized. Talc formulations failed to stimulate rooting regardless of concentration or growth stage. A quick-dip of K-IBA increased rooting percentage at all growth stages. Softwood and hardwood cuttings had the highest rooting percentages. Hardwood cuttings treated with 6000 ppm (0.6%) or 9000 ppm (0.9%) rooted 90 and 100%, respectively. Softwood cuttings treated with 6000 ppm (0.6%) rooted 87%. K-IBA improved root number per rooted cutting for softwood cuttings, whereas root length was unaffected by K-IBA at any growth stage.

scholarly journals Stand Reduction and Foliage Damage Reduce Yield of Dehydrating Onion

HortScience ◽  
2004 ◽  
Vol 39 (5) ◽  
pp. 1005-1007 ◽  
Author(s):  
George H. Clough
Keyword(s):  
Plant Growth ◽  
Growth Stage ◽  
Leaf Growth ◽  
Field Trials ◽  
Growth Stages ◽  
Leaf Stage ◽  
Plant Growth Stages ◽  
The Impact ◽  
Stage Yield ◽  
Different Growth Stages

Field trials were conducted at Hermiston, Ore., from 1995 through 1998, to determine impact of stand loss and plant damage at different growth stages on yield of onions (Allium cepa) grown for dehydration. Stand reduction (0%, 20%, 40%, 60%, 80%) and foliage damage (0%, 25%, 50%, 75%, 100%) treatments were applied at three-, six-, nine-, and twelve-leaf onion growth stages. Although average bulb weight increased as stand was reduced, marketable, cull, and total yields decreased as stand reduction increased (plant population decreased) at all plant growth stages. Bulb weight was not changed by up to 100% foliage removal at the three-leaf stage. At the six- and twelve-leaf stages, weight was reduced when ≥50% of the foliage was removed. The most severe response occurred at the nine-leaf stage. At the three-leaf stage, yield was not affected by foliage damage. At the six-leaf growth stage, yield was reduced by 75% or more foliage loss, but at the nine- and twelve-leaf stages, ≥50% foliage removal reduced expected yields. As with bulb weight, the impact of foliage removal on yield was most severe at the nine-leaf growth stage.

scholarly journals A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2764
Author(s):  
Xin Yu Liew ◽  
Nazia Hameed ◽  
Jeremie Clos
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Main Process ◽  
Learning Approaches ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Computer Aided ◽  
Conventional Methods ◽  
The Impact

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

scholarly journals Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 667
Author(s):  
Wei Chen ◽  
Qiang Sun ◽  
Xiaomin Chen ◽  
Gangcai Xie ◽  
Huiqun Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Deep Learning ◽  
Heart Sounds ◽  
Learning Approaches ◽  
Automated Classification ◽  
Learning Methods ◽  
Artificial Intelligence Technology ◽  
Medical Big Data ◽  
Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

SMILE: Mutual Information Learning for Integration of Single-cell Omics Data

2021 ◽  
Author(s):  
Yang Xu ◽  
Priyojit Das ◽  
Rachel Patton McCord
Keyword(s):  
Deep Learning ◽  
Mutual Information ◽  
Single Cell ◽  
Learning Algorithm ◽  
Cellular Systems ◽  
Supplementary Information ◽  
Omics Data ◽  
Learning Approaches ◽  
Rna Seq ◽  
Integrate Data

Abstract Motivation Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Results Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome wide peaks for ATAC-seq. Integrated representations learned from joint profiling technologies can then be used as a framework for comparing independent single source data. Supplementary information Supplementary data are available at Bioinformatics online. The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE.

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