scholarly journals Better Effect of a Lower Dose of Plant Substances in Deterring Storage Pests

2021 ◽  
Author(s):  
Aleksandra Izdebska ◽  
Małgorzata Kłyś ◽  
Sylwia Koczanowicz ◽  
Magdalena Nowak-Chmura
Keyword(s):  
Storage Pests ◽  
Plant Substances

Search for new plant substances as candidates for development of skin antiseptics

2020 ◽  
pp. 13-16
Author(s):  
D. A. Suvorin ◽  
A. V. Alimov ◽  
Yu. A. Zakharova ◽  
E. V. Bolgarova
Keyword(s):  
Plant Substances

Plant Substances as Antiviral Agents: An Update (1997-2001)

2003 ◽  
Vol 7 (12) ◽  
pp. 1163-1180 ◽  
Author(s):  
P. Cos ◽  
D. Berghe ◽  
T. Bruyne ◽  
A. Vlietinck
Keyword(s):  
Antiviral Agents ◽  
Plant Substances

Plant substances. VII. The structure of aplotaxene from costus oil

1959 ◽  
Vol 24 (6) ◽  
pp. 2018-2022 ◽  
Author(s):  
M. Romaňuk ◽  
V. Herout ◽  
F. Šorm
Keyword(s):  
Plant Substances

Plant substances. XVI. The constituents of coltsfoot rhizomes (Petasites officinalis MOENCH.)

1962 ◽  
Vol 27 (6) ◽  
pp. 1393-1399 ◽  
Author(s):  
L. Novotný ◽  
J. Jizba ◽  
V. Herout ◽  
F. Šorm
Keyword(s):  
Plant Substances

Method of sampling and laboratory extraction affects interpretation of grain infestation by storage pests

2008 ◽  
Vol 115 (3) ◽  
pp. 129-133 ◽  
Author(s):  
V. Stejskal ◽  
R. Aulický ◽  
Z. Kučerová ◽  
J. Lukáš
Keyword(s):  
Storage Pests

scholarly journals Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 879
Author(s):  
Clíssia Barboza da Silva ◽  
Alysson Alexander Naves Silva ◽  
Geovanny Barroso ◽  
Pedro Takao Yamamoto ◽  
Valter Arthur ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Prediction Models ◽  
Sitophilus Zeamais ◽  
Human Consumption ◽  
Storage Pests ◽  
Maize Weevil ◽  
X Ray ◽  
Precise Control ◽  
X Ray Imaging

The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.

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