scholarly journals Growing Environment of Vallisneria asiatica Miki, a Freshwater Plant in Brackish Lake Togo-ike

2020 ◽  
Vol 22 (2) ◽  
pp. 117-124
Author(s):  
Akihiro MORI ◽  
Akihiro MAEDA ◽  
Yoshiyuki HIOKI
Keyword(s):  
Brackish Lake ◽  
Growing Environment ◽  
Vallisneria Asiatica

Effect of Humidity Level in a Warm Growing Environment on Porcine Carcass Composition and Quality

1967 ◽  
Vol 26 (4) ◽  
pp. 705-708 ◽  
Author(s):  
P. B. Addis ◽  
H. R. Johnson ◽  
C. J. Heidenreich ◽  
H. W. Jones ◽  
M. D. Judge
Keyword(s):  
Carcass Composition ◽  
Humidity Level ◽  
Growing Environment

FIELD EXPERIMENT OF ANEAROBIC BOTTOM LAYER IMPROVEMENT IN BRACKISH LAKE

2018 ◽  
Vol 74 (7) ◽  
pp. III_35-III_41
Author(s):  
Koki SUGIHARA ◽  
Shingo MASUKI ◽  
Shogo SUGAHARA ◽  
Ryuichi SHINME
Keyword(s):  
Field Experiment ◽  
Bottom Layer ◽  
Brackish Lake

scholarly journals Design and Implementation of a Hydroponic Strawberry Monitoring and Harvesting Timing Information Supporting System Based on Nano AI-Cloud and IoT-Edge

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1400
Author(s):  
Sun Park ◽  
JongWon Kim
Keyword(s):  
South Korea ◽  
Monitoring System ◽  
Learning Algorithm ◽  
Great Influence ◽  
Integrated System ◽  
Environmental Data ◽  
Decision System ◽  
Horticultural Crops ◽  
Deep Learning Algorithm ◽  
Growing Environment

The strawberry market in South Korea is actually the largest market among horticultural crops. Strawberry cultivation in South Korea changed from field cultivation to facility cultivation in order to increase production. However, the decrease in production manpower due to aging is increasing the demand for the automation of strawberry cultivation. Predicting the harvest of strawberries is an important research topic, as strawberry production requires the most manpower for harvest. In addition, the growing environment has a great influence on strawberry production as hydroponic cultivation of strawberries is increasing. In this paper, we design and implement an integrated system that monitors strawberry hydroponic environmental data and determines when to harvest with the concept of IoT-Edge-AI-Cloud. The proposed monitoring system collects, stores and visualizes strawberry growing environment data. The proposed harvest decision system classifies the strawberry maturity level in images using a deep learning algorithm. The monitoring and analysis results are visualized in an integrated interface, which provides a variety of basic data for strawberry cultivation. Even if the strawberry cultivation area increases, the proposed system can be easily expanded and flexibly based on a virtualized container with the concept of IoT-Edge-AI-Cloud. The monitoring system was verified by monitoring a hydroponic strawberry environment for 4 months. In addition, the harvest decision system was verified using strawberry pictures acquired from Smart Berry Farm.

US2RO: Union of Superpoints to Recognize Objects

2021 ◽  
Vol 15 (04) ◽  
pp. 513-537
Author(s):  
Marcel Tiator ◽  
Anna Maria Kerkmann ◽  
Christian Geiger ◽  
Paul Grimm
Keyword(s):  
Imitation Learning ◽  
Network Architectures ◽  
Application Development ◽  
Repetitive Work ◽  
Data Set ◽  
Partition System ◽  
Performance Metric ◽  
Growing Environment ◽  
The Creation ◽  
Near Future

The creation of interactive virtual reality (VR) applications from 3D scanned content usually includes a lot of manual and repetitive work. Our research aim is to develop agents that recognize objects to enhance the creation of interactive VR applications. We trained partition agents in our superpoint growing environment that we extended with an expert function. This expert function solves the sparse reward signal problem of the previous approaches and enables to use a variant of imitation learning and deep reinforcement learning with dense feedback. Additionally, the function allows to calculate a performance metric for the degree of imitation for different partitions. Furthermore, we introduce an environment to optimize the superpoint generation. We trained our agents with 1182 scenes of the ScanNet data set. More specifically, we trained different neural network architectures with 1170 scenes and tested their performance with 12 scenes. Our intermediate results are promising such that our partition system might be able to assist the VR application development from 3D scanned content in near future.

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