CNN-based real-time prediction of growth stage in soybeans cultivated in hydroponic set-ups

被引:6
|
作者
Dhal, Sambandh Bhusan [1 ]
Mahanta, Shikhadri [2 ]
Gadepally, Krishna Chaitanya [1 ]
He, Samuel [1 ]
Hughes, Mary [1 ]
Moore, Janie [2 ]
Nowka, Kevin J. [1 ]
Kalafatis, Stavros [1 ]
机构
[1] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA
[2] Texas A&M Univ, Dept Biol & Agr Engn, College Stn, TX USA
来源
SOUTHEASTCON 2023 | 2023年
关键词
hydroponic; CVAT; CNN; Flask; GUI;
D O I
10.1109/SoutheastCon51012.2023.10115131
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The purpose of this research is to create a deep learning model capable of predicting the day of harvest for soybeans growing in hydroponic conditions. The algorithm uses feature extraction to calculate the day of growth for each annotated picture fed into the model. The recorded photos in this study were tagged using the Computer Vision Annotation Tool (CVAT), which was then used to train a five-layer Convolutional Neural Network (CNN) to predict the range of cultivation days. This pre-trained model was then deployed on the backend using Flask, and for each picture provided as input to the model, a Graphical User Interface (GUI) was created to accept a taken image as input and estimate the day of cultivation for real-time application.
引用
收藏
页码:193 / 197
页数:5
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