Water Stress Identification in Chickpea Plant Shoot Images using Deep Learning

2020 ◽  
Author(s):  
Shiva Azimi ◽  
Taranjit Kaur ◽  
Tapan K Gandhi
Keyword(s):  
Deep Learning ◽  
Water Stress ◽  
Chickpea Plant

Identifying crop water stress using deep learning models

2020 ◽  
Author(s):  
Narendra Singh Chandel ◽  
Subir Kumar Chakraborty ◽  
Yogesh Anand Rajwade ◽  
Kumkum Dubey ◽  
Mukesh K. Tiwari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Water Stress ◽  
Learning Models ◽  
Crop Water ◽  
Crop Water Stress

scholarly journals Classification of water stress in cultured Sunagoke moss using deep learning

2021 ◽  
Vol 19 (5) ◽  
pp. 1594
Author(s):  
Yusuf Hendrawan ◽  
Retno Damayanti ◽  
Dimas Firmanda Al Riza ◽  
Mochamad Bagus Hermanto
Keyword(s):  
Deep Learning ◽  
Water Stress

Real-Time Detection of Water Stress in Corn Using Image Processing and Deep Learning

2020 ◽  
Author(s):  
Mor Soffer ◽  
Naftali Lazarovitch ◽  
Ofer Hadar
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Water Stress ◽  
Crop Production ◽  
Irrigation Treatment ◽  
Agricultural Crop ◽  
Learning Techniques ◽  
Web Camera ◽  
Use Efficiency ◽  
Non Destructive

<p>Water limitation is one of the main environmental constraints that adversely affects agricultural crop production around the world. Precise and rapid detection of plant water stress is critical for increasing agricultural productivity and water use efficiency. Numerous studies conducted over the years have attempted to find effective ways to correctly recognize situations of water stress in order to determine irrigation regimes.</p><p>Water stress detection is currently done by various methods that are not ideal; these methods are often very expensive, destructive and cumbersome. Water stress in plants is also expressed at different visual levels. Image processing is alternative way to visually recognize water stress levels. Such analysis is non-destructive, inexpensive and allows to examine the spatial variability of stress level under field conditions.</p><p>In our study, we propose a new method for detecting water stress in corn using image processing and deep learning. For the purpose of collecting the images, we performed a three-months experiment, in which we took images of five different groups of corn. Each group had a different irrigation treatment, which led to five different levels of water stress. The images were collected using a web camera located approximately 2 m from the plants.</p><p>Stress classification was done by inserting processed images into a Convolutional Neural Network (CNN). Training the network was done using transfer-learning techniques in order to exploit the performance of an already trained CNN, for a fast and efficient training over the dataset. Testing the quality of classification was done using extra camera which took a different set of images.</p><p>Results were tested upon two sub-experiments - classification of three types of treatments and classification of five types of treatments; the results were 98% accuracy in classification into three types of treatments (well-watered, reduced-watered and draught stressed treatment), and 85% accuracy in classification into five different treatments. These initial results are definitely excellent and can certainly serve decision making for optimal irrigation. <strong> </strong></p>

scholarly journals Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

2021 ◽  
Vol 11 (4) ◽  
pp. 1403
Author(s):  
Mohd Hider Kamarudin ◽  
Zool Hilmi Ismail ◽  
Noor Baity Saidi
Keyword(s):  
Deep Learning ◽  
Water Stress ◽  
Sensor Fusion ◽  
Water Status ◽  
System Modeling ◽  
Economic Loss ◽  
Plant Water ◽  
Plant Water Stress ◽  
Stress Assessment ◽  
Intelligence System

Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

scholarly journals HortNet417v1—A Deep-Learning Architecture for the Automatic Detection of Pot-Cultivated Peach Plant Water Stress

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7924
Author(s):  
Md Parvez Islam ◽  
Takayoshi Yamane
Keyword(s):  
Deep Learning ◽  
Water Stress ◽  
Plant Stress ◽  
High Stress ◽  
Classification Performance ◽  
Stress Conditions ◽  
Test Accuracy ◽  
Plant Water ◽  
Plant Water Stress ◽  
Learning Techniques

The biggest challenge in the classification of plant water stress conditions is the similar appearance of different stress conditions. We introduce HortNet417v1 with 417 layers for rapid recognition, classification, and visualization of plant stress conditions, such as no stress, low stress, middle stress, high stress, and very high stress, in real time with higher accuracy and a lower computing condition. We evaluated the classification performance by training more than 50,632 augmented images and found that HortNet417v1 has 90.77% training, 90.52% cross validation, and 93.00% test accuracy without any overfitting issue, while other networks like Xception, ShuffleNet, and MobileNetv2 have an overfitting issue, although they achieved 100% training accuracy. This research will motivate and encourage the further use of deep learning techniques to automatically detect and classify plant stress conditions and provide farmers with the necessary information to manage irrigation practices in a timely manner.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning
Sign in / Sign up

Export Citation Format

Share Document