scholarly journals New Non-invasive Tools for Early Plant Stress Detection

2015 ◽  
Vol 29 ◽  
pp. 249-250 ◽  
Author(s):  
E.Gorbe Sánchez ◽  
E. Heuvelink ◽  
Arie de Gelder ◽  
C. Stanghellini
Keyword(s):  
Plant Stress ◽  
Stress Detection ◽  
Non Invasive

scholarly journals Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Biosensors ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 193
Author(s):  
Alanna V. Zubler ◽  
Jeong-Yeol Yoon
Keyword(s):  
Machine Learning ◽  
Optical Sensors ◽  
Near Infrared ◽  
Plant Stress ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Stress Detection ◽  
Recent Advances ◽  
Wide Range

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

Hyperspectral Image Analysis for Plant Stress Detection

2010 ◽  
Author(s):  
Yunseop Kim ◽  
David M Glenn ◽  
Johnny Park ◽  
Henry K Ngugi ◽  
Brian L Lehman
Keyword(s):  
Image Analysis ◽  
Hyperspectral Image ◽  
Plant Stress ◽  
Stress Detection ◽  
Hyperspectral Image Analysis

scholarly journals Plant Stress Detection Accuracy Using Deep Convolution Neural Networks

2019 ◽  
pp. 263-270
Author(s):  
Chege Kirongo ◽  
Kelvin Omieno ◽  
Makau Mutua ◽  
Vitalis Ogemah
Keyword(s):  
Neural Networks ◽  
Food Security ◽  
Plant Stress ◽  
Crop Productivity ◽  
Detection Accuracy ◽  
Activation Functions ◽  
Stress Detection ◽  
Convolution Neural Networks ◽  
Abiotic Stressors ◽  
Complex Relationships

Plant Stress detection is a vital farming activity for enhanced productivity of crops and food security. Convolution Neural Networks (CNN) focuses on the complex relationships on input and output layers of neural networks for prediction. This task further helps in detecting the behavior of crops in response to biotic and abiotic stressors in reducing food losses. The enhancement of crop productivity for food security depends on accurate stress detection. This paper proposes and investigates the application of deep neural network to the tomato pests and disease stress detection. The images captured over a period of six months are treated as historical dataset to train and detect the plant stresses. The network structure is implemented using Google’s machine learning Tensor-flow platform. A number of activation functions were tested to achieve a better accuracy. The Rectifier linear unit (ReLU) function was tested. The preliminary results show increased accuracy over other activation functions.

scholarly journals Imaging of Chlorophyll a Fluorescence in Natural Compound-Induced Stress Detection

2020 ◽  
Vol 11 ◽  
Author(s):  
Adela M. Sánchez-Moreiras ◽  
Elisa Graña ◽  
Manuel J. Reigosa ◽  
Fabrizio Araniti
Keyword(s):  
Imaging Techniques ◽  
Fluorescence Emission ◽  
Invasive Technique ◽  
Plant Defense Response ◽  
Stress Detection ◽  
Secondary Effects ◽  
Non Invasive ◽  
Induced Stress ◽  
Fluorescence Images ◽  
Spatio Temporal

Imaging of chlorophyll a fluorescence (CFI) represents an easy, precise, fast and non-invasive technique that can be successfully used for discriminating plant response to phytotoxic stress with reproducible results and without damaging the plants. The spatio-temporal analyses of the fluorescence images can give information about damage evolution, secondary effects and plant defense response. In the last years, some studies about plant natural compounds-induced phytotoxicity have introduced imaging techniques to measure fluorescence, although the analysis of the image as a whole is often missed. In this paper we, therefore, evaluated the advantages of monitoring fluorescence images, presenting the physiological interpretation of different possible combinations of the most relevant parameters linked to fluorescence emission and the images obtained.

scholarly journals Thermal Imaging for Plant Stress Detection and Phenotyping

2020 ◽  
Vol 13 (1) ◽  
pp. 68
Author(s):  
Mónica Pineda ◽  
Matilde Barón ◽  
María-Luisa Pérez-Bueno
Keyword(s):  
Imaging Techniques ◽  
Plant Stress ◽  
Stress Factors ◽  
Plant Phenotyping ◽  
Physiological Status ◽  
Stress Detection ◽  
Factors Affecting ◽  
Crop Fields ◽  
Abiotic Stress Factors ◽  
Imaging Sensors

In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.

Plant Stress Detection by Reflectance and Fluorescencea

1998 ◽  
Vol 851 (1 STRESS OF LIF) ◽  
pp. 271-285 ◽  
Author(s):  
H. K. LICHTENTHALER ◽  
O. WENZEL ◽  
C. BUSCHMANN ◽  
A. GITELSON
Keyword(s):  
Plant Stress ◽  
Stress Detection
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