Wireless Sensor Network and Irrigation System to Monitor Wheat Growth under Drought Stress

Amin, Atia B.; Dubois, Georges Octave; Thurel, Sephora; Danyluk, Jean; Boukadoum, Mounir; Diallo, Abdoulaye Baniré

doi:10.1109/iscas51556.2021.9401545

Cited by 5 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This experiment used rapeseed plants for different water treatment applications. Three groups of experimental pots were placed in the plant growth incubator to ensure that other external environmental factors remained unchanged during the water control process [16], [17]. After two weeks of water control, an experiment was conducted to collect electrical signals from rapeseed plants.…”

Section: Collection Of Plant Electrical Signalsmentioning

confidence: 99%

Research on Classification of Water Stress State of Plant Electrical Signals Based on PSO-SVM

Tian,

Shang,

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Plant electrical signals are physiological signals within the plant body that respond to both external and internal stimuli. Using plant electrical signals as an effective indicator for evaluating the plant growth status is a new theory and method to study the relationship between environmental factors affecting plant growth and plant growth responses. This novel proposed approach is advantageous in terms of response sensitivity and accuracy. Therefore, automation and intelligence of agricultural plant cultivation can be realized and implemented by monitoring the changes in the patterns of plant electrical signals. This paper investigated rapeseed plants in three groups and evaluated the effect of soil water content as the controlled environmental variable. The plant electrical signals under different soil water contents were collected for wavelet packet noise reduction processing. The plant electrical signals were analyzed from three aspects: time domain, frequency domain, and wavelet packet decomposition. The mean, root mean square, standard deviation in the time domain, the power spectral entropy (PSE) of the centroid frequency (SCF) in the frequency domain, and the electrical signal energy in the wavelet packet decomposition were used as the eigenvectors required for classification. The external plant morphological data and the rapeseed growth under different soil water contents were collected to establish plant water stress evaluation indexes. By this, the optimal water demand gradient for rapeseed growth was obtained. The plant water stress evaluation index was used to verify the classification effect of electrical signals, combined with the plants' electrical signal changes under different water stress conditions to comprehensively evaluate the growth status of plants under different soil water contents. Finally, a support vector machine (SVM) and a particle swarm optimized support vector machine (PSO-SVM) were used to classify the water stress status of plants and establish prediction model the relationship between plant growth status and plant electrical signals under different soil water contents. The results showed that the plant water stress state classification model accuracy based on SVM was 90.83%, and the mean square error MSE was 0.175, while the accuracy of the plant water stress state classification model based on PSO-SVM was 94.3167%, and the mean square error was 0.1646. The classification experiments results show that the water stress of plants and the classification of plant growth status can be realized utilizing electrical signal analysis, with the support vector machine classification model after particle swarm optimization being more accurate. This method lays the foundation for the realization of automation in agricultural plant cultivation and monitoring through plant electrical signals.INDEX TERMS plants electrical signals, Water stress, Plant water stress evaluation index, PSO-SVM Classification Model I. INTRODUCTIONPlant growth is a process of continuous material and energy exchang...

show abstract

Section: Collection Of Plant Electrical Signalsmentioning

confidence: 99%

Research on Classification of Water Stress State of Plant Electrical Signals Based on PSO-SVM

Tian,

Shang,

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…As part of a more extensive study of this crop's genetic responses to drought stress, [25] have developed a sensory control system for wheat to simulate drought conditions. A drip irrigation technique was applied to maintain the soil moisture on potted wheat plants within a specified range.…”

Section: ) Wireless Sensor Network (Wsn)mentioning

confidence: 99%

The Recent Technological Trends of Smart Irrigation Systems in Smart Farming: A Review

Khebbache,

Merizig,

Rezeg

et al. 2023

IJCDS

View full text Add to dashboard Cite

Agriculture is considered one of the most vital industries worldwide responsible for people's food and the country's economic development. Nevertheless, this sector is the largest consumer of freshwater. Access to water, soil kind, weather conditions, fertilizers, and illnesses are crucial aspects of agricultural activities. Moreover, water scarcity has become a significant issue for farmers to tackle irrigation activity. While irrigation depends on water availability, building smart irrigation systems that control and manage water use efficiency is highly needed. This paper examines the most modern technologies used in irrigation systems over the last few years. This survey can easily be thorough and beneficial to academics while providing essential recommendations for the new era of irrigation systems technologies. The findings indicate widespread use of the Internet of Things (IoT) and artificial intelligence algorithms, while digital twins and blockchain technologies are less common and still in the early stages. To reduce costs and improve security in irrigation systems, it is strongly recommended to adopt digital twin technology. By utilizing digital twins, farmers can enhance operations, optimize resource utilization, and increase efficiency. Furthermore, digital twins contribute to identifying vulnerabilities and implementing robust security measures, ensuring protection against potential threats and disruptions.

show abstract

Precision Agriculture Farming by Monitoring and Controlling Irrigation System Using Sensors

Kumar

Sobhana

Duvvuru

et al. 2022

Intelligent Data Communication Technologies and Internet of Things

View full text Add to dashboard Cite

Wireless Sensor Network and Irrigation System to Monitor Wheat Growth under Drought Stress

Cited by 5 publications

References 10 publications

Research on Classification of Water Stress State of Plant Electrical Signals Based on PSO-SVM

Research on Classification of Water Stress State of Plant Electrical Signals Based on PSO-SVM

The Recent Technological Trends of Smart Irrigation Systems in Smart Farming: A Review

Precision Agriculture Farming by Monitoring and Controlling Irrigation System Using Sensors

Contact Info

Product

Resources

About