Abstract:
Plant diseases are one of the leading causes of the loss of crops. Monitoring the plant growth conditions could help prevent the spread of diseases and ensure healthy agr...Show MoreMetadata
Abstract:
Plant diseases are one of the leading causes of the loss of crops. Monitoring the plant growth conditions could help prevent the spread of diseases and ensure healthy agricultural productivity. A leaf wetness sensor (LWS) is used to detect wetness on the surface of the leaves. Leaf wetness duration (LWD) is an essential parameter that governs the possibility of the occurrence of diseases in plants. The sensor system used in this work for monitoring the environmental conditions transmits data every 30 min due to power consumption considerations. To improve the time resolution of the signal and accurate estimation of LWD, a deep-learning-based architecture is proposed. The data from LWS has been used to train the architecture and the results obtained have been compared with the existing methods to analyze its performance. Using the proposed architecture, the signal from LWS was benchmarked with the commercially available Phythos-31. The time resolution of data has been improved from ±30 to ±4 min. The average signal-to-noise ratio (SNR) values for three test signals with improved time resolution have increased from 19.36 to 22.16 dB. Similarly, the average root means square error (RMSE) of the estimated LWD values after time resolution improved from 0.55 to 0.20. During the experimental analysis, it has been observed that the proposed architecture estimates the LWD values better compared to the other time-resolution techniques.
Published in: IEEE Sensors Journal ( Volume: 22, Issue: 24, 15 December 2022)
Funding Agency:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Leaf Wetness ,
- Leaf Wetness Duration ,
- Root Mean Square Error ,
- Disease Transmission ,
- Sensory Systems ,
- Time Resolution ,
- Plant Disease ,
- Signal Resolution ,
- Interpolation ,
- Convolutional Neural Network ,
- Convolutional Layers ,
- Internet Of Things ,
- Sensor Data ,
- Generative Adversarial Networks ,
- Magnitude Of Variation ,
- Original Signal ,
- Signal Quality ,
- Residual Block ,
- Residual Network ,
- Deep Learning Architectures ,
- Lower Branch ,
- Bicubic Interpolation ,
- Rest Of The Methods ,
- Commercial Sensors ,
- Total Variation Regularization ,
- Short-time Fourier Transform ,
- Signal-to-noise Ratio Values ,
- Output Of Module ,
- Signal Values ,
- Input Signal
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Leaf Wetness ,
- Leaf Wetness Duration ,
- Root Mean Square Error ,
- Disease Transmission ,
- Sensory Systems ,
- Time Resolution ,
- Plant Disease ,
- Signal Resolution ,
- Interpolation ,
- Convolutional Neural Network ,
- Convolutional Layers ,
- Internet Of Things ,
- Sensor Data ,
- Generative Adversarial Networks ,
- Magnitude Of Variation ,
- Original Signal ,
- Signal Quality ,
- Residual Block ,
- Residual Network ,
- Deep Learning Architectures ,
- Lower Branch ,
- Bicubic Interpolation ,
- Rest Of The Methods ,
- Commercial Sensors ,
- Total Variation Regularization ,
- Short-time Fourier Transform ,
- Signal-to-noise Ratio Values ,
- Output Of Module ,
- Signal Values ,
- Input Signal
- Author Keywords