scholarly journals A Cyber-Physical Data Collection System Integrating Remote Sensing and Wireless Sensor Networks for Coffee Leaf Rust Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5474
Author(s):  
David Velásquez ◽  
Alejandro Sánchez ◽  
Sebastián Sarmiento ◽  
Camilo Velásquez ◽  
Mauricio Toro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Leaf Rust ◽  
Test Bench ◽  
Wireless Sensor ◽  
Collection System ◽  
Data Collection System ◽  
Coffee Leaf Rust

Coffee Leaf Rust (CLR) is a fungal epidemic disease that has been affecting coffee trees around the world since the 1980s. The early diagnosis of CLR would contribute strategically to minimize the impact on the crops and, therefore, protect the farmers’ profitability. In this research, a cyber-physical data-collection system was developed, by integrating Remote Sensing and Wireless Sensor Networks, to gather data, during the development of the CLR, on a test bench coffee-crop. The system is capable of automatically collecting, structuring, and locally and remotely storing reliable multi-type data from different field sensors, Red-Green-Blue (RGB) and multi-spectral cameras (RE and RGN). In addition, a data-visualization dashboard was implemented to monitor the data-collection routines in real-time. The operation of the data collection system allowed to create a three-month size dataset that can be used to train CLR diagnosis machine learning models. This result validates that the designed system can collect, store, and transfer reliable data of a test bench coffee-crop towards CLR diagnosis.

scholarly journals A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks, Remote Sensing, and Deep Learning: Case Study of the Caturra Variety in Colombia

2020 ◽  
Vol 10 (2) ◽  
pp. 697 ◽  
Author(s):  
David Velásquez ◽  
Alejandro Sánchez  ◽  
Sebastian Sarmiento  ◽  
Mauricio Toro  ◽  
Mikel Maiza ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wireless Sensor Networks ◽  
Deep Learning ◽  
Sensor Networks ◽  
Leaf Rust ◽  
Visual Inspection ◽  
Wireless Sensor ◽  
Diagnostic Model ◽  
Coffee Leaf Rust ◽  
Technological Integration

Agricultural activity has always been threatened by the presence of pests and diseases that prevent the proper development of crops and negatively affect the economy of farmers. One of these pests is Coffee Leaf Rust (CLR), which is a fungal epidemic disease that affects coffee trees and causes massive defoliation. As an example, this disease has been affecting coffee trees in Colombia (the third largest producer of coffee worldwide) since the 1980s, leading to devastating losses between 70% and 80% of the harvest. Failure to detect pathogens at an early stage can result in infestations that cause massive destruction of plantations and significantly damage the commercial value of the products. The most common way to detect this disease is by walking through the crop and performing a human visual inspection. As a result of this problem, different research studies have proven that technological methods can help to identify these pathogens. Our contribution is an experiment that includes a CLR development stage diagnostic model in the Coffea arabica, Caturra variety, scale crop through the technological integration of remote sensing (through drone capable multispectral cameras), wireless sensor networks (multisensor approach), and Deep Learning (DL) techniques. Our diagnostic model achieved an F1-score of 0.775. The analysis of the results revealed a p-value of 0.231, which indicated that the difference between the disease diagnosis made employing a visual inspection and through the proposed technological integration was not statistically significant. The above shows that both methods were significantly similar to diagnose the disease.

scholarly journals Distributed and morphological operation-based data collection algorithm

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771759 ◽  
Author(s):  
Yalin Nie ◽  
Haijun Wang ◽  
Yujie Qin ◽  
Zeyu Sun
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Network Lifetime ◽  
Algorithm Analysis ◽  
Wireless Sensor ◽  
Morphological Operation ◽  
Event Monitoring ◽  
Binary Images ◽  
Structuring Element

When monitoring the environment with wireless sensor networks, the data sensed by the nodes within event backbone regions can adequately represent the events. As a result, identifying event backbone regions is a key issue for wireless sensor networks. With this aim, we propose a distributed and morphological operation-based data collection algorithm. Inspired by the use of morphological erosion and dilation on binary images, the proposed distributed and morphological operation-based data collection algorithm calculates the structuring neighbors of each node based on the structuring element, and it produces an event-monitoring map of structuring neighbors with less cost and then determines whether to erode or not. The remaining nodes that are not eroded become the event backbone nodes and send their sensing data. Moreover, according to the event backbone regions, the sink can approximately recover the complete event regions by the dilation operation. The algorithm analysis and experimental results show that the proposed algorithm can lead to lower overhead, decrease the amount of transmitted data, prolong the network lifetime, and rapidly recover event regions.

scholarly journals Capacity of data collection in randomly-deployed wireless sensor networks

2010 ◽  
Vol 17 (2) ◽  
pp. 305-318 ◽  
Author(s):  
Siyuan Chen ◽  
Yu Wang ◽  
Xiang-Yang Li ◽  
Xinghua Shi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Wireless Sensor
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