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.
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.
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