Abstract
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.
Highlights
The food and beverage industry is characterized by a relatively small number of multinational companies that link small producers around the world with consumers
The presented state-of-the-art showed that several researchers sought the detection of any vital element like water stress, nitrogen levels, and vegetation indexes that could lead to an improvement of production and quality in crops, which translated to an increase in profitability
The objective of this research is to evaluate to what extent it is possible to diagnose the Coffee Leaf Rust (CLR) development stage in the Colombian Caturra variety through a technological integration system that involves Remote Sensing (RS), Wireless Sensor Networks (WSN), and Deep Learning (DL)
Summary
The food and beverage industry is characterized by a relatively small number of multinational companies that link small producers around the world with consumers. A development analysis conducted by the World Economic Forum and Accenture, in 2018 [1], focused, predominantly, on upstream value chain segments due to the low tech nature of food and beverage processing and production and the substantial potential for improving efficiency in agrifood activities. According to the Organisation for Economic Co-operation and Development (OECD), the food and beverage industry is classified as a low tech industry, so it can add innovation without significant social disadvantages [2]. According to the OECD, each opportunity presented by the Fourth Industrial
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
