As agriculture struggles to support the rapidly growing global population, the productivity and quality of food crops is being undermined by plant diseases and nutritional disorders. These oftentimes result in catastrophic losses, estimated to be between 20% and 40% for major food and cash crops (rice, wheat, maize, barley, potatoes, soybeans, cotton, and coffee) at country and regional levels in different continents (Oerke, 2006). Crop losses have grave implications for the wellbeing of rural families, as well as for food security and economic and political stability at the national, regional and global level (Zadoks and Schein, 1979; Savary and Willocquet, 2014; Avelino et al., 2015). To avoid losses, farmers spend billions of dollars on plant disease management and chemical pesticides and fertilizers, often without adequate technical support, resulting in poor control, pollution and other harmful effects. Plant diseases can also devastate natural ecosystems, further exacerbating environmental problems caused by habitat loss and poor land management.
Although agricultural extension workers and
other professionals are responsible for the recognition of plant diseases,
intelligent systems can facilitate in-situ diagnosis at early stages. Various
systems and applications for specific crops, agricultural systems and even
countries have been developed for this purpose. They rely on facts described by
the user or on image processing of plant photos in visible/infrared light. The recognition
of a disease or any many other stress factors (e.g. nutrient deficiency) is
based on symptoms such as lesions or spots, which can be characterized based on
color and shape (RGB images: true color image stored digitally as an m-by-n-by-3
data array that defines red, green, and blue color components for each
individual pixel), spectral signatures and even temperature. Low cost and
user-friendly smartphone applications with such capabilities may help farmers
to early detect and even identify diseases and disorders in their crops in-situ.
Higher cost molecular analyses and tests could follow if necessary.
For example, Plantix (https://plantix.net/en),
developed by Hannover University-based startup Peat, is a free mobile
application which offers farmers and gardeners the possibility to receive
decision support directly on their smartphone. Using image recognition, the app
is able to identify the plant type, as well as the appearance of a possible
disease, pest or nutrient deficiency. Plantix takes advantage of deep learning
technology which involves neural networks. Furthermore, it provides information
on treatment and preventive measures. The daily new images sent by Plantix
users worldwide to the system allow it to constantly learn and send back to the users up-to-date information and alerts in terms of plant
diseases, pests and their worldwide distribution in real time. Other example is
Maize Doctor (http://ww12.maizedoctor.org/), developed by CIMMYT (The
International Maize and Wheat Improvement Center), which a simple, stepwise method for identifying maize production
problems, pests and diseases and suggests ways you can overcome your problems.
However, the weakness of these apps (and many other of
similar characteristics) is that they depend to a large extent on a central
server and lacks autonomy from the own capacities of the mobile phone.
The aforementioned weakness can be overcome by
the system developed by the University of Barcelona (UB): to be exact, the Integrative
Crop Ecophysiology Group within the Plant Physiology Unit, Department of
Evolutionary Biology, Ecology and Environmental Sciences. It comprises a simple
yet fully integrated data collection and processing platform that connects the
Open Data Kit (ODK) Collect (https://opendatakit.org/) with a Google
cloud-based organization structure for gathering image and questionnaire data
that can be easily accessed and processed in real-time using any personal
computer connected to the internet (for more details see https://integrativecropecophysiology.com/fusion/).
The ODK Fusion application allows for even easier data-sharing via GoogleSheets
and geographical visualization and machine learning analyses through the new
Google BigQuery GIS tools. Both may be used along with the mobile app
geographical data as inputs for driving satellite-based analyses through the
Google Earth Engine API. Together, they not only enable strong user feedback
and an enlightening user experience that may contribute to better
participation, but also a very strong potential for backend analyses at local
to regional scales.
The International Center for Biosaline
Agriculture (ICBA), based in Dubai, proposes to implement a project in
partnership with UB that will take advantage of the system developed by the
latter to create a low-cost, user-friendly application for smartphones that
farmers can use to identify and address diseases and nutritional disorders in
their produce and thus minimize yield losses. ICBA has previously developed
several brochures and booklets to help farmers detect some of the problems
affecting their crops. However, as the global population becomes more
accustomed to and reliant on rapidly developing ICT technologies, farmers –
particularly new generations – are more attracted to digital applications than
paper-based publications. The almost universal availability of mobile phones,
even among farmers in developing countries, provides many with the opportunity
to access the information anywhere.
The project will initially target farmers in
four countries of the Middle East and North Africa (MENA) region, including the
United Arab Emirates (UAE), Egypt, Tunisia and Morocco, and will be expanded to
other countries in the future. The application will first focus on the greenhouse
crops commonly grown in the region, such as tomato, capsicum and cucumber, as
well as some newly introduced crops. Among them is quinoa, a stress-tolerant crop
with the potential to make a significant contribution to food security in the
countries of the MENA region, particularly those adversely affected by climate
change impacts such as water stress and increasing soil and water salinity. The
application will be gradually expanded to include a wide range of other conventional
and underutilized crops.
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