How artificial intelligence and machine learning can help farmers diagnose crop diseases?
Plant diseases are one of the most important reasons that lead to the destruction of plants and crops. Detecting those diseases at early stages enable farmers to overcome and treat them appropriately. Expert systems help a great deal in identifying those diseases and describing methods of treatment. Can artificial intelligence and machine learning techniques help diagnose crop diseases, increase food security in a country or region and keep feeding the world?
The first commercial expert system evolved as a product of artificial intelligence and is now available in number of fields that requires decision-making. The sustainability of this technology has been recognized and realized in the field of agriculture and a few successful expert systems have been developed. Riley have developed an expert system for plant disease diagnosis based on visual observations of symptoms expressed by the infected plant. El-Dessoki developed an integrated expert system for crop management of cucumber.
Rajkishore developed an expert system for the diagnosis of pests, disease and disorders in Indian mango. Lopez-Morales developed an intelligent system for the diagnosis and control of tomatoes diseases and pests in greenhouses. Gonzalez-Andujaret developed an expert system for pests, diseases and weeds identification in olive crops. These expert systems concentrate on a specific type of disease and one methodology of diagnosis.
Development of a visual method of discriminating between crop seedlings and weeds is an important and necessary step towards the automation of non-chemical weed control systems in agriculture, and towards the reduction in chemical use through spot spraying.
Food security is threatened by many things. In some regions, climate variability causes droughts that make vital resources scarce. In others, political turmoil creates logistical blockades for farming, harvesting, and shipping produce. But, practically everywhere, plant disease can wipe out entire crops with little warning.
A team of researchers at Pennsylvania State University and the École Polytechnique Fédérale de Lausanne, Switzerland have turned the keen eye of artificial intelligence toward agriculture, using deep learning algorithms to help detect crop disease before it spreads.
Most crops in developed regions are farmed through large-scale operations, where sufficient finances and manpower help tackle disease early on. In developing regions, up to 80 percent of agricultural production is conducted by smallholder farmers, according to the study published in Frontiers in Plant Science. These small-scale operations are more prone to the devastating effects of crop disease, which can wipe out entire crops and lead to localized or widespread famine. The issue is made worse by the fact that as many as 50 percent of the world’s hungry population lives in smallholder farm households, with too few resources to address crop disease quickly.
Along with lead author Sharada Mohanty and co-author Marcel Salathé of EPFL, David Hughes developed a program that’s fast, efficient, and compact enough to pack into a smartphone. They trained the algorithm by feeding it huge datasets — over 50,000 images — gathered as a part of PlantVillage, an open access online archive of plant photos including images of plant disease. With this data, the researchers trained the algorithm to identify 26 different disease in 14 different plant species. After the training phase, the program performed with 99.35% accuracy, giving any smartphone user the ability to identify diseases with the eye of a well-trained expert.
Another great example of artificial intelligence and machine learning in farming is Plantix – mobile app that is helping farmers on three continents quickly identify plant diseases. For several years in the Brazilian rain forest, a team of young German researchers studied the emission and mitigation of greenhouse gases due to changing land use. The team’s analysis was yielding new knowledge, but the farmers they worked with weren’t interested in those findings. They wanted to know how to treat crops being ravaged by pathogens.
Today, farmers in Germany, Brazil and India use Plantix to upload photos of diseased crops. The images are part of a huge and growing crowdsourced database that is helping farmers to identify, treat and prevent crop diseases.
The magic happens once it has received the photos and runs them through its image recognition software — which grows more powerful with each new crop disease the company logs. The app has been downloaded 50,000 times in the last year, resulting in 100,000 image uploads into dataset. Already, it can identify more than 60 plant pests and pathogens with more than 90 percent accuracy. Those numbers figure to rise as the database grows.
It also hopes to start implementing its software on drones, agricultural equipment and greenhouses next year so that farmers can automate the process and respond to crop illness quicker. For now, it’s collecting photos and learning how to accurately identify as many crop diseases as possible, with a focus on major crops like corn and wheat that are planted by large-scale farmers around the world. In exchange for supplying photos, users of Plantix receive actionable information, including the scientific names of diseases, along with triggers, symptoms, treatment options, preventative measures and the like.
Global food security is one of the most pressing issues for humanity, and agricultural production is critical for achieving this. Agricultural production is critical for achieving global food security, as are factors such as economic development for everyone, fair international trade agreements, and sound global and national governance. Plant and animal diseases, environmental degradation, climate change are all future threads to our world. Now artificial intelligence and machine learning fight notorious crop diseases. It’s a real green revolution.
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