Plant diseases have posed a major threat to farmers since the early days of agriculture. Today, despite our improved understanding of the causes and treatment of these diseases, they continue to cause significant economic losses. Although detecting plant diseases early is a farmer's best bet to minimize their impact, manual inspection of each plant is a monumental task and is prone to errors. Only a well-trained eye can accurately tell the difference between diseases that cause similar symptoms.
Fortunately, artificial intelligence (AI) is quickly paving the way to smarter agricultural practices. Recent machine learning models are capable of automated identification of plant diseases from digital photographs. When combined with drones and high-quality cameras, such models can reduce the time and effort needed to monitor large fields. However, even the latest algorithms struggle under specific challenging conditions.
One notable example is the effect of background interference on disease classification results. In some cases, diseased leaves acquire a color similar to that of soil, which tends to confuse the automatic classifier, particularly when the affected areas are on the edges of the leaves. Other problems include the variability of symptoms caused by a single disease and the similarities that exist between different diseases.
In a new study, a team of researchers set out to develop a model that could handle these challenges. They focused on five common diseases that affect tomato leaves and developed a machine learning model, called PLPNet, that can accurately detect these diseases from images taken in real-time. The study, led by Professor Guoxiong Zhou from China's Central South University of Forestry and Technology, was recently published in Plant Phenomics .
Read more at phys.org