Automatic Detection of Fungal and Bacterial Plant Leaves Diseases: A Review
R. Manavalan *
Department of Computer Science, Arignar Anna Government Arts College, Villupuram – 605 602, Tamilnadu, India.
*Author to whom correspondence should be addressed.
Abstract
Agriculture is the country's most vital economic growth sector, and it is intertwined with every aspect of society. Agriculture provides revenue and food for the people. Agriculture production is adversely affected by various bacterial and fungal diseases, including leaf spot, bacterial leaf scorch, bacterial blight, Alternaria Alternata, Cercospora Leaf Spot, Anthracnose, yellow spots, and early blight. The loss of plant crops can lead to hunger and malnutrition. Farmers suffer significant economic losses when their plants are infected with various diseases. Early diagnosis of plant pathogens is critical for increased productivity. Because of the difficulty to distinguish between plant diseases with the naked eye, incorrect pesticide control assessments are made. As a result, for increased growth and quality, automatic plant disease diagnosis is required. The image processing paradigm extract information from plant leaves quickly and accurately, allowing disease kinds to be identified at an early stage. This research examines a variety of image processing paradigm and machine learning approaches for extracting key features and swiftly analyzing them for the detection of various fungal and bacterial leaf diseases. The review study also discussed the challenges that computational techniques for assessing plant leaves confront, as well as possible future directions.
Keywords: Plant diseases, bacterial, fungal diseases, SVM, neural network, image processing
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