Using MLP Neural Networks to Detect Late Blight in Brazilian Tomato Crops

Using MLP Neural Networks to Detect Late Blight in Brazilian Tomato Crops

Sergio Manuel Serra Cruz, Gizelle Kupac Vianna
DOI: 10.4018/978-1-7998-0414-7.ch061
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The food quality is a major issue in agriculture, economics, and public health. The tomato is one the most consumed vegetables in the world, having a significant production chain in Brazil. Its culture permeates many economic and social sectors. This paper presents a technological approach focused on enhancing the quality of tomatoes crops. The authors developed intelligent computational strategies to support early detection of diseases in Brazilian tomato crops. Their approach consorts real field experiments with inexpensive computer-aided experiments based on pattern recognition using neural networks techniques. The recognition tasks aimed at the identification foliage diseases named late blight, which is characterized by the incidence of brown spots on tomato leaves. The identification method achieved a hit rate of 94.12%, by using digital images in the visible spectrum of the leaves.
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Late Blight In Tomato Crops

The tomatoes are grown and eaten all around the world. It was originated from the South America (Andean region) and was imported by Europe in the 15th century. Domestication on a much more intense level occurred throughout Europe in the 18th and 19th centuries (Sims, 1980). Since the beginning of the 20th century, farmers created an enormous range of morphologically different cultivars and forms from a single species S. lycopersicum. Through domestication, the modern tomato genotypes (mostly hybrids varieties) have been developed with different shapes, colors, and sizes. Nowadays, the tomato is consumed in several ways, including raw tomatoes in salads, and processed into ketchup, pulp, juice, puree, and sauce or soup.

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