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Flower disease identification and classification by deep learning

Abstract

In India, Agriculture performs a crucial role because of the rapid increase of population and extended in demand for food. Therefore, it desires to increase crop yield. One major impact on low crop yield is an ailment caused by bacteria, viruses, and fungus. It can be avoided by the usage of plant disease detection strategies. Machine gaining knowledge of methods can be used for disease identification because it mainly practices on the information themselves and gives precedence to the outcomes of the sure task. This paper presents the levels of widespread flower illness detection systems and comparative study on gadgets getting to know classification strategies for flower disorder detection. In this survey, it located that Convolutional Neural Network offers high accuracy and detects an extra range of sicknesses.

Smart Agriculture and AIIdentification (biology)Convolutional neural networkPlant diseaseArtificial intelligenceComputer scienceAgricultureYield (engineering)Task (project management)Machine learningDeep learning
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Flower disease identification and classification by deep learning · Scinovex