Identification of plant diseases using machine learning: a survey

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Abstract

Agriculture plays an important role across the globe. Nowadays manual plant disease identification has become difficult and time-consuming. Plant disease is an important aspect that reduces plant growth. According to the studies, plant disease has reduced the quality of plants in agriculture. Machine Learning approach helps to identify these plant diseases. Hence diseases like rice blast, stem rot, leaf scald, smut tungro, bacterial blight, brown spot, sheath blight, false, bacterial leaf streak, and fungal diseases, etc. can be identified using machine learning methods like clustering, classification, regression, etc. Different plant images that are affected by diseases are captured as well as wireless sensor network (WSN) also helps in disease identification. These images can be processed, and diseases can be identified. This practice gives rise to plant yield. This survey paper describes the machine learning approach for plant disease identification and a detailed study of various techniques is classified.

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Andhare, S., & Wankhade, S. (2021). Identification of plant diseases using machine learning: a survey. In Advances in Intelligent Systems and Computing (Vol. 1200 AISC, pp. 411–421). Springer. https://doi.org/10.1007/978-3-030-51859-2_38

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