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Indian economy depends a lot on agriculture. Around 38% of the land in India is suitable for agriculture. The crop in the agricultural land is threatened by the diseases. These diseases may have been caused by microorganisms, viruses, fungi, and bacteria. The traditional method of identifying the crop disease by the naked eye needs enormous labor, expertise with crop diseases, and needs a lot of time. In most cases, by the time the results are out, the damage is already done. To avoid such problems, an automated system can be adapted for the detection of plant diseases. The aim is to build a model that is fast, accurate, and reliable that can be used for the identification of leaf diseases. In this article, we compared three deep learning neural networks we evaluated the performance of three deep neural networks and analyzed the strengths of each neural network interms of accuracy. We also determined the working of each model and its applications. [ABSTRACT FROM AUTHOR]