Abstract: Plant diseases pose a significant threat to agricultural produce and have disastrous consequences for farmers as the world's population grows. Early detection of plant disease can help ensure food security while also limiting financial losses. Images of diseased plants can aid in disease identification. Convolutional Neural Networks' classification abilities are used to generate consistent results. The simplicity of the created CNN model demonstrates its development and innovation; healthy leaves and backdrop images are consistent with previous CNN models. Using CNN, the model can distinguish between damaged and healthy leaves. Plants are the primary source of food on the planet. Plant infections and illnesses are a major risk, and the most common method of diagnosing plant diseases is to examine the plant body for visible signs and growth

[1]. Various research efforts aim to identify realistic plant protection techniques and assist our farmers as an alternative to the old time-consuming process. Technological advancements have spawned a slew of new ways to supplement old procedures in recent years
[2]. Deep learning approaches are especially effective and powerful in image classification challenges.

PDF | DOI: 10.17148/IJARCCE.2022.11496

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