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Detecting Plant Diseases Based on Leaf Analysis for IEEE Final Year Projects at Smart AI Technologies

Abstract:
Food supply must be raised in accordance with the rise in world population while also safeguarding crops from a number of lethal illnesses. Plant pathologists and farmers have historically used experience-based research to identify plant diseases with their unaided eyes. Traditional methods are challenging, time-consuming, and occasionally produce erroneous diagnoses, which causes major financial loss in agriculture. Later, a number of research studies used machine learning to identify plant diseases, but the results were unimpressive and the method was too slow for everyday usage. Convolution Neural Networks (CNNs) have recently achieved significant success in the field of computer vision because of their advantages over machine learning, such as automated feature extraction and the ability to provide efficient results quickly with little datasets. This study covers the difficulties in diagnosing plant leaf diseases and attempts to address the issue of incorrect and time-consuming disease detection and classification by analyzing several approaches and cutting-edge algorithms that are attempting to solve this problem.

At Smart AI Technologies, we provide the best final year engineering projects with complete support, specifically focusing on IEEE projects related to plant disease detection based on leaf analysis. Our support includes:
1. Complete working code implementation in clients' machines.
2. Complete classes on the project will be held to guide and support students throughout their project development.
3. Documentation support for the project.
4. Help in researching without any extra cost/price increase.

With our expertise and resources, we ensure that students receive comprehensive assistance and guidance for their final year projects in the field of plant disease detection using leaf analysis.

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