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[Audio] Hi guys, today I will show my presentation about AUTOMATED PLANT HEALTH DIAGNOSIS REVOLUTIONIZING DISEASE IDENTIFICATION THROUGH IMAGE PROCESSING TECHNIQUES.
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[Audio] This. [image]. GROUP NAME. [image]. [image].
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INTRODUCTION. PROBLEM STATEMENT. CONTRIBUTION/IMPACT.
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INTRODUCTION Hydroponic farming faces significant in efficiently and precisely identifying leaf diseases. Accurate disease identification is crucial for plant health and optimizing crop yield in controlled environments. There is a lack of research on algorithms tailored to the specific needs of hydroponic farming systems. This project aims to: 1. To use sophisticated image processing techniques for precise automated identification of plant diseases using extensive picture databases. 2. Utilize deep learning and image processing to improve disease diagnosis accuracy. 3. To increase crop yield and ensure the sustainability of hydroponic agriculture in urban areas. 4. To address the need for efficient techniques to identify leaf diseases in hydroponic plants.
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[image]. [image]. [image]. [image]. A tractor in a field Description automatically generated A person in a hat picking plants Description automatically generated A drone spraying plants with water Description automatically generated with medium confidence A hand holding a tablet with plants growing out of it Description automatically generated Hydroponic farming lacks effective, scalable, and accurate methods for detecting leaf diseases. Inefficient disease detection can negatively affect plant health and crop yields. Develop solutions using advanced image processing techniques. Enhance accuracy in disease detection and improve monitoring and management practices..
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BENEFITS:. [image]. A beaker with a leaf in it and a honeycomb in the background Description automatically generated Reduce human error in disease identification..
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[image]. [image]. A MAGIC PICTURE.
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