This project introduces an innovative approach to plant disease detection using machine learning, specifically convolutional neural networks (CNNs). By analyzing digital images of plant leaves and incorporating environmental factors, the project can accurately detect diseases early on, promoting sustainable agriculture. The machine learning models are trained on a diverse dataset, ensuring adaptability across different crops and diseases. The project’s user-friendly interface allows farmers to receive real-time feedback, empowering them with actionable insights for effective crop management. Overall, this automated plant disease detection project aims to enhance crop productivity, reduce losses, and strengthen agricultural resilience.

• Convolutional Neural Networks
• Image Processing

The rapid advancement in color printing technology has led to a surge in counterfeit currency production, undermining the authenticity of legal tender in India. To address this issue, we have developed a Python-based system that utilizes image processing techniques. This system evaluates various features of Indian currency notes to determine their authenticity. Through processes like grayscale conversion and edge detection, it provides a straightforward and high-performance solution for distinguishing real currency from counterfeits, thus aiding in the fight against fraudulent currency circulation.

• Currency Authentication
• Image Processing

Underwater images often suffer degradation due to factors like light scattering, absorption, and reflection, causing reduced visibility and color distortion. To enhance image quality, It  introduces a two-stage dehazing method. estimate the transmission map using the dark channel prior, effectively measuring haze thickness. Then, color correction is applied to restore color balance. By employing a color transfer function and selecting a reference image based on content and lighting similarity, it improve the visual quality and quantitative metrics of contrast and colorfulness in underwater image dehazing, outperforming existing methods.

• Image Processing
• Image Quality Enhancement
• Reference Image Selection

A method for diabetic retinopathy detection through image processing. Retinal images are enhanced, relevant features extracted via image processing, and a machine learning algorithm trained for classification. The system’s potential benefits include enhanced accuracy and accessibility in healthcare.

• Diabetic retinopathy
• Image Processing
• Machine learning-based diagnosis

A novel technique creates a mosaic image from a secret image, making it appear like a chosen target image. Skilled color transformation ensures nearly lossless secret image recovery. Overflows/underflows are managed by recording color differences in the original space. Embedded information allows lossless secret image retrieval, validated through successful experiments.

• Image Processing
• Information Retrieval
• Steganography