Plant-Fettle-Detector

PLANT FETTLE DETECTOR - Unifying Fruit Quality Tracking and Plant Disease Detection through Advanced Image Analysis.

This research presents an innovative integration of Convolutional Neural Networks (CNNs) to address key challenges in agriculture—fruit quality tracking and plant disease detection. The unified system comprises the Fruit Quality Tracker and the Plant Disease Detector, leveraging advanced image analysis for precision agriculture. The Fruit Quality Tracker uses computer vision to assess ripeness and defects in real-time, empowering farmers with informed decisions. Simultaneously, the Plant Disease Detector swiftly identifies and classifies diseases, mitigating economic losses.

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Developed a Convolutional Neural Network (CNN) model to identify diseases and assess the quality of apples and oranges, enhancing agricultural quality control processes. Extracted and preprocessed an extensive image dataset, implementing data augmentation techniques to improve model accuracy and generalizability. Achieved 98% accuracy in disease detection and quality assessment, utilizing Python and TensorFlow/Keras. Engineered a user-friendly web application for real-time image analysis, allowing users to upload photos for instant evaluation of fruit health. Utilized HTML, CSS, and JavaScript for frontend development, and [Flask/Django/other] for backend integration of the machine learning model. Demonstrated potential for scalability by successfully focusing on two fruit types, with the capability of extending the model to additional categories.

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Research Paper:

Link : Plant Fettle Detector: Unifying Fruit Quality Tracking and Plant Disease Detection through Advanced Image Analysis