CNN-Based Plant Disease Recognition System
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
NHCE
Abstract
The advancement of technology in agriculture has introduced innovative methods for crop management, such as leaf detection and analysis using Python. By leveraging machine learning and computer vision techniques, Python enables the identification and classification of leaf features like shape, size, and colour. Tools like OpenCV for image processing and TensorFlow or PyTorch for model training provide a robust framework for accurate and efficient leaf detection.
This technology has transformative applications, including early disease detection, plant health monitoring, and resource optimization. By analysing visual indicators, farmers can identify abnormalities, track stress levels, and allocate water, fertilizers, or pesticides more effectively. Integration with loT devices and drones further enhances data collection and real-time insights, advancing precision agriculture.
Python-based leaf detection systems mark a major shift toward data-driven farming practices. These tools empower farmers with actionable insights, improving crop yield, reducing waste, and promoting sustainable agriculture. As these technologies evolve, they hold immense potential to address global food security challenges and foster environmentally friendly farming methods.