Utilizing Convolutional Neural Networks for Image-Based Crop Classification System
Keywords:
CNN, Plant disease, Disease Prediction, Crop Classification, AccuracyAbstract
The new method for classifying crops using a Convolutional Neural Network (CNN)-based system is presented in-depth in this proposal. The increasing demand for efficient agricultural practices calls for automated methods to classify and monitor crop types. The proposed system leverages the power of CNNs to accurately classify crops based on images. It discusses the architecture and training process of the CNN model, highlighting its ability to extract meaningful features from crop images. By employing advanced deep learning techniques, the system achieves high classification accuracy, surpassing traditional methods. It presents the image-based crop classification system that utilizes CNNs. The system aims to overcome the limitations of manual classification methods by automating the process and improving the accuracy of crop identification. By feeding crop images into the CNN model, it can extract discriminative features and enable the system to make reliable predictions’ have completely changed the field of computer vision and have excelled in many different image identification tasks. They are highly suited for analysing cropped photos because of their automated learning capabilities and capacity to extract useful features from unprocessed input data. A CNN can be trained to identify between various crop types based on the visual properties of a huge collection of labelled crop photos. Additionally, it explores the potential applications and benefits of the image-based crop classification system, including improved crop management, yield prediction, and resource optimization. Through extensive experimentation and evaluation, it validates the effectiveness and reliability of our CNN-based approach, making it a promising solution for the agricultural industry.
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