Andere Kunden interessierten sich auch für
![Hybrid Deep Learning Model for Wheat Yellow Rust Disease Detection]( "Hybrid Deep Learning Model for Wheat Yellow Rust Disease Detection")
Hybrid Deep Learning Model for Wheat Yellow Rust Disease Detection26,99 €![Erkennung der Reis-Hispa-Krankheit mithilfe eines Deep-Learning-Modells]( "Erkennung der Reis-Hispa-Krankheit mithilfe eines Deep-Learning-Modells")
Erkennung der Reis-Hispa-Krankheit mithilfe eines Deep-Learning-Modells39,90 €![DETECTION OF AUTISM DISEASE USING MACHINE LEARNING ALGORITHMS]( "DETECTION OF AUTISM DISEASE USING MACHINE LEARNING ALGORITHMS")
DETECTION OF AUTISM DISEASE USING MACHINE LEARNING ALGORITHMS26,99 €![Rice Plant Diseases Detection Using IoT & Machine Learning]( "Rice Plant Diseases Detection Using IoT & Machine Learning")
Rice Plant Diseases Detection Using IoT & Machine Learning26,99 €![Image classification using Deep Learning & Swarm Intelligence]( "Image classification using Deep Learning & Swarm Intelligence")
Image classification using Deep Learning & Swarm Intelligence36,99 €![Automatic Medical Disease Detection System Using Data Mining]( "Automatic Medical Disease Detection System Using Data Mining")
Automatic Medical Disease Detection System Using Data Mining26,99 €![Deep Learning and Machine Learning for Medical Diagnosis]( "Deep Learning and Machine Learning for Medical Diagnosis")
Deep Learning and Machine Learning for Medical Diagnosis29,99 €
Agriculture is the most important element of the globe, and large-scale agricultural operations around the world make it more susceptible to numerous diseases. Rice is one of the most important agricultural plants cultivated in enormous quantities. There are a variety of rice illnesses that impact rice crop plantations in various ways, and detecting and recognising them is one of the most difficult tasks. An endeavour has been initiated to use deep learning to recognise rice hispa illness. In order to carry out the experimental work with a real-time dataset of rice hispa and healthy rice crop plant, a CNN-based deep learning approach was used. The detection of rice hispa disease was divided into two parts: the first was a binary classification based on healthy and sick plants, and the second was a multi-classification based on five severity levels of the disease. The suggested architecture and model serves as a rice disease detection (RDD) system for rice hispa disease, assistingfarmers and cultivators in recognising and detecting rice crop plants and taking appropriate and timely action.