Isbn: 9786208011864 - early detection of disease in tomatoes: using artificial intelligence and machine learning

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Plant diseases and pests are a serious problem in agriculture. Accurate and faster detection of diseases and pests in plants could aid in the development of an early treatment method while significantly lowering economic losses. In this regard, the introduction of deep learning-based image classification has resulted in a significant number of solutions. The primary goal of this research is to detect tomato plant leaf illnesses more quickly by adopting Deep Learning (DL), which can be efficiently utilised for image classification utilising various convolutional neural network (CNN) architectures including VGG-16, ResNet34 and EfficentNet0.Some pre-processing techniques were done on the data before concluding data preparation. An imbalance among the classes was discovered in the dataset, which was remedied using weighted random sample techniques to avoid bias. Model optimization was done to improve perform of the model.The result of the predictions is embedded in an interactive GUI. The GUI allows shows the disease detected. The user may just put their own image for it to detect the class of disease.…

Early Detection of Disease in Tomatoes

Azeezat Ojoogun

Lingua: Inglese

Editore: LAP LAMBERT Academic Publishing Aug 2024, 2024

6208011868 / 9786208011864

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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

Venditore con 5 stelle

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Condizione: Nuovo

EUR 43,90

EUR 60,00 spedizione

Spedito da Germania a U.S.A.

Quantità: 1 disponibile

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Plant diseases and pests are a serious problem in agriculture. Accurate and faster detection of diseases and pests in plants could aid in the development of an early treatment method while significantly lowering economic losses. In this regard, the introduction of deep learning-based image classification has resulted in a significant number of solutions. The primary goal of this research is to detect tomato plant leaf illnesses more quickly by adopting Deep Learning (DL), which can be efficiently utilised for image classification utilising various convolutional neural network (CNN) architectures including VGG-16, ResNet34 and EfficentNet0.Some pre-processing techniques were done on the data before concluding data preparation. An imbalance among the classes was discovered in the dataset, which was remedied using weighted random sample techniques to avoid bias. Model optimization was done to improve perform of the model.The result of the predictions is embedded in an interactive GUI. The GUI allows shows the disease detected. The user may just put their own image for it to detect the class of disease.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch.…

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