Early Detection of Disease in Tomatoes | Using Artificial Intelligence and Machine Learning

Azeezat Ojoogun

ISBN 10: 6208011868 / ISBN 13: 9786208011864

Published by LAP LAMBERT Academic Publishing, 2024

About this Item

Early Detection of Disease in Tomatoes | Using Artificial Intelligence and Machine Learning | Azeezat Ojoogun | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786208011864 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 130099195

Bibliographic Details

• Title: Early Detection of Disease in Tomatoes | Using Artificial Intelligence and Machine Learning
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2024
• Language: English
• ISBN 10: 6208011868
• ISBN 13: 9786208011864
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 96 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.