Titre : | Mobile Application based on Deep Learning for Detecting plant Diseases |
Auteurs : | ASMA KHELFA, Auteur ; Rachida Saouli, Directeur de thèse |
Type de document : | Monographie imprimée |
Editeur : | Biskra [Algérie] : Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie, Université Mohamed Khider, 2020 |
Format : | 1 vol. (37 p.) / ill. / 29 cm |
Langues: | Anglais |
Résumé : | Since man invented agriculture, plant disease epidemics have been a major challenge for crop growers. An accurate and a faster detection of this diseases could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems, which makes this technique adopted successfully to diagnostic plant diseases. In this work, A computer vision approach is proposed to identify the disease by capturing the leaf images and detect the possibility of the diseases. We develop a mobile application used a deep learning architecture, which is MobileNet to detect five types of tomato diseases. The proposed system is tested on 1400 images from PlantVillage dataset. The results show that MobileNet is able to detect the disease up to more than 970/0 accuracy. |
Sommaire : |
1 General introduction 1
2 State of the art 3 2.1 Introduction 3 2.2 Machine Learning Methods 3 2.2.1 SVM (Support Vector Machine) 5 2.2.2 Artificial neural networks 6 2.3 Deep Learning architectures 8 2.4 Related works 17 2.5 Synthesis 18 2.6 Conclusion 19 3 Convolutional Neural Network for Tomato Diseases Detection and Implementation 20 3.1 Introduction 20 3.2 General Architecture 20 3.3 Needs and conception details. 22 3.3.1 Needs details 22 3.3.2 Conception details 23 3.4 Implementation details 24 3.4.1 Work environments 24 3.4.2 Development languages 26 3.4.3 MobileNet training 27 3.5 Discussion 31 3.6 Conclusion 32 4 General Conclusion 3 |
Type de document : | Mémoire master |
Disponibilité (1)
Cote | Support | Localisation | Statut |
---|---|---|---|
MINF/575 | Mémoire master | bibliothèque sciences exactes | Consultable |