Жанры
Авторы
Контакты
О сайте
Книжные новинки
Популярные книги
Найти
Главная
Авторы
Группа авторов
The Digital Agricultural Revolution
Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 1
Оглавление
Предыдущая
Следующая
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
...
66
Оглавление
Купить и скачать книгу
Вернуться на страницу книги The Digital Agricultural Revolution
Оглавление
Страница 1
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Страница 7
Страница 8
Страница 9
Страница 10
Страница 11
1
Scope and Recent Trends of Artificial Intelligence in Indian Agriculture
1.1 Introduction
1.2 Different Forms of AI
1.3 Different Technologies in AI
1.3.1 Machine Learning
1.3.1.1 Data Pre-processing
1.3.1.2 Feature Extraction
1.3.1.3 Working With Data Sets
1.3.1.4 Model Development
i. Support Vector Machine
ii. Regression Algorithm
iii. Decision Tree
iv. K-means Clustering
v. Association Algorithm
1.3.1.5 Improving the Model With New Data
1.3.2 Artificial Neural Network
1.3.2.1 ANN in Agriculture
1.3.3 Deep Learning for Smart Agriculture
1.3.3.1 Data Pre-processing
1.3.3.2 Data Augmentation
1.3.3.3 Different DL Models
1.4 AI With Big Data and Internet of Things
1.5 AI in the Lifecycle of the Agricultural Process
1.5.1
Improving Crop Sowing and Productivity
1.5.2
Soil Health Monitoring
1.5.3
Weed and Pest Control
1.5.4 Water Management
1.5.5
Crop Harvesting
1.6 Indian Agriculture and Smart Farming
1.6.1
Sensors for Smart Farming
1.7 Advantages of Using AI in Agriculture
1.8 Role of AI in Indian Agriculture
1.9 Case Study in Plant Disease Identification Using AI Technology—Tomato and Potato Crops
1.10 Challenges in AI
1.11 Conclusion
References
Страница 48
2
Comparative Evaluation of Neural Networks in Crop Yield Prediction of Paddy and Sugarcane Crop
2.1 Introduction
2.2 Introduction to Artificial Neural Networks 2.2.1 Overview of Artificial Neural Networks
2.2.2 Components of Neural Networks
2.2.3 Types and Suitability of Neural Networks
2.3 Application of Neural Networks in Agriculture 2.3.1 Potential Applications of Neural Networks in Agriculture
2.3.2 Significance of Neural Networks in Crop Yield Prediction
2.4 Importance of Remote Sensing in Crop Yield Estimation
2.5 Derivation of Crop-Sensitive Parameters From Remote Sensing for Paddy and Sugarcane Crops 2.5.1 Study Area
2.5.2 Materials and Methods
2.5.2.1 Data Acquisition and Crop Parameters Retrieval From Remote Sensing Images
2.5.3 Results and Conclusions
2.6 Neural Network Model Development, Calibration and Validation 2.6.1 Materials and Methods
2.6.1.1 ANN Model Design
2.6.1.2 Model Training
2.6.1.3 Model Validation
2.6.2 Results and Conclusions
2.7 Conclusion
References
{buyButton}
Подняться наверх
