Textbook in PDF format

Decision support systems are developed for integrated pest and disease management and nutrition management using open-source technologies as Java, Android, and low-cost hardware devices like Arduino micro controller. This text discusses the techniques to convert agricultural knowledge in the context of ontology and assist grape growers by providing this knowledge through decision support system.
Many solutions are available for sensing environmental parameters. For using environmental readings for decision support, sensors need to be boarded on fields and be connected to computerized systems for measurement recording and manipulation. Arduino is an open- source platform for implementing wireless sensor networks. Using Arduino microcontroller, sensor inputs are read and sent to server using some connectivity technique like Wi-Fi, Zigbee or GPRS. Programs can be written to read inputs and to write outputs to connected devices using Arduino IDE. Instructions can be sent on microcontrollers of Arduino kit to get the work done. ESP8266 is a Wi-Fi module which can be used for transferring sensor outputs to server machines through GPRS or Zigbee. It can be mounted on Arduino microcontroller and programmed using AT commands.
Apache Jena is a free and open source library available to work with semantic data. It is available in Java language. For this research, APIs available for manipulation of ontologies are used. With Jena API, one can create, read and modify RDF documents. Querying to RDF is also possible using ARQ engine which supports execution of SPARQL queries on RDF. Inference API is also available to reason over RDF document. Pellet reasoner can also be used with Jena.
Open source computer vision library has so many image processing functions. It supports many languages including C, C++, Python and Java. Java language is used in current research. Functions for all steps in image processing like image reading, smoothing, noise removal, thresholding and classification/clustering are supported by OpenCV. Accuracy of disease detection using image processing technique was tested for all three diseases.
The key features of the book are:
Presents the design & development of an ontology-based decision support system for integrated crop management.
Discusses the techniques to convert agricultural knowledge in text to ontology.
Focuses on an extensive study of various e-Negotiation protocols for automated negotiations
Provides an architecture for predicting the opponent’s behaviour and various factors which affect the process of negotiation.
The text is primarily written for graduate students, professionals, and academic researchers working in the fields of Computer Science and engineering, agricultural science, and information technology.
Preface
Part 1 Decision Support System
1. Decision Support System for Agriculture
2. Ontology Development
3. Building Vineyards Knowledge Base
4. Knowledge Bases: Making It All Together
5. PDMGrapes: Forecasting Occurrence of Pests on Grapes
6. Nutrient Management
7. Irrigation Management
8. Crop Suitability Recommendation
Part 2 Automated Negotiation
9. Negotiation
10. Literature Survey
11. Problem Statement and Scope
12. Methodology
13. Results and Analysis
14. Conclusion