Expert system approach to improve the accuracy of prediction and solution of various agricultural scenarios

Abstract

The proposed research on developing expert systems for accurately predicting and recommending solutions for various agricultural scenarios is novel in several ways:  Integration of Multiple Technologies: The research involves the integration of multiple technologies such as knowledge representation, artificial intelligence and machine learning algorithms, data integration and analysis techniques, and evaluation and validation techniques to develop a comprehensive and effective expert system for agriculture. Interdisciplinary Approach: The research is an interdisciplinary approach that brings together experts from various fields such as computer science, agriculture, and data science to develop an expert system that takes into account the needs of farmers and agriculture professionals. Use of Real-World Data: The research uses real-world data to test and validate the performance of the expert system, which increases its applicability and effectiveness in practical agricultural scenarios. Customization and Personalization: The proposed expert system can be customized and personalized based on the unique needs of individual farmers and agriculture professionals, which will make it more useful and user-friendly. Potential to Enhance Agriculture Productivity and Sustainability: The development of an effective expert system for agriculture has the potential to enhance productivity and sustainability in agriculture, which will benefit not only the farmers and agriculture professionals but also the wider society by improving food security and reducing environmental impact. In summary, the proposed research on developing expert systems for accurately predicting and recommending solutions for various agricultural scenarios is a novel and interdisciplinary approach that has the potential to transform agriculture by improving productivity, sustainability, and profitability. Future research in the development of expert systems for agriculture can build on the proposed research in several ways: Integration of Internet of Things (IoT) Technology: The integration of IoT technology can provide real-time data on various parameters such as soil moisture, temperature, and humidity, which can be used to improve the accuracy of the expert system predictions and recommendations. Integration of Remote Sensing Technology: The integration of remote sensing technology such as satellite imagery can provide a broader view of agricultural landscapes and enable the expert system to predict and recommend solutions for large-scale agricultural scenarios. Development of User-Friendly Interfaces: Future research can focus on developing user-friendly interfaces that enable easy access and understanding of expert system predictions and recommendations by farmers and agriculture professionals. Use of Explainable AI Techniques: Future research can explore the use of explainable AI techniques that enable the expert system to provide explanations for its predictions and recommendations, which can improve user trust and confidence in the system. Implementation of Expert System in Developing Countries: Future research can focus on the implementation of expert systems in developing countries where smallholder farmers face significant challenges in accessing and utilizing agricultural technology. The expert system can be adapted to the local context and needs of smallholder farmers to improve their productivity and livelihoods. Ffuture research in the development of expert systems for agriculture can focus on the integration of new technologies, development of user-friendly interfaces, use of explainable AI techniques, and implementation in developing countries to improve the effectiveness and accessibility of the expert system.