| Title |
Intellifarmassist – A Novel Machine Learning Integrated Genetic Algorithm Based Optimal Crop Recommendation System |
| ID_Doc |
32251 |
| Authors |
Mala D.J.; Basak S.; Reynold A.P. |
| Year |
2024 |
| Published |
Lecture Notes in Networks and Systems, 1051 LNNS |
| DOI |
http://dx.doi.org/10.1007/978-3-031-64850-2_5 |
| Abstract |
Crop recommendation is a critical task for agricultural productivity, and weather and soil conditions play a significant role in determining the suitable crop to grow. In this research, an intelligence assisted approach is used to develop a crop recommendation system that takes into account the weather and soil content of a particular region. The system uses weather and soil data to create objects that represent the conditions, and then applies a set of rules used as the fitness criteria in the proposed Genetic Algorithm to determine the optimal crop for the given conditions. The approach allows for flexible and modular design, making it easy to modify and extend the system. The results demonstrate the effectiveness of the proposed system in providing accurate and timely crop recommendations, which can help farmers to make informed decisions and improve their yield. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
| Author Keywords |
Crop Recommendation; Fitness Criteria; Generic Algorithm; Intelligent Assistant; Machine Learning; Optimal Crop; Smart Cities |
