Precision agriculture transform the agricultural sector by integrating advanced technologies to enhance productivity and sustainability. In crop farming, precision agriculture can significantly improve practices through precise monitoring and data-driven decision-making, addressing challenges such as optimizing resource usage and improving crop health. This study presents the development and implementation of an IoT-based Crop Recommendation System designed to optimize farming practices through a mobile application. This system uses different sensors to continuously extract data regarding the temperature, pH, NPK value and other relevant parameters. These parameters can be analyzed in real-time to help farmers make informed decisions on irrigation, fertilization, and crop selection, tailored to specific field conditions. This information is stored to create individual datasets, offering researchers valuable insights into optimal conditions for various crops. This can improve yield and promote sustainable farming practices. In this study, we evaluated a series of machine learning algorithms for their ability to predict an optimal crop based on environmental parameters. Among these algorithms, Naive Bayes demonstrated superior performance, achieving an accuracy of 99.55%, precision of 99.58%, recall of 99.55%, and F1-score of 99.54%. These findings highlight the effectiveness of our approach in integrating machine learning with the IoT for precise crop management. Implemented through a user-friendly mobile application, the proposed system enhances accessibility and usability for farmers.

Precision agriculture is revolutionizing the agricultural sector by integrating advanced technologies to enhance productivity and sustainability. In aquaculture, precision agriculture can significantly improve fish farming practices through precise monitoring and data-driven decision-making, addressing challenges such as optimizing resource usage and improving fish health. This paper presents the development and implementation of an IoT-based Fish Recommendation System designed to optimize aquaculture practices through a mobile application. This system uses different sensors for extracting data continuously regarding temperature, PH and Turbidity etc. These parameters can be analysed in real-time to help fish farmers make decisions on when or how much the system should feed and aerate, and what approach of water treatment is best for their fishes. This information is stored to create individual datasets, offering researchers valuable insights into optimal conditions for each fish species. This can enhance their survival rates and promote growth. In this study, we evaluate a series of machine learning algorithms for their ability to predict the optimal fish species based on water quality parameters. Among these algorithms, Random Forest demonstrated superior performance, achieving an accuracy of 92.5%, precision of 93%, recall of 93%, and F1-score of 92%. These findings highlight the effectiveness of our approach in integrating machine learning with IoT for precise aquaculture management. Implemented through a user-friendly mobile application, our system enhances accessibility and usability for fish farmers.