Crop prediction and fertilizer reccomendation

computer engineering

Agriculture plays a vital role in India. India is the world's largest producer of different crops but still, it uses traditional framing methods therefore crop yield becomes down. Hence, with the introduction of newer seed varieties, new methods of agriculture crop production have increased. But without using the smarter ways, the agricultural field still having an imperfection. And due to these farmers need a smarter way to increase crop production. Hence, to maximize the crop yield some smart methods came into the picture used in IoT and Machine Learning. In this paper, we will review the algorithms like Random Forest, Decision Tree, ANN to get better accuracy for the system. The core components of this system include a network of IoT sensors and actuators, data analytics, machine learning algorithms, and a user-friendly interface. IoT sensors are strategically deployed in the fields to continuously measure crucial parameters such as soil moisture, temperature, humidity, and light intensity. This real-time data is transmitted to a centralized database, which is then processed and analyzed using advanced machine learning algorithms. The system utilizes machine learning models to provide accurate crop yield predictions based on historical data, current conditions, and crop-specific characteristics. By considering factors like soil quality, weather conditions, and crop type, the system generates optimal fertilizer recommendations. These recommendations aim to optimize nutrient supply, reduce over-fertilization, and minimize environmental impact. Farmers and agricultural professionals can access the system through a user-friendly interface, such as a mobile application or web platform. Here, they can receive personalized recommendations for fertilization and access real-time data on their crops' health and environmental conditions. This not only enhances crop yield but also streamlines decision-making processes for farmers, reducing costs and environmental harm.

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