• Kirana G D
• Keerthana S
• Gowthama Sharma M
• Bhoomika N P
• Nithin H V

Hydroponics is a soil-less method of growing plants that involves the use of nutrient-rich water solutions. This technique has gained popularity due to its ability to produce high-quality crops in a controlled environment. The use of hydroponic farming systems has gained significant attention in recent years as a sustainable and efficient way of growing crops. This paper presents a smart hydroponic system that utilizes sensors, actuators, and machine learning algorithms to optimize plant growth and resource usage, and includes a disease detection system. The system is designed to monitor and adjust critical environmental variables, such as temperature, humidity, pH, and nutrient levels, in real-time to ensure the best possible growing conditions for plants. These systems can be susceptible to plant diseases and suboptimal nutrient conditions, which can result in lower crop yield and quality. To address these challenges, we propose a smart nutrition prediction and disease detection system for hydroponic farms, which uses image recognition algorithms like VGG19 and machine learning algorithms like Random Forest Regressor to predict optimal nutrient delivery and detect plant diseases. The system can analyze images of plant leaves and detect signs of disease or stress using VGG19, and then use Random Forest Regressor to predict the optimal nutrient solution composition and delivery for plants. The system can help to maximize yield and improve crop quality while reducing losses due to diseases and suboptimal nutrient conditions. The proposed system has a wide range of applications in commercial hydroponic farming, research, education, and urban agriculture. This system has the potential to create a more sustainable and efficient food system for the future.

Hydroponics, machine learning, VGG19, Random Forest Regression

"Nutrition Prediction and Disease Detection in Hydroponics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.f67-f69, May-2023, Available :http://www.jetir.org/papers/JETIR2305512.pdf

"Nutrition Prediction and Disease Detection in Hydroponics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppf67-f69, May-2023, Available at : http://www.jetir.org/papers/JETIR2305512.pdf