New technologies solve complex problems in agricultural production, enabling both food security and the optimization of the use of natural resources. In this paper, we will cover hydroponic agriculture, advanced monitoring, and data analysis. Plants interact with light, humidity, and temperature environmental factors. Light is one of the most important environmental factors for photosynthesis and growth. Various wavelengths have contrasting impacts on plant development and physiological traits. Artificial manipulation of light conditions may allow for an improvement in crop production and quality. Thus, the present study deals with the influence of diverse light wavelengths on the growth of strawberry plants cultivated in a hydroponic system. The growing processes of strawberry plants (Fragaria × ananassa) have been taken into consideration due to their importance as food and the sensitivity of their cultivation. It tests the influence of specific wavelengths on plant growth and development. State-of-the-art technologies include Arduino-based temperature and light sensors to monitor the cultivation conditions in real-time, while Convolutional Neural Networks trace the growth patterns and pests of the crops by images taken. This work models and predicts behaviors of plants under different light conditions using Machine Learning techniques, thus optimizing cultivar development with a view of maximum yield production. The results obtained show that red light promotes growth through enhanced flower and fruit development. Blue light is favored by robust leaf and stem growth since it is most effective in photosynthesis. Green lights, which help inner light penetration inside the leaf canopy, have less of an effect on photosynthesis. Yellow light also has some advantages in general growth but is inefficient compared with blue and red light. Result using CNN architecture, accuracy 89%. This work contributes to precision agriculture, sensor technology, and sustainable farming practices.