DELVING INTO ACCURATE LIGHT INTENSITY ANALYSIS ACROSS DIVERSE PARAMETERS IN HYDROPONIC SETTINGS USING MACHINE LEARNING

The study focuses on hydroponic gardens, which are highly regarded for their sustainability and precision. on important growth characteristics such as light intensity, temperature, pH level, plant height, leaf weight, number of leaves, stem growth, and light quality using four distinct colours of LED light (red, white, green, and blue). Analyzing the effect Oneness is ensured by stringent control with LEDs set for continuous intensity and extended exposure times. Monitoring the nutrition solution's steady pH and temperature improves accuracy. The development of hydroponic plants is affected differently by the hues of LEDs, according to preliminary studies. While blue light increases leaf weight and compactness, red light encourages vertical development, and diffused white light establishes equilibrium, green light has the ability to adjust pH levels. More significant than energy is spectral organization. Convolutional neural networks (CNNs), principal component analysis (PCA), clustering    algorithms, predictive modelling, and other methods related to deep learning and machine learning are still in their infancy, according to research researchers. By fostering sustainable agricultural practices, these contribute to the goals of food security and environmental preservation by offering a deeper understanding of the intricate relationship between LED colour and plant growth. This understanding facilitates the development of specialized agricultural techniques that boost energy and productivity.