Tech-Enhanced Forest Nursery Management: Harnessing Embedded Systems for Plant Health Monitoring and Growth Forecasting

This study explores IoT-based forest nursery monitoring using Sony Picam 2 and Raspberry Pi B2 (8 GB RAM). The primary objective revolves around the measurement and continuous monitoring of plant leaf color, a crucial indicator of plant health and nutrient status. To accomplish this, a Python program has been developed to systematically monitor and analyze variations in plant leaf color within the same species, and even within individual plants. Additionally, the study extends its focus to nutrient deficiencies, encompassing essential elements like N, P, K, and 13 micronutrients, shedding light on the critical aspect of plant nutrition in nursery settings. To further enrich our comprehension of forest sapling growth, a growth model incorporating deep learning techniques has been meticulously designed. The development and refinement of this model were greatly facilitated by Google CoLab. The next phase of the research will involve thorough field testing of the developed model at a nursery in Bhubaneswar. This testing will include a variety of plant species. The primary objectives are to evaluate its accuracy and effectiveness in real-world scenarios and to explore its scalability for application in larger fields. This interdisciplinary study seamlessly bridges the realms of technology, biology, and agriculture, culminating in the creation of a robust system for efficient forest nursery management and early detection of plant health issues. By doing so, this research contributes significantly to the sustainable growth and conservation of forest ecosystems, ensuring their long-term vitality and resilience.