Cotton Genomics and Genetics 2025, Vol.16, No.2, 48-56 http://cropscipublisher.com/index.php/cgg 50 support systems to enhance agricultural practices. For instance, a study highlights the use of UAS to capture RGB data for developing a Digital Twin framework, which forecasts cotton crop features such as canopy cover and height, thereby aiding in yield prediction and biomass estimation (Pal et al., 2019). Additionally, the integration of diverse data sources, including historical weather data and soil nutrient analysis, enables personalized recommendations for farmers, enhancing decision-making and risk management (Singh et al., 2024). 3.2 Machine learning and predictive modeling Machine learning (ML) plays a pivotal role in precision agriculture by analyzing complex datasets to predict crop yields and optimize farming practices. Various ML models, such as random forests, XGboost, and artificial neural networks, have been employed to predict cotton yield and determine the impact of management and environmental variables (Dhaliwal et al., 2022). These models facilitate informed decision-making by predicting suitable crops, detecting diseases, and optimizing irrigation (Mohyuddin et al., 2024). Moreover, ML techniques, including support vector regression and ensemble methods, have been used to enhance prediction accuracy and decision-making capabilities, contributing to sustainable farming practices (Bachu et al., 2024). 3.3 DSS tools for cotton farmers Decision support systems (DSS) are essential tools for cotton farmers, providing insights into irrigation scheduling, crop management, and yield optimization. For example, an irrigation DSS based on forecasted rainfall and water stress indices has been shown to significantly increase cotton yield and water productivity in arid climates (Chen et al., 2020). Furthermore, IoT-based DSS frameworks integrate multiple soil and environmental parameters to predict soil moisture content and optimize irrigation control schemes, ensuring efficient water use and maintaining uniform moisture levels across fields (Keswani et al., 2020). These tools empower farmers to make data-driven decisions, ultimately enhancing crop yield and resource efficiency. 4 Impact of Precision Agriculture on Cotton Yield and Sustainability 4.1 Yield enhancement through site-specific management Precision agriculture significantly enhances cotton yield through site-specific management techniques. By utilizing technologies such as GPS, IoT sensors, and variable rate technology (VRT), farmers can apply inputs precisely where needed, optimizing crop yield. For instance, precision nitrogen management in Bt cotton has shown to improve seed cotton yield by aligning nitrogen application with crop demand, thereby enhancing nitrogen use efficiency (Gupta et al., 2022). Additionally, precision agriculture practices have demonstrated a 20% increase in crop yield by addressing inter- and intravariability in cropping systems. 4.2 Efficient use of resources and input cost reduction Precision agriculture contributes to the efficient use of resources and reduction of input costs by minimizing waste and optimizing input application. For example, precision nitrogen management not only improves yield but also reduces nitrous oxide emissions, showcasing a dual benefit of resource efficiency and environmental protection. Moreover, precision agriculture has been shown to reduce water and fertilizer usage by 40%, leading to significant cost savings. The use of site-specific management strategies also results in economic benefits through cost savings and increased profits, as evidenced by studies on various crops (Bahmutsky et al., 2024). 4.3 Environmental and ecological benefits The environmental and ecological benefits of precision agriculture are substantial. By reducing the overuse of fertilizers and pesticides, precision agriculture minimizes environmental impacts such as greenhouse gas emissions and pesticide runoff. Precision farming techniques, such as site-specific sensing and management, allow for targeted input use, reducing agrichemical residuals and promoting environmental sustainability (Finger et al., 2019). Additionally, precision agriculture practices have been shown to decrease greenhouse gas emissions by 14% in sugarcane production, highlighting their potential for broader ecological benefits (Sanches et al., 2023). 5 Challenges and Limitations in Adoption 5.1 Economic and infrastructure barriers The adoption of precision agriculture technologies (PATs) is often hindered by significant economic and infrastructure barriers. High initial costs and the need for substantial investments in technology infrastructure are
RkJQdWJsaXNoZXIy MjQ4ODYzNA==