Impact of Climate Change on Crop Yield: Studying how climate change affects the growth, development, yield, and quality of crops such as cotton, and how to use big data and machine learning technologies to predict and adapt to these impacts. Smart Agriculture: Developing smart agricultural solutions using sensors, remote sensing technology, and big data analysis to help farmers optimize agricultural management practices, increase crop yield, and improve resource utilization efficiency. Precision Agriculture: Implementing precision management of farmland based on big data and machine learning algorithms, including fertilization, irrigation, pest and disease monitoring, and control, to reduce the use of pesticides and fertilizers and minimize environmental pollution. Crop Genetic Improvement: Integrating genomics and big data analysis technologies to study the genetic characteristics of crops, accelerate the breeding process, and cultivate new varieties that are adapted to climate change and resistant to pests and diseases. Establishment and Optimization of Prediction Models: Utilizing machine learning and deep learning technologies to establish crop growth models and yield prediction models, and continuously optimizing and improving their accuracy and reliability.