Review of Smart Agricultural Technologies for Precision Pest Control and Sustainable Water in Cotton Farming

Cotton production is extremely susceptible to pests, disease, and irrigation mismanagement, resulting in considerable yield loss and environmental degradation. Conventional practices like manual monitoring of pests and blanket spraying of pesticides are time-consuming, ecologically detrimental, and frequently inadequate in averting timely infestations. The intersection of Artificial Intelligence (AI), the Internet of Things (IoT), and smart water management offers a revolutionary chance towards sustainable agriculture. This paper discusses the cooperative function of IoT and AI in cotton cultivation, particularly real-time pest identification, disease classification, and dynamic irrigation management. Deep learning techniques precisely detect pests at all stages of development, whereas IoT sensors track environmental data like soil moisture, temperature, and humidity. The sensor data are used as input to machine learning algorithms that adjust irrigation schedules and predict outbreaks of disease. Furthermore, adaptive learning methods such as federated and incremental learning optimize system performance through time, allowing for ongoing improvement without violating data privacy. Major findings indicate that the integration of these technologies decreases pesticide use, saves water, and enhances crop resistance. Yet, the absence of complete integrated systems prevents the full potential from being realized. This research highlights the necessity for integrated frameworks that address pest management and water management simultaneously in real time. Closing these gaps will open the door to innovations that increase yield, enhance sustainable agriculture practices, and make cotton farming sustainable in the long term.