Begin with existing knowledge then

Introduce the gap of your topic

Explain why the gap is matter

State the research problems or aim clearly

Hint at the contribution

A major problem in smart agriculture is the lack of timely and accurate decision-making support for farmers in managing crops, pests, and resources. Traditional methods of pest and disease identification, irrigation scheduling, and yield prediction often rely on manual observation and experience, which are time-consuming, labor-intensive, and prone to error. This results in yield losses, inefficient use of water and fertilizers, and higher production costs. Artificial intelligence can address these challenges by analyzing large datasets from sensors, drones, and satellites to provide real-time insights, predictive models, and automated recommendations. However, the gap lies in developing cost-effective, user-friendly AI solutions that are accessible to small and medium-scale farmers, making the integration of AI into smart agriculture both a technological and socio-economic challenge.