Climate-Based AI-Powered Precision Irrigation: Sustainably Smart Agriculture Frameworks for Maximum Crop Yields

A digital transformation is necessary for agriculture to achieve sustainability and increase productivity. With the use of IoT, high-tech detectors and helicopters can monitor crop health and environmental factors in real time, transmitting crucial data to cloud-based systems. Due to their robust computational and storage capabilities, these systems are capable of handling large datasets and carrying out intricate data analytics. Even before the COVID-19 pandemic, the world’s food supply was precariously balanced and insufficient. The present agricultural system prioritizes short-term gains above the preservation of the earth and its ecosystems. To construct agricultural systems that can meet the needs of ten billion individuals in the next 30 years, significant structural and automated changes are required. However, with the support of intelligent technology and the progress of artificial intelligence in agriculture, these obstacles might be overcome. Integration of data, processing images, and neural networks are examples of artificial intelligence technologies that have allowed producers to make better choices using up-to-date information and prediction models. The study emphasizes the need to address the ethical and practical concerns associated with climate-based AI in agriculture to promote its equitable deployment. The results of this research contribute to the ongoing debate about artificial intelligence’s possible applications in farming. New agricultural technologies provide optimism for a future that is environmentally friendly and will be able to feed the growing global population. The integration of renewable energy sources is one of the areas where artificial intelligence is seen to be able to assist in reaching global sustainability goals. Artificial intelligence (AI) has the potential to revitalize both newly developed and existing agricultural land by modifying, deploying, and implementing automated systems and tools. We take stock of where AI is at in the agricultural sector and shine a light on its most promising recent applications in this article. Unpredictable conditions are not well-suited to these models because of their weak learning capabilities. An intelligent irrigation system for precision farming based on deep learning neural networks and facilitated by the Internet of Things is proposed in this research. No matter the time of year or the region’s weather, this feedback-integrated system continues to work admirably. To forecast the volume of moisture content of the soil for the next day, the irrigation time, and the distribution of water needed to irrigate the cropland, DLISA employs a short-term long-term memory network.