Feasibility of Integrated Ann Model for Agriculture Farming: A Systematic Review Paper

Artificial Neural Networks (ANNs) have surfaced as a potent transformative influence in smart agriculture, significantly improving soil, crop, irrigation, disease, pest, weed management, and decision support systems. ANNs excel at recognizing non-linearity and intricate relationships among variables such as soil nutrients, soil temperature, whether temperature and leaf characteristics help to detect and predict crop health. Crop suitability concerning soil characteristics, crop yields, soil fertility level, irrigation and fertilizer time tables, weed and disease prediction are the other areas where these models find immense usage.. 　　Combining ANN models with cutting-edge remote sensing technologies, like UAVs fitted with high-resolution cameras, spectral sensors, and IoT devices, enhances their predictive precision. This results in more reliable outcomes for farmers and helps to minimize all pre-harvesting losses.Through continuous research and development, ANNs were set to take on an increasingly crucial role in revolutionizing agricultural methods, boosting efficiency, and improving productivity for a sustainable future.ANN models in farming is aligned with SDGs established under national agriculture mission of our nation for 2021-2025, Tamil Nadu precision farming mission and national AI mission. This review paper fills the gap between soil health, crop prediction, crop yield, scientific irrigation, and fertilizer uses.It directly and indirectly contributes towards enhancing soil fertility that can lead to sustainable agriculture.