Advancements and Future Prospects of Artificial Intelligence for Sustainable Agriculture
Dhamni Patyal
Department of Agronomy, Sher-e-Kashmir University of Agriculture Sciences and Technology of Jammu, Jammu and Kashmir, India.
Chandan Kumar Panigrahi *
Department of Entomology, Faculty of Agricultural Sciences, Siksha 'O' Anusandhan, Deemed to be University, Bhubaneswar - 751029 Odisha, India.
Nikitasha Dash
Department of Fruit Science, OUAT, BBSR, Odisha, India.
Smikhyia Gautam
Department of Agronomy, Sher-e-Kashmir University of Agriculture Sciences and Technology of Jammu, Jammu and Kashmir, India.
Anjali Verma
Department of Plant Pathology, Banda University of Agriculture and Technology (U.P.), 210001, India.
Aman Tutlani *
Division of Genetics and Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST–K), Wadura- 193201, J & K, India.
Rumaina Rehman Khan
School of Bioengineering and Biosciences, Lovely Professional University, Phagwara- 144411, Punjab, India.
*Author to whom correspondence should be addressed.
Abstract
Artificial Intelligence (AI) in the sector of agriculture is still in the budding phase. Integrating AI in agriculture with multidisciplinary approach can provide enhanced results and revolutionize agriculture. AI is showing significant change in the agriculture by improving economic performance, precision farming, disease detection, crop monitoring, automated machines and management of livestock to improve the productivity and optimized use of resources. Integrating AI in the management of plant disease and soil health are helping farmers in reducing the cost on resources use and manpower. Machine learning algorithms are very helpful in analyzing the data for the use of fertilizers, herbicides, pesticides and water. It will further reduce agricultural waste and increases productivity of crop which align with sustainability and SDGs. Initial cost of implementation and data security are still the big challenges which need to be addressed for the robust implementation of AI in the field of Agriculture.
Keywords: Artificial Intelligence (AI), machine learning, soil health, crop monitoring, autonomous tractors, Convolutional Neutral Networks (CNNs), food processing, sustainability, robotics