Applications of Artificial Intelligence (AI) in the Field of Agriculture: A Review
Nand Lal Singh
Department of Agriculture, Dr. C.V. Raman University, Bhagwanpur, Vaishali-844114, Bihar, India.
Bahiram Rani Vishwas
Department of Soil Science & Agricultural Chemistry, H. H. Sri Sri Murlidharan Swamiji College of Agriculture (Mahatma Phule Krishi Vidyapeeth, Rahuri, Ahamadnagar, Maharashtra) Malegaon-423104, Nasik, India.
Saurabh Singh
College of Agri-Business Management, Acharya Narendra Deva University of Agriculture & Technology, Kumarganj-224229, Ayodhya (Uttar Pradesh) India.
Jitender Bhati
Department of Genetic and Plant Breeding, Gochar Mahavidyala (Maa Shakumbhari University Saharanpur, Uttar Pradesh) Rampur Maniharan, Saharanpur (Uttar Pradesh) India.
Sanjay Kumar Tripathi
College of Agriculture (Campus) Lakhimpur Kheri, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur (Uttar Pradesh) India.
Dheerendra Kumar
Department of Soil Science & Agricultural Chemistry, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur (Uttar Pradesh) India.
Pradip Kumar Saini *
Department of Crop Physiology, Acharya Narendra Deva University of Agriculture & Technology, Kumarganj-224229, Ayodhya (Uttar Pradesh) India.
*Author to whom correspondence should be addressed.
Abstract
Artificial Intelligence (AI) has emerged as a crucial instrument in the agriculture industry, with the opportunity to transform conventional farming methods and tackle issues such as climate change and population increase. Artificial intelligence (AI) technologies are used throughout the entire agricultural industry, starting with planning which crops to grow to the last stages of harvesting and distributing the produce. Machine learning algorithms process extensive agricultural data, empowering farmers to make decisions based on data and optimise the allocation of resources. The implementation of artificial intelligence (AI) in robotics and automation has significantly transformed operations that need a lot of manual labour, resulting in lower operational expenses and increased efficiency. Artificial intelligence (AI) powered predictive analytics technologies empower farmers to forecast market trends, enhance supply chain management, and manage risks related to price volatility and demand changes. Nevertheless, it is crucial to give considerable thought to challenges such as data privacy, interoperability, and algorithmic bias. The disparity in access to digital resources between rural and urban communities also creates obstacles to the adoption of technology, underscoring the importance of focused investment in infrastructure and skill development. AI technology can transform agriculture by promoting sustainable practices, improving productivity, and guaranteeing food security for future generations.
Keywords: AI, agriculture, challenges and limitations, digital technology