Data-Driven Decision Making in Agriculture with Sensors, Satellite Imagery and AI Analytics by Digital Farming
Arijit Ghosh
Department of Agricultural Chemistry and Soil Science, Bidhan Chandra Krishi Viswavidyalaya, West Bengal, India.
Sumit Rai *
Centre for Environment Assessment & Climate Change, GB Pant National Institute of Himalayan Environment, Kosi-Katarmal, Almora-263643, Uttarakhand, India.
Ashoka, P
Department of Agronomy, College of Agriculture (University of Agriculture Science, Dharwad-580005) Hanumanmatti (p) Karnataka State, India.
Kiran Kotyal
Institute of Agri-Business Management, University of Agricultural Sciences Bangalore, Karnataka - 560065, India.
Sabarinathan B
Department of Remote sensing & GIS, Tamil Nadu Agricultural University, Coimbatore – 641003, India.
Saty Saran
Department of Entomology, U.P. Autonoms College, Varanasi, India.
Anjali
Agriculture Extension and Communication, Motherhood University Roorkee, India.
K.P.Sivakumar
Department of Family Resource Management and Consumer Science, Community Science College and Research Institute, TNAU, Madurai, India.
Narinder Panotra
Agronomy, Institute of biotechnology SKUAST Jammu, India.
Shivam Kumar Pandey
Rashtriya Raksha University, India.
*Author to whom correspondence should be addressed.
Abstract
Digital technologies are revolutionizing agriculture by enabling data-driven decision making. A combination of sensors, satellite imagery, and AI analytics is providing farmers with unprecedented insights to optimize crop management. Sensors monitor soil moisture, temperature, and nutrient levels in real-time. High-resolution satellite images track crop health, growth stages, and yield potential. Machine learning algorithms process this data to generate actionable recommendations on irrigation, fertilization, pest control, and harvest timing. Case studies demonstrate how these technologies have increased yields, reduced inputs, and improved sustainability on farms worldwide. However, challenges remain in technology adoption due to high costs, lack of digital literacy, and data privacy concerns. Overcoming these barriers will be crucial to harnessing the full potential of digital farming. This paper reviews the current state of digital technologies in agriculture and discusses future research directions to advance data-driven decision making on farms.
Keywords: Digital agriculture, precision farming, remote sensing, machine learning, big data