Efficient Detection of Soil Nutrient Deficiencies through Intelligent Approaches
P. Ashoka
Agricultural Research Station, University of Agricultural Sciences, Dharwad, Hanumanmatti (p) Ranebennur(tq), Haveri (District)– 581115, Karanataka State, India.
G. J. Avinash
Department of Soil science, Navsari Agricultural University, Navsari, Gujarat, India.
M. T. Apoorva
Department of Soil science, University of Agricultural Sciences Bengaluru, GKVK, Bangalore, Karnataka, India.
Pranav Raj *
Department of Soil Science and Agricultural Chemistry, Sam Higginbottom University of Agriculture Technology and Sciences, India.
M. Sekhar
CASAR, Bharatiya Engineering Science and Technology Innovation University (BESTIU), India.
Sanjay Singh
Department of Plant Pathology, ANDUAT, Kumarganj, Ayodhya, UP, India.
R. Vijay Kumar
SHUATS, Prayagraj, UP, India.
Bal veer Singh *
Department of Agronomy, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, India.
*Author to whom correspondence should be addressed.
Abstract
Exploring the use of intelligent approaches, particularly artificial intelligence (AI) and machine learning (ML), in detecting soil nutrient deficiencies, is a crucial aspect of agriculture. Traditional methods of soil nutrient analysis, although effective, are beset with limitations, including high costs, time-intensiveness, and lack of real-time data. Emerging intelligent approaches address these challenges by providing real-time, accurate data on soil nutrient levels, thereby enabling timely and precise fertilization. Several case studies, including the Indian startups CropIn and Fasal, demonstrate the successful application of these technologies in agriculture, leading to improved crop yields, reduced fertilizer costs, and enhanced sustainability. The article also discusses ongoing research and prospects, highlighting the potential of AI not only in detection but also in predictive analysis. Finally, the piece provides a roadmap for farmers and stakeholders interested in adopting these intelligent approaches, emphasizing the importance of understanding the technology, choosing suitable tools, and fostering a mindset of change and continuous learning. Overall, intelligent approaches to soil nutrient detection promise a more productive, sustainable, and economically viable future in farming.
Keywords: Soil nutrient analysis, artificial intelligence, machine learning, precision agriculture, sustainable farming