Introduction
Artificial intelligence’s versatility is one aspect that makes the technology practically universal when it comes to industrial applications and process improvements. However, the most admirable aspect of the technology is how it transforms industries that may not seem to be the ideal candidates. This paper presents AI integration into farming to monitor soil health and production-friendly indicators. The primary direction in this analysis is that artificial intelligence can promote multidimensional soil data integration into an agro-industrial system that guides decision-making on crop rotation.
Discussion
Machine learning algorithms for automated farm monitoring and soil data processing can catapult intensive food-based agricultural production to end global hunger. Deorankar and Rohankar (2020) detailed that an AI system in soil test-based fertility management can effectively increase farm productivity, especially for soils characterized by high special variability. The fertility management technique entails remote sensing capabilities for detecting or estimating soil quality indicators (Diaz-Gonzalez et al., 2022). The automated soil-testing approach complements knowledge of existing crop yield prediction systems that use soil data such as biological, physical, and chemical composition (Diaz-Gonzales et al., 2022). Therefore, the new value provided by AI technology is that it allows automation and algorithm-based predictions for more solid decision-making.
Any innovative technology that serves human needs should be capable of adding value by saving money or improving work efficiency. AI in soil health monitoring is an unconventional application of the technology, albeit capable of adding numerous benefits to farmers and consumers. One value-addition of test-based fertility management is that, as production increases, food prices come down. According to Deorankar and Rohankar (2020), agriculture-dependent nations will benefit from AI-led soil diversity, which allows farmers to maintain year-round production efficiencies. The implication is that such nations can gain comparative trade advantages by providing quality food varieties in global markets.
Conclusion
In conclusion, soil health monitoring became an ideal candidate for AI technology once recent studies showed future value-based opportunities in farming. The possible benefits of AI technology in test-based fertility management are production efficiency improvements and lowered food costs. The technology is likely to get a friendly reaction from industry stakeholders, given that the production technique can improve crop yields and food production for animals. Therefore, farmers should embrace automation and algorithm-based predictions for more solid production decision-making.
References
Diaz-Gonzalez, F. A., Vuelvas, J., Correa, C. A., Vallejo, V. E., & Patino, D. (2022). Machine learning and remote sensing techniques applied to estimate soil indicators–Review. Ecological Indicators, 135, 108517. Web.
Deorankar, A. V., & Rohankar, A. (2020). Soil health monitoring system using AI. JETIR, 7(1), 1-4. Web.