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Towards a New Era of Sustainable Agriculture: AI Applications and Case Studies in Crop Management
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 18 |
| Year of Publication: 2025 |
| Authors: Viswa Chaitanya Marella, Sai Teja Erukude, Suhasnadh Reddy Veluru |
10.5120/ijca2025925294
|
Viswa Chaitanya Marella, Sai Teja Erukude, Suhasnadh Reddy Veluru . Towards a New Era of Sustainable Agriculture: AI Applications and Case Studies in Crop Management. International Journal of Computer Applications. 187, 18 ( Jul 2025), 15-20. DOI=10.5120/ijca2025925294
Abstract
Agriculture is experiencing a digital revolution, and Artificial Intelligence (AI) is emerging as the catalyst for sustainable crop management. This paper provides a concise review of AI-enabled applications in precision agriculture, focusing on four key areas of crop management: yield prediction, precision seeding and fertilization, pest and disease control, and optimal irrigation and soil health. Several case studies and real-world implementations are highlighted to exemplify technical outcomes and practical benefits. AI is now leveraging machine learning (ML) and deep learning (DL) models to model yield prediction in real-time, utilizing multi-source data (weather, soil, remote sensing components) to predict crop yield and empower proactive decisions. In precision seeding and fertilization, AI-enabled systems, including computer vision-based planters and variable rate fertilization systems, demonstrate uniform sowing and optimal nutrient application, thereby increasing efficiency and eliminating ceremonial waste. In pest and disease control, deep learning-based image recognition achieves expert or better-than-expert performance in image recognition. Aside from thorough identification (pests or diseases), innovative sprayers and robotics enable interventions directed at the affected areas, reducing pesticide use (up to 90% in some cases). In irrigation and soil health, smart irrigation scheduling and AI-enabled soil monitoring optimize water use (30-40% water savings compared to conventional practices) and maintain soil health (e.g., salinization). This paper also discusses implementation and deployment issues, including limited data, costs, barriers to adoption by farmers, and the interpretability of various models. Taming these issues highlights the need to scale up AI-based solutions in agriculture. The case studies demonstrate ontological progress and opportunities for continued development toward more resilient, productive, and sustainable farming systems.
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