AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data

Nithya Segar¹, Ragunath Kaliyaperumal¹*, Pazhanivelan¹, Kumaraperumal¹, Latha², Muthumanickam¹, Jagadeeshwaran¹

¹Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore-641 003
²Coconut Research Station (CRS), TNAU, Aliyarnagar

(Manuscript received 16 April 2024; accepted for publication 31 May 2024)

Abstract. Plantation mapping plays a vital role in agriculture, forestry, and land management. The integration of Artificial intelligence and Machine learning techniques with high-resolution satellite data has revolutionized the accuracy and efficiency of plantation mapping. Utilizing AI and machine learning tools for plantation mapping offers a transformative approach to efficient and accurate land management. These technologies enable automated analysis of satellite imagery and other geospatial data, facilitating rapid and precise identification of plantations, crop health assessment, and yield predictions. The integration of AI enhances the mapping process, providing valuable insights for sustainable agriculture, resource optimization, and environmental monitoring. The application of these advanced tools in plantation mapping represents a significant leap towards data-driven and environmentally conscious land management practices. It presents a promising advancement in agricultural practices. By leveraging these technologies for automated analysis of satellite imagery and geospatial data, accurate and timely mapping of plantations becomes feasible. The use of AI and ML tools in Plantation mapping, challenges in integration, the possible solutions and its future prospects are reviewed in this paper not only to enhance efficiency but also to offer insights into crop health, aiding in precision agriculture and resource optimization.