Category: InCane

An intelligent approach for optimizing sugarcane transporting cost and logistics management Halagani, R., Soragaon, B., Rajesh, S.M. & Shailaja, V.N.  Asian journal of civil engineering | May 2025 Comparative analysis

Assessment of gains in productivity and water-energy-carbon nexus with tillage, trash retention and fertigation practices in drip irrigated sugarcane Wakchaure, G.G., et al Renewable and Sustainable Energy Reviews | 211|

Artificial Intelligence in Agricultural Mapping: A Review Espinel, R., et al Agriculture |14(7)| July 2024 Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for

Generative AI and Its Impact on Sugarcane Industry: An Insight into Modern Agricultural Practices Ray, P.P. Sugar Tech |January 2024 Mobile Application Implementation as Agriculture 4.0 Strategy for Sugarcane Yield

A Mathematical Model to Minimize the Total Cultivation Cost of Sugarcane Kumar, S. & Pant, M. In: Soft Computing for Problem Solving| 2023 Operational cycle analysis of Colombian sugarcane harvest,

A Soil Physical Assessment Over Three Successive Burned and Unburned Sugarcane Annual Harvests Ortiz, P.F.S., et al Sugar Tech |25| 2023 Combined Chemical Fertilizers with Molasses Increase Soil Stable Organic

Chapter 6. Smart Irrigation and Cultivation Recommendation System for Precision Agriculture Driven by IoT Kumari, N.M.J., Thirupathi Rao, N. & Bhattacharyya, D. In: Machine Intelligence, Big Data Analytics, and IoT

Sugarcane (Saccharum officinarum L.) under saline stress: Growth, productivity, technological quality, and industrial yield Florentino de Morais, J.E., et al Industrial crops and products |188| November 2022 Detection of White

A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops Amarasingam, N., et al Environment |26| April 2022 Improving sugarcane production in saline soils with Machine Learning

Improving sugarcane growth simulation by integrating multi-source observation into a crop model Yu, D., Zha, Y., Shi, L., Ye, H. & Zhang, Y. European Journal of Agronomy |132|January 2022 Machine