Lucky Candra Musahada
Papers
1
Total Citations
5
H-Index
1
About
Lucky Candra Musahada is a researcher specializing in agricultural technology and computer vision, with a focus on applying deep learning to precision agriculture. His key research areas include fruit detection and counting using convolutional neural networks (CNNs), particularly the YOLOv7 architecture. In his most-cited work, a 2024 comparative study on citrus fruit detection, Musahada evaluated the performance of different YOLOv7 variants—original, tiny, and X—for identifying and counting Citrus reticulata Blanco cv. Batu 55 fruits. He demonstrated that the original YOLOv7 model outperformed its counterparts in accuracy and efficiency, offering a robust solution for automated fruit monitoring. This contribution has garnered 5 citations, highlighting its relevance to the growing field of smart farming. Musahada’s work addresses critical challenges in agricultural productivity, enabling real-time, non-invasive crop assessment. His research not only advances computer vision methodologies but also provides practical tools for farmers and agronomists to optimize yield estimation and resource management. With a focus on bridging AI and agriculture, Musahada continues to make impactful strides in sustainable farming technologies.
Research Focus
Key Achievements
Top Papers
- 1