Author:
R. Shamshiri Redmond,Behjati Maryam,K. Balasundram Siva,Teh Boon Sung Christopher,A. Hameed Ibrahim,Kamil Zolkafli Ahmad,Ho-Song An,Mohd Noh Arina,Hisham Abu Bakar Badril,Balogun W.A.,Kang Beom-Sun,Pham Cong-Chuan,Khanh Linh Le Dang,Hee Noh Dong,Kim Dongseok,Habineza Eliezel,Kamaroddin Farizal,Kim Gookhwan,Kim Heetae,Hwang Hyunjung,Park Jaesung,Song Jisu,Sung Joonjea,Muslimin Jusnaini,Young Lee Ka,Lee Kayoung,Do Lee Keong,Kazeem Kolawole Keshinro,Il Park Kyeong,Fu Longsheng,Ashrafuzzaman Gulandaz Md,Asrakul Haque Md,Nasim Reza Md,Razob Ali Md,Rejaul Karim Md,Sazzadul Kabir Md,Shaha Nur Kabir Md,Song Minho,Shukri Zainal Abidin Mohamad,Ali Mohammad,Aufa Md Bookeri Mohd,Nadzim Nordin Mohd,Nadzri Md Reba Mohd,Nizam Zubir Mohd,Saiful Azimi Mahmud Mohd,Taufik Ahmad Mohd,Hariz Musa Muhammad,Sharul Azwan Ramli Muhammad,Mohd Mokji Musa,Yoshimoto Naoto,Tuong An Nguyen Nhu,Khalidah Zakaria Nur,Kumar Prince,Garg P.K.,Ismail Ramlan,Kondo Ren,Kojo Ryuta,Samsuzzaman ,Yu Seokcheol,Park Seok-Ho,Ahmed Shahriar,Noor Aliah Baharom Siti,Islam Sumaiya,Chung Sun-Ok,Sen Teik Ten,Manduna Mutabazi Tinah,Lin Wei-Chih,Jin Cho Yeon,Ho Kang Young
Abstract
This chapter is a collection of selected abstracts presented at the 10th Asian-Australasian Conference on Precision Agriculture, held from October 24th to 26th in Putrajaya, Malaysia. It aims to emphasize the transformative potential of technology in precision agriculture and smart farming. The featured studies highlight the transformative impact of technology and current improvements in agriculture, offering modern solutions including machine learning, robotics, remote sensing, and geographic information systems (GIS). From autonomous navigation for mobile robots to stress classification in crop production systems, and from phenotypic analysis with LiDAR technology to real-time sensor monitoring in greenhouse agriculture, the majority of abstracts underline the integration of digital tools in different fields of farming with the core objective of reshaping conventional farming techniques and eliminating dependency on manual works. Key examples include the development of a distributed sensing system (DSS) used for orchard robots, stress classification for tomato seedlings through image-based color features and machine learning, and the integration of remote sensing and AI in crop protection. Other solutions, such as automated spraying robots for cherry tomato greenhouses, active back exoskeletons for rice farm lifting tasks, and advancements in seedling transplanting techniques, have shown promising results for contributing to sustainable farming practices by providing accurate and timely information for decision-making amid climate change-induced uncertainties.