03399nas a2200373 a 4500 20260818100103.0 260407s20249999th u ms tm 000 eng d .b12498695 AIT Diss. no.ICT-24-02 Nisit Pukrongta Enhancing crop yield predictions with ensemble machine learning model using IoT environmental data and UAV vegetation indices : a case study of Maize Pathum Thani, Thailand : Asian Institute of Technology, 2024 92 leaves : ill. +1 online resource Dissertation ; no. ICT-24-02 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Information and Communication Technologies Thesis (Ph.D.) - Asian Institute of Technology, 2024 This dissertation introduces a weighted ensemble prediction model, PEnsemble4model, that combines several machine learning models to enhance the accuracy of maize yield at early growth stages. Utilizing both unmanned aerial vehicle (UAV) imagery and In ternet of Things (IoT) enabled sensors collected environmental data, as well as soil and plant nutrient attributes, the model offers a detailed, data-driven methodology for maize yield prediction. Given the increasing global demand for maize and the crop{u2019}s suscep tibility to weather fluctuations, the enhancement of predictive capabilities is crucial. The PEnsemble 4 model meets this demand by utilizing extensive datasets that include soil characteristics, nutrient levels, weather conditions, and UAV-captured vegetation imagery. It employs a combination of Huber and M estimates to analyze temporal varia tions in vegetation indices, particularly Chlorophyll-red edge index (CIre) and Normal ized difference red edge index (NDRE), which are key indicators of canopy density and plant height. Remarkably, the PEnsemble 4 model achieves an accuracy rate of 91%. It improves the timing of yield predictions from the traditional reproductive stage (R6) to the earlier blister stage (R2), thus facilitating more timely decision-making in agricul tural practices. Additionally, the model also capabilities extend to the water stress, crop stress , and disease detection, enhancing overall agricultural management. By integrat ing multi-modal data and machine learning technologies, the PEnsemble 4 model offers an innovative and effective approach to maize yield prediction. Internet of things Agricultural applications Agriculture Data processing Crop improvement Attaphongse Taparugssanagorn, Chairperson Vatcharaporn Esichaikul, Examination Committee Chaklam Silpasuwanchai, Examination Committee National Science and Technology Development Agency (NSTDA),Thailand, Scholarship Donor Asian Institute of Technology. Dissertations ; no. ICT-24-02 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B23971 .b12498695 mnarc a 260423 0 260422 m h a 0 mnarc 67 Barcode : - CREATED : 2026-07-04 RECORD # : i13595453 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 38437 38437 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT Diss. no.ICT-24-02 2026-08-18 10:01:03 1 2026-08-18 67