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008			260407s20249999th u ms tm 000 eng d
035			_a.b12498695
099		9	_aAIT Diss. no.ICT-24-02
100	1		_aNisit Pukrongta
245	1	0	_aEnhancing crop yield predictions with ensemble machine learning model using IoT environmental data and UAV vegetation indices : _ba case study of Maize
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2024
300			_a92 leaves : _bill. _e+1 online resource
490	1		_aDissertation ; _vno. ICT-24-02
500			_aA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Information and Communication Technologies
502			_aThesis (Ph.D.) - Asian Institute of Technology, 2024
520			_aThis 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.
650		0	_aInternet of things _xAgricultural applications
650		0	_aAgriculture _xData processing
650		0	_aCrop improvement
700	0		_aAttaphongse Taparugssanagorn, _eChairperson
700	0		_aVatcharaporn Esichaikul, _eExamination Committee
700	0		_aChaklam Silpasuwanchai, _eExamination Committee
710	2		_aNational Science and Technology Development Agency (NSTDA),Thailand, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tDissertations ; _vno. ICT-24-02
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B23971
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