Search Results - (( java implication based algorithm ) OR ( using rice learning algorithm ))

Rice yield prediction - a comparison between enhanced back propagation learning algorithms by Saad, Puteh, Jamaludin, Nor Khairah, Rusli, Nursalasawati, Bakri, Aryati, Kamarudin, Siti Sakira

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Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks by Rayner Alfred, Joe Henry Obit, Christie Chin Pei Yee, Haviluddin Haviluddin, Yuto Lim

“…This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. …”
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Rice Yield prediction - a comparison between Enchanced Back Propagation Learning Algorithms by Puteh, Saad, Nor Khairah, Jamaludin, Nursalasawati, Rusli, Aryati, Bakri, Siti Sakira, Kamarudin

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Prediction of international rice production using long short- term memory and machine learning models by Arya, Suraj, Anju, ., Nor Azuana, Ramli

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Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass by Derraz, Radhwane, Muharam, Farrah Melissa, Nurulhuda, Khairudin, Ahmad Jaafar, Noraini, Keng Yap, Ng

“…Nevertheless, VIs are collinear, and their analyses require machine learning algorithms (MLs). The analysis of collinear VIs using base (single) and ensemble MLs is yet to be investigated. …”
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A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework by Bari, Bifta Sama, Islam, Md Nahidul, Rashid, Mamunur, Hasan, Md Jahid, Mohd Azraai, Mohd Razman, Musa, Rabiu Muazu, Ahmad Fakhri, Ab. Nasir, Majeed, Anwar P.P. Abdul

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Machine learning techniques for reference evapotranspiration and rice irrigation requirements prediction: a case study of Kerian irrigation scheme, Malaysia by Mohd Nasir, Muhammad Adib, Harun, Sobri, Zainuddin, Zaitul Marlizawati, Kamal, Md Rowshon, Che Rose, Farid Zamani

“…Two machine learning algorithms, named Support Vector Regression (SVR) and Random Forest (RF), were applied to predict ETo and rice irrigation requirements using only climatic data (rainfall, temperature, relative humidity, and wind speed). …”
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Prediction of rice biomass using machine learning algorithms by Radhwane, Derraz

“…Unmanned aerial vehicles (UAVs) may address these issues. Machine learning algorithms (MLs) can predict rice biomass from UAV-based vegetation indices (VIs). …”
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A connectionist model to predict rice yield based on disease infection by Kamaruddin, Siti Sakira

“…Advance changes in technology, economy and business environment are influencing all sectors including agriculture.Rice as the worlds main dietary food is experiencing a decrease in yield due to the infection of pests and diseases, decreasing level of water sources, the scarcity of suitable land for agriculture and inefficient labour management.Rice Yield losses of approximately 31.5% were attributed to rice plant related diseases.This work describes the development of a connectionist model to predict the rice yield based on the amount of area infected by rice diseases.The Back Propagation learning algorithm were used with 5 input parameters which represents the planting seasons; the plantation district and the 3 main deadly disease recordings from the Muda Agricultural area in Malaysia during various planting seasons from 1995-2001. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Data-driven rice yield predictions and prescriptive analytics for sustainable agriculture in Malaysia by Marong, Muhammad, Husin, Nor Azura, Zolkepli, Maslina, Affendey, Lilly Suriani

“…This research investigates the impact of environmental conditions and management methods on crop yields, focusing on accurate predictions to inform decision-making by farmers. Utilizing machine learning algorithms as decision-support tools, the study analyses commonly used models—Linear Regression, Support Vector Machines, Random Forest, and Artificial Neural Networks—alongside key environmental factors such as temperature, rainfall, and historical yield data. …”
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Producer gas composition prediction using artificial neural network algorithm / Mohd Mahadzir Mohammud ... [et al.] by Mohammud, Mohd Mahadzir, Mohamad Bakre, Muhammad Syaham, Mohd Fohimi, Nor Azirah, Rabilah, Rosniza, Ahmad, Muhammad Iqbal

“…The goals are to predict the output producer gas using an algorithm and to compare the trained prediction result with actual experiment data for rice husk gasification. …”
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Intelligent inventory forecasting system / Fadzlinor Mustapa by Mustapa, Fadzlinor

“…The general finding for this project is that with Back propagation algorithm, the suitable learning rate for forecasting prototype is 0.1 with architecture 7-11-1 that is seven nodes employed in the input layer, eleven nodes in the hidden layer and lastly one node employed in the output layer.…”
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Evaluation of quality attributes and milling metrics of glutinous rice stored under different storage conditions using infrared thermal imaging by Dasore, Abhishek, Hashim, Norhashila, Shamsudin, Rosnah, Che Man, Hasfalina, Mohd Ali, Maimunah, Ageh, Opeyemi Michael

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An annotated image dataset of pests on different coloured sticky traps acquired with different imaging devices by Song-Quan Ong, Toke Thomas Høye

“…When investigating the automatic identification and counting of pests on sticky traps using computer vision and machine learning, two aspects can strongly influence the performance of the model – the colour of the sticky trap and the device used to capture the images of the pests on the sticky trap. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Aerial imagery paddy seedlings inspection using deep learning by Anuar, Mohamed Marzhar, Abdul Halin, Alfian, Perumal, Thinagaran, Kalantar, Bahareh

“…Experimental results showed that our proposed methods were capable of detecting the defective paddy rice seedlings with the highest precision and an F1-Score of 0.83 and 0.77, respectively, using a one-stage pretrained object detector called EfficientDet-D1 EficientNet.…”
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Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina by Dele-Afolabi, T.T., Jung, D.W., Ahmadipour, Masoud, Azmah Hanim, M.A., Adeleke, A.O., Kandasamy, M., Gunnasegaran, Prem

“…This study demonstrates the mass loss of monolithic and Ni-reinforced porous alumina developed using rice husk and sugarcane bagasse in acidic and alkaline corrosive media. …”
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Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina by Dele-Afolabi T.T., Jung D.W., Ahmadipour M., Azmah Hanim M.A., Adeleke A.O., Kandasamy M., Gunnasegaran P.

“…This study demonstrates the mass loss of monolithic and Ni-reinforced porous alumina developed using rice husk and sugarcane bagasse in acidic and alkaline corrosive media. …”

Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study by Abdellatief M., Hassan Y.M., Elnabwy M.T., Wong L.S., Chin R.J., Mo K.H.

“…Overall, the dataset of 128 CS results was used to develop the machine learning (ML) models. …”