Search Results - (( java application optimisation algorithm ) OR ( leaf identification _ algorithm ))

Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201 by CHYNTIA JABY, ENTUNI, TENGKU MOHD AFENDI, ZULCAFFLE

“…The DenseNet-201 algorithm was utilised in the proposed method of identifying corn leaf diseases. …”
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Comparative study on leaf disease identification using Yolo v4 and Yolo v7 algorithm by Wang, Xinming, Tang, Sai Hong, Mohd Ariffin, Mohd Khairol Anuar, Ismail, Mohd Idris Shah

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Classification of Citrus (Rutaceae) by Using Image Processing by Najwa Bari'ah Mohd Tabri

“…A machine learning algorithms, SVM have been used to build species identification models. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

“…Plants identification has become a significant and incentive research area. …”
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Plant recognition based on identification of leaf image using image processing / Nor Silawati Sha’ari by Sha’ari, Nor Silawati

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Feature selection for Malaysian medicinal plant leaf shape identification and classification by Sainin, Mohd Shamrie, Alfred, Rayner

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Maize leaf disease detection and classification using Convolutional Neural Network (CNN) / Syafiqah Amir by Amir, Syafiqah

“…The labelled dataset is then used to create and train a CNN architecture which define the type of the leaf based on its category. The dataset used in this project consist of 800 images of four category of leaf achieved 90 percent of accuracy by using the CNN algorithm.…”
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SMART AGRICULTURAL MONITORING SOLUTION FOR CHILLI LEAF DISEASES USING A LOW-COST KINECT CAMERA AND AN IMPROVED CNN ALGORITHM by Chyntia Jaby, Entuni, Tengku Mohd Afendi, Zulcaffle, Kismet, Hong Ping, Amit Baran, Sharangi, Tarun Kumar, Upadhyay, Mohd, Saeedd

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Deep plant: A deep learning approach for plant classification / Lee Sue Han by Lee , Sue Han

“…They look for the procedures or algorithms that maximize the use of leaf databases for plant predictive modelling, but this results in leaf features which are liable to change with different leaf data and feature extraction techniques. …”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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Identification of reliable reference genes for gene expression studies of oil palm plantlets using normfinder and bestkeeper algorithms by A. H., Muhamad Afiq, Shaharuddin, Noor Azmi, R., Zubaidah

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Plant leaf recognition algorithm using ant colony-based feature extraction technique by Ghasab, Mohammad Ali Jan

“…To do this, at first, based on the proposed algorithm,the physiological dimensions of leaves are automatically measured and with regard to these parameters, specified features such as shape, morph, texture and colour are extracted from the image of the plant leaf through image processing to create a reserved feature database to be used for different species of plants. …”
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Analysis of banana plant health using machine learning techniques by Thiagarajan, Joshva Devadas, Kulkarni, Siddharaj Vitthal, Jadhav, Shreyas Anil, Waghe, Ayush Ashish, Raja, S.P., Rajagopal, Sivakumar, Poddar, Harshit, Subramaniam, Shamala

“…This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.…”
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Classification of capsicum leaf disease from a complex cluster of leaves using an improved multiple layers ShuffleNet CNN model by Chyntia Jaby, Entuni, Tengku Mohd Afendi, Zulcaffle, Kismet, Hong Ping

“…One of the major challenges in capsicum cultivation is the accurate identification of leaf diseases. Leaf diseases can have a detrimental effect on the quality of capsicum production, leading to substantial losses for farmers. …”
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Review of Wheat Disease Classification and Severity Detection Models by Hongyan, Zang, Annie, Joseph, Shourong, Zhang, Rong, Liu, Wanzhen, Wang

“…This paper mainly aims to explain deep learning-based wheat diseases identification algorithm, and to discuss the benefits and drawbacks of present wheat disease detection approaches. …”
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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

“…Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.…”
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Cabbage disease detection system using k-NN algorithm by Mohamad Ainuddin Sahimat

“…Identification of plant diseases is key to avoiding losses in agricultural yields and product quantities. …”
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Detection of multiple mangoes using histogram of oriented gradient technique in aerial monitoring by Mohd Ali, Nursabillilah, Karis, Mohd Safirin, Mohd Sobran, Nur Maisarah, Bahar, Mohd Bazli, Oh, Kok Ken, Mat Ibrahim, Masrullizam, Johan, Nurul Fatiha

“…The project uses shape identification algorithm and Histogram of Oriented Gradient principle to detect and count the total number of mango on its tree using a quad copter with an attachable webcam. …”
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Delineating mangrove forest zone using spectral reflectance by Abdul Whab @ Abdul Wahab,, Zulfa

“…Later, electromagnetic wave bands derived from the in-situ measurements were used as spectral libraries to classify individual mangrove species. Species identification with spectral library derived from in-situ measurements using SID algorithm and derived from Landsat 8 using SAM algorithm was done. …”
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