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Pengelasan dan Pengecaman Daun Tumbuhan Akuatik

By: Muhammad Azfar Firdaus Azlah, Lee Suan Chua, Fakhrul Razan Rahmad, Farah Izana Abdullah and Sharifah Rafidah Wan Alwi

Article Prepared By: Farah Izana

At the present time, aquatic plant recognition is still the specialization of plant taxonomists. These laboratory-based recognition requires skills in sample treatment and data interpretation, in addition to time consuming procedures. With the advancement of computing technologies would be another alternative of choice for non-specialists. Nowadays, the morphological characteristics of leaves can be extracted by a mathematical model to put into a software program for recognition. This could reduce false positive results due to human error. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation.

Web: 10.3390/computers8040077

Tarikh Input: 30/12/2021 | Kemaskini: 30/12/2021 | m_fakhrulddin

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