Jurnal Tanaman Hias Untuk Pertambahan Jumlah Daun

Sejumlah metode untuk mengidentifikasi pokok kayu melalui citra telah diajukan oleh bilang pengkaji. Umumnya, metode-metode nan digunakan tidak memperhatikan pengetahuan warna, sebab corak tidak dianggap andai faktor buat identifikasi, mengingat tanaman yang digunakan sebagai spesimen bercat hijau. Hal itu tidak berlaku bagi pokok kayu hias daun, yaitu tanaman yang memiliki daun beraneka warna, corak daun yang indah, dan rajah daun yang partikular. Puas tanaman hias daun, bagan, warna dan tekstur merupakan fitur yang perlu diikutsertakan dalam identifikasi daun. Pada riset ini, bentuk patera dan lemak tulang daun, corak, dan tekstur plong daun disertakan dalam mengklasifikasi tanaman maupun mencari tanaman-tanaman nan memiliki kemiripan paling janjang terhadap patera masukan. Pemburuan pokok kayu ditujukan bikin menyerahkan saran kepada konsumen berupa pilihan yang terdiri atas lima pokok kayu nan diharapkan dapat mendukung konsumen dalam mengenali tanaman ketika sistem pengklasifikasi gagal melaksanakan tugasnya. Selain itu, riset ini mengembangkan algoritma nan digunakan kerjakan mencari tanaman berdasarkan rona dominan yang biasa digunakan anak adam, seperti hijau atau hijau liar. Pada penelitian ini, Probabilistic neural Network (PNN) dipakai laksana pengklasifikasi dan jarak City block dengan bobot terhadap fitur bentuk, warna, benak daun, dan tekstur dipakai kerjakan mengerjakan pencarian patera dan pendekatan fuzzy dipakai untuk melakukan pencarian pokok kayu berdasarkan warna dominan. Penelitian juga melibatkan Principal component Analysis (PCA) untuk mengurangi total fitur yang diperlukan bakal identifikasi. Eksperimen dilakukan dengan menunggangi dataset Flavia dan Foliage. Flavia adalah dataset nan bersumber dari peneliti enggak nan umum dipakai dalam penelitian identifikasi patera. Flavia mengandung 32 tipe tanaman dengan daun berwarna hijau. Dataset Foliage ampuh enam puluh jenis daun tanaman rias, yang dipotret sepanjang riset dilakukan. Hasil penelitian menunjukkan bahwa sistem klasifikasi memberikan ketelitian sebesar 94,6875% jika menunggangi Flavia. Keakuratan pada Foliage sebesar 92,9167% dengan menyertakan 52 fitur. Dengan mereduksi total fitur hingga 20 biji kemaluan melalui PCA, kinerja meningkat menjadi 94,5%. Pengejawantahan tegar berada di atas 80% sekiranya fitur direduksi melampaui PCA menjadi deka- buah.

Several methods to identify plants by using a leaf of plant have been proposed by several researchers. Commonly, the methods did not capture color, because color was not recognized as an important aspect to the identification. The main reason was caused by a fact that they used green colored leaves as samples. However, for foliage plants—plants with colorful leaves, fancy patterns in their leaves, and interesting plants with unique shape—color and also texture could not be neglected. Therefore, combination of shape, color, texture features, and other attribute contained on the leaf is very useful in leaf identification. In this research, shape and vein, color, and texture features were incorporated to classify a leaf and to retrieve plants which have most similar with leaf of query. The plant retrieval was intended to give suggestion to users five plants that may help them to identify the leaf when classification process failed. Besides, this research also tried to develop an algorithm to find all leaves of plants in a database that have certain dominant color, where the dominant color is determined by human perception, such as green or dark green. In this riset, a neural network called Probabilistic Neural network (PNN) was used as a classifier, the Euclidean distance with weighting coefficients of shape, vein, and color features was used to retrieve leaves and fuzzy approach was used to retrieve leaves which fulfill the dominant color. Moreover, the research also included Principal Component Analysis (PCA) in order to reduce features. The experiments was accomplished by using Flavia and Foliage datasets. Flavia is a dataset came from other researchers that was commonly used in leaf retrievals. It contains 32 kinds of plants with all of them have green color leaves. Meanwhile, Foliage are dataset with various color leaves that were prepared by authors. Foliage contains 60 kinds of foliage plants. The results shows that the method for classification gave average accuracy of 94.6875% when it was tested on Flavia dataset. It means that the method gave better performance compared to the original work. The identification system gave average accuracy of 92.9167% for 60 kinds of foliage plants by using 52 features. By reducing features to 20 using PCA, the performance increased to 94.5%. The performance was still more than 80% when the besaran features was compressed to 10.

Kata Kancing : jarak City block, Fuzzy, identifikasi tanaman, PCA, PNN