Jurnal Keteknikan Pertanian (JTEP) terakreditasi berdasarkan SK Dirjen Penguatan Riset dan Pengembangan Kementerian Ristek Dikti Nomor I/E/KPT/2015 tanggal 21 September 2015. Selain itu, JTEP juga telah terdaftar pada Crossref dan telah memiliki Digital Object Identifier (DOI) dan telah terindeks pada ISJD, IPI, Google Scholar dan DOAJ. Mulai edisi ini redaksi memandang perlu untuk meningkatkan nomor penerbitan dari dua menjadi tiga kali setahun yaitu bulan April, Agustus dan Desember berisi 12 naskah untuk setiap nomornya. Hal ini dimaksudkan untuk mengurangi masa tunggu dengan tidak menurunkan kualitas naskah yang dipublikasikan. Jurnal berkala ilmiah ini berkiprah dalam pengembangan ilmu keteknikan untuk pertanian tropika dan lingkungan hayati. Penulis makalah tidak dibatasi pada anggota PERTETA tetapi terbuka bagi masyarakat umum. Lingkup makalah, antara lain: teknik sumberdaya lahan dan air, alat dan mesin budidaya pertanian, lingkungan dan bangunan pertanian, energi alternatif dan elektrifikasi, ergonomika dan elektronika pertanian, teknik pengolahan pangan dan hasil pertanian, manajemen dan sistem informasi pertanian. Makalah dikelompokkan dalam invited paper yang menyajikan isu aktual nasional dan internasional, review perkembangan penelitian, atau penerapan ilmu dan teknologi, technical paper hasil penelitian, penerapan, atau diseminasi, serta research methodology berkaitan pengembangan modul, metode, prosedur, program aplikasi, dan lain sebagainya. Penulisan naskah harus mengikuti panduan penulisan seperti tercantum pada website dan naskah dikirim secara elektronik (online submission) melalui http://journal.ipb.ac.id/index.php/jtep.
Destructive impacts of herbicide usage on environment and water contamination have led to many researches oriented toward finding solutions for their accurate use. If density and weeds species could be correctly detected, patch spraying or spot spraying can effectively reduce herbicide usage. A precision automated machine vision for weed control could also reduce the usage of chemicals. Machine vision is a useful method for segmentation of different objects in agricultural applications, especially pattern recognition methods. Many indices have been investigated by researchers to perform weed segmentation based on color information of the images. But there is no research that aims to identify weed diversity and its influence on the consumption of herbicides. The purpose of this research is to build a system that can recognize weeds and plants. In this study the relation between three main components (red, green and blue) of the images and color feature extraction (Hue, Saturation, Intensity) used to define weeds and plants density. Fractal dimension used as the methode to define shape features to distinguish weeds and plants. Weeds and plants were segmented from background by obtaining H value and its shape was obtained by fractal dimension value. The results show fractal dimension value for weeds and plants has specific values. Corn plants have fractal dimension values in the range 1.148 to 1.268, peanut plants have fractal dimension values in the range 1.511 to 1.629, while the weeds have Fractal dimension values in the range 1.325 to 1.497
This study aimed at designing a method to determine color level of corn leave and to estimate chlorophyll content using mobile phone cameras. The image of the leafs are captured with hand phone camera, and then the R, G, B color components are extracted. Four levels of color levels were used as those of leaf color card issued by IRRI. As for the natural light compensation, the colors of the Indonesian national inhabitant ID card were used as color references. The leafs were placed on the ID card, captured together, and then the leaf color level is determined by the variation of the grey levels of the leaves and the color references. The chlorophyll content were measured with SPAD chlorophyll meter. The average accuracy of leaf color prediction were 81.48% for sweet corn and 76.82% for hybrid corn under various field luminance, while the accuracy were 94.45% under a fixed 1500 lux luminance. The accuracy of chlorophyll prediction was very low with and R2 = 0.4762 for sweet corn and R2 = 0.5284 with national ID card references for hybrid corn. This low results were relatively contrast with the prediction from manual measurement of leaf color level, where the correlation coefficients were R2 = 0.8466 for sweet corn and R2 = 0.8506 for hybrid corn.
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