Judul: Estimasi Nitrogen Tanaman Padi berdasarkan Reflectance Daun Menggunakan Artificial Neural Network
Abstrak: Nitrogen (N) merupakan salah satu nutrisi yang dibutuhkan tanaman dalam jumlah yang banyak. Ketersediaan N dalam tanaman perlu diestimasi sebelum pemupukan untuk menentukan tingkat pemberian pupuk N yang tepat. Penelitian ini bertujuan untuk mengestimasi kadar N padi (Oryza sativa, sp.) berdasarkan reflectance daun menggunakan Artificial Neural Network (ANN). Pada penelitian ini, 45 sampel daun diambil secara acak pada kondisi lingkungan yang beragam. Reflectance daun diukur menggunakan handheld spectroradiometer sedangkan kadar N aktual daun diukur menggunakan metode Kjeldahl. Data spectral reflectance pada saluran visible (daerah panjang gelombang 400−700 nm) dan kadar N aktual daun digunakan sebagai data input dan target dalam pembuatan model ANN. Teknik k-fold cross validation (k=3) digunakan untuk memilih model yang paling baik dan memperkirakan performa model secara keseluruhan. Hasilnya menunjukan model ANN dengan 17 neuron hidden layer secara relatif dapat mengestimasi N dengan baik. Hal ini dibuktikan dengan nilai root mean square error (RMSE) terkecil sebesar 0.23 dan akurasi tertinggi sebesar 93%. Penelitian ini menjanjikan untuk membantu petani untuk memprediksi kadar N tanaman padi sehingga mengoptimalkan pemberian pupuk N.
Keyword: ANN, Nitrogen, Padi, Reflectance, Kadar nitrogen, Warna daun, reflectance

Judul: Development of Empirical Model for Estimation of Rice Nitrogen Accumulation Based on Sentinel 2 Satellite Imagery
Abstrak: Plant Nitrogen Accumulation (PNA) merupakan jumlah total (akumulasi) N dalam tajuk tanaman per satuan luas tanah. PNA adalah sebuah indikator yang menunjukkan status N pada tanaman. Penelitian ini mengusulkan sebuah model empiris untuk menghitung PNA pada lahan sawah yang luas, dengan cepat dan dekat waktu nyata berdasarkan data Sentinel 2. Hasil analisis terhadap hubungan indeks vegetasi dengan data PNA sampel tanaman padi yang diambil dari plot-plot percobaan menunjukkan bahwa status N tanaman padi dapat dideteksi secara dekat waktu nyata menggunakan data Sentinel 2. Analisis statistik juga menunjukkan bahwa variasi temporal dan spasial indeks PNA tanaman padi pada perlakuan teknik tanam dan varietas yang berbeda dapat diestimasi berdasarkan indeks vegetasi dari Sentinel 2. Analisis hubungan data pengukuran PNA tanaman padi dengan delapan metode indeks vegetasi, membuktikan bahwa perhitungan PNA berbasis NDRE merupakan model optimum yang ditunjukkan oleh nilai R2 = 75.17%, RMSE = 22.5 kg/ha, dan akurasi = 80.80% terhadap data training. Validasi model perhitungan PNA berbasis NDRE memiliki performa yang baik dengan 65.08%, RMSE = 45.2 kg/ha, dan akurasi = 74.22%. Model PNA ini telah digunakan untuk memetakan distribusi spasial status N tanaman padi yang berbeda waktu tanaman pada lahan baku sawah Kecamatan Rawamerta, Kabupaten Karawang. Hasil penelitian telah berkontribusi terhadap penerapan prinsip-prinsip pertanian presisi, dengan memberikan landasan penting bagi pengembangan sistem rekomendasi pemupukan berbasis teknologi penginderaan jauh., Plant Nitrogen Accumulation (PNA) is the total amount (accumulation) of N in the plant canopy per unit area of land. PNA is an indicator that shows the N status in plants. This study proposes a method for computing PNA over large areas of rice field, faster and in near real-time based on Sentinel 2 data. The research was proven that the N status of rice plants can be detected in real time using Sentinel 2 data, through empirical analysis of the relationship between the Sentinel 2-based vegetation indices and PNA of rice taken from the experimental plots. Statistical analysis was shown that the temporal and spatial variations of the PNA of rice on different planting techniques and varieties can be determined based on the vegetation indices. Empirical analysis of the PNA based on eight vegetation indices methods were proven that the NDRE-based PNA model was an optimum model, indicated by the value of R2 = 75.17%, RMSE = 22.5 kg/ha, and accuracy = 80.80% to the training data. The performance indicators of the PNA model were calculated with the results of R2 = 65.08%, RMSE = 45.2 kg/ha, and accuracy = 74.22%. The PNA model has been used to map the spatial distribution of the N status of rice plants at different planting stages over rice field area in Rawamerta District, Karawang Regency. The research results have contributed to the application of the principles of precision agriculture, through providing an important basis for the development of a fertilization recommendation system based on remote sensing technology.
Keyword: near real-time measurement, plant nitrogen accumulation, planting technique, rice varieties, vegetation indices

Judul: Distribution and Diversity of Ectoparasite Mites on Geckos in Indonesia
Abstrak: Data on the diversity and dispersal of parasitic mites on house geckos in Indonesia are very scarce. In this work, the distribution and diversity of mites living on three species of house geckos, namely Cosymbotus platyurus, Hemidactylus frenatus, and H. garnotii collected throughout Indonesia, has been elaborated. Geckos and mites were captured and immediately preserved in 70% ethanol. Whole mount of the mites was prepared by clearing in lactophenol followed by mounting on polyvinyl lactophenol solutions. The SEM preparation was conducted to examine the detail morphological characters of the mites. The results showed that among 448 individuals of geckos, 221 geckos were infected by Geckobia mites. Prevalences of C. platyurus, H. frenatus, and H. garnotii infested by mites were 14.8%, 50.69%, and 79.6%, respectively. Three different spesies of Geckobia (G1, G2, and G3) could be differentiated; and based on similarities of their morphological characters to ones described in published literatures, Geckobia G2 is Geckobia glebosum and Geckobia G3 is Geckobia bataviensis while G1 could not be identified to the species level. The highest mean intensity of Geckobia G1 infestation was found on H. garnotii (I=7.0), G. glebosum infestation on H. frenatus (I=3.5), and G. bataviensis infestation on H. garnotii (I=11.8). In general, C. platyurus was infested by the least number of mites. Geckobia G1 were found living on the skin folds on the claws, G. glebosum were found mainly on the body and thigh, and G. bataviensis were found on almost all parts of the host’s body. Geckobia mites are distributed randomly throughout Indonesian Archipelago, following the pattern of distribution of their hosts. So it is concluded that Geckobia G1, G. glebosum, and G. bataviensis are sympatric.
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