Survey of Rice Pests, Diseases and Natural Enemies on “Upsus” Program in Karawang District, West Java Province

"Upsus" (Special Efforts) Program is a program to increase crop production and productivity to support the acceleration of food self-sufficiency held by the government. The targets are to increase the planting index (IP) by 0.5 and productivity by 0.3 ton/ha/Harvested Dry Grain (HDG). Increased productivity has not been reached optimally. This research aimed to determine the number/types of the dominant pests and natural enemies, and pests and diseases attack rates in the wetland rice plantation "Upsus" Program in Karawang District. Direct observation was carried out on four stages of plant development, in the nursery stage (10–14 days after sowing) 200–300 m², seedlings stage (10–20 days after planting), vegetative stage (5–6 weeks after planting), and reproductive stage (1–2 weeks after flowering) respectively within an area of 2000 m². Twenty samples were observed in the nursery stage and 50 samples in the following stages. The dominant pests and diseases found were Nilaparvata lugens, Leptocarisa oratorius, Schirpophaga incertulas, Cnaphalocrocis medinalis, Scotinophara coarctata, Mythimna separata, bacterial leaf blight (Xanthomonas. oryzae pv. oryzae), blast (Pyricularia grisea) and narrow brown spot (Cercospora oryzae). The recorded dominant natural enemies were Cyrtorhinus sp., Paederus sp., Tetragnatha sp., and Pardosa pseudoannulata. S. incertulas showed the highest attack intensity and the highest disease severity was found in bacterial leaf blight. The largest population of dominant pests and natural enemies was found in the generative stage. The high application of pesticides affected the population of pests, natural enemies, and the level of pest and disease attacks.

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Identification of Bacterial Leaf Blight and Brown Spot Disease In Rice Plants With Image Processing Approach

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika ◽

10.26555/jiteki.v5i2.14136 ◽

2020 ◽

Vol 5 (2) ◽

pp. 59

Author(s):

Ihsanuddin Ihsan ◽

Eka Wahyu Hidayat ◽

Alam Rahmatulloh

Keyword(s):

Image Processing ◽

Leaf Blight ◽

Brown Spot ◽

Bacterial Leaf Blight ◽

Spot Disease ◽

Rice Plants ◽

Processing Approach ◽

Brown Spot Disease

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Detection of Rise Damage by Leaf Folder (Cnaphalocrocis medinalis) Using Unmanned Aerial Vehicle Based Hyperspectral Data

Sustainability ◽

10.3390/su12229343 ◽

2020 ◽

Vol 12 (22) ◽

pp. 9343

Author(s):

Tao Liu ◽

Tiezhu Shi ◽

Huan Zhang ◽

Chao Wu

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicle ◽

Spectral Index ◽

Crop Production ◽

Leaf Roll ◽

Hyperspectral Data ◽

Cnaphalocrocis Medinalis ◽

Crop Pests ◽

Pests And Diseases ◽

Aerial Vehicle

Crop pests and diseases are key factors that damage crop production and threaten food security. Remote sensing techniques may provide an objective and effective alternative for automatic detection of crop pests and diseases. However, ground-based spectroscopic or imaging sensors may be limited in practically guiding the precision application and reduction of pesticide. Therefore, this study developed an unmanned aerial vehicle (UAV)-based remote sensing system to detect leaf folder (Cnaphalocrocis medinalis). Rice canopy reflectance spectra were obtained in the booting growth stage by using the UAV-based hyperspectral remote sensor. Newly developed and published multivariate spectral indices were initially calculated to estimate leaf-roll rates. The newly developed two-band spectral index (R490−R470), three-band spectral index (R400−R470)/(R400−R490), and published spectral index photochemical reflectance index (R550−R531)/(R550+R531) showed good applicability for estimating leaf-roll rates. The newly developed UAV-based micro hyperspectral system had potential in detecting rice stress induced by leaf folder. The newly developed spectral index (R490−R470) and (R400−R470)/(R400−R490) might be recommended as an indicator for estimating leaf-roll rates in the study area, and (R550−R531)/(R550+R531) might serve as a universal spectral index for monitoring leaf folder.

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Keberlanjutan Inovasi Teknologi Lahan Rawa Pasang Surut : Prospek, Kendala dan Implementasi

Jurnal Sumberdaya Lahan ◽

10.21082/jsdl.v12n2.2018.117-131 ◽

2020 ◽

Vol 12 (2) ◽

pp. 117

Author(s):

Mamat Haris Suwanda ◽

Muhammad Noor

Keyword(s):

Water Management ◽

Crop Production ◽

Land Preparation ◽

Post Harvest ◽

Pests And Diseases ◽

Tidal Swamp ◽

Development Technology ◽

Increase Food Production ◽

The Government ◽

Institutional Model

Abstrak. Masalah pangan yang dihadapi semakin berat dan kompleksnya ditengah semakin meningkatnya jumlah populasi penduduk dan konversi lahan. Sejak tahun 2018 melalui Program UPSUS PAJALE dan Program SERASI pemerintah di bawah Kementerian Pertanian berupaya meningkatkan produksi pangan, khsusunya beras secara nasional. Dari luas 20,11 juta hektar lahan rawa pasang surut berpotensi sebagai sumber produksi padi seluas 9,53 juta hektar. Jika 50% saja dari lahan yang cocok tersebut dimanfaatkan, maka dapat dihasilkan sekitar 14,295 juta ton GKG per tahun. Dukungan ilmu pengetahuan dan teknologi untuk pengembangan lahan rawa pasang surut hasil penelitian dan pengkajian cukup tersedia, baik dalam upaya peningkatan produktivitas dan produksi maupun peningkatan kapasitas dan kelembagaan petani. Implementasi hasil-hasil penelitian tersebut dalam skala luas (scalling up) memerlukan dukungan pemerintah baik pusat maupun daerah. Berdasarkan hasil penelitian dalam meningkatkan produksi tanaman pangan, khususnya padi diperlukan 10 komponen teknologi meliputi : (1) pengelolaan air, (2) penyiapan lahan dan, (3) pengolahan tanah, (4) penataan lahan, (5) ameliorasi, (6) pemupukan, (7) penggunaan varietas adaptif, (8) sistem tanam dan jarak tanam, (9) pengendalian hama dan penyakit tanaman, dan (10) panen dan pasca panen. Tulisan ini bertujuan untuk mengemukakan tentang perspektif keberlanjutan dari inovasi teknologi lahan rawa pasang surut terkait dengan potensi dan kendalanya dalam implementasi ke depan. Masalah pokok yang menghambat implementasi teknologi terdiri paling tidak atas tiga hal yaitu (1) kesesuaian teknis dari teknologi tersebut dengan kemampuan teknis petani, (2) kesesuaian teknis dari teknologi tersebut dengan sosial budaya petani setempat, (3) dukungan dari aspek kelembagaan petani dan pemerintah, termasuk pemerintah daerah. Keberlanjutan inovasi teknologi pertanian lahan rawa pada umumnya paling tidak ditentukan minimal oleh tiga aspek atau dimensi, yaitu aspek ekonomi, sosial dan aspek lingkungan. Secara garis besar, maka implementasi keberlanjutan teknologi pengembangan rawa, termasuk keberhasilan Program SERASI hanya dapat dicapai melalui (1) penyiapan infrastruktur pengelolaan air yang tepat sebagai kunci; (2) paket teknologi yang lengkap dan spesifik lokasi dari komponen teknologi penyiapan bibit, penyemaian sampai komponen pasca-panen (pengemasan), dan (3) model kelembagaan menyeluruh dari penyuluhan, permodalan, pengelolaan air, budidaya, pengolahan hasil, pemasaran, dan penyediaan sarana dan prarana produksi, termasuk alsintan. Abstract. The problem of food faced is increasingly heavy and complex amidst the increasing population and land conversion. Since 2018 through the UPSUS PAJALE Program and the SERASI Program the government under the Ministry of Agriculture has sought to increase food production, especially rice nationally. Of the total 20.11 million hectares of tidal swamp land potentially as a source of rice production, 9.53 million hectares. If only 50% of the suitable land is utilized, it can produce around 14,295 million tons per year. The support of science and technology for the development of tidal swamp land from the results of research and studies is quite available both in an effort to increase productivity and production and increase the capacity and institutional capacity of farmers. In the implementation of scaling up results, the support of the central and regional governments is needed. The results of the research in increasing food crop production, especially rice showed that 10 components of technology were needed including : (1) water management, (2) land preparation and, (3) soil management, (4) land management, (5) amelioration, (6) fertilization , (7) use of adaptive varieties, (8) planting systems and spacing, (9) control of plant pests and diseases, and (10) harvest and post-harvest. This paper aims to suggest the sustainability perspective of tidal swamp technology innovation related to the potential and constraints in future implementation. The main problems that hinder the implementation of technology comprise at least three things, namely (1) the technical suitability of the technology with the farmers' technical capabilities, (2) the technical suitability of the technology with the local socio-cultural farmers, (3) support from the institutional and farm , including local government. The sustainability of technological innovation in swamp farming in general is at least determined by at least three aspects or dimensions, namely economic, social and environmental aspects. Broadly speaking, the implementation of the sustainability of swamp development technology, including the success of the SERASI Program can only be achieved through (1) the preparation of appropriate water management infrastructure; (2) complete and site-specific technology packages from the components of seedling preparation technology, seeding to post-harvest components (packaging), and (3) a comprehensive institutional model of extension, capital, water management, cultivation, yield processing, marketing, and provision facilities and production facilities, including Alsintan.

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Penerapan Algoritma Convolutional Neural Network Dan Arsitektur MobileNet Pada Aplikasi Deteksi Penyakit Daun Padi

Swabumi ◽

10.31294/swabumi.v9i2.11678 ◽

2021 ◽

Vol 9 (2) ◽

pp. 184-188

Author(s):

Rizal Amegia Saputra ◽

Sri Wasiyanti ◽

Adi Supriyatna ◽

Dede Firmansyah Saefudin

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Confusion Matrix ◽

Leaf Blight ◽

Brown Spot ◽

Bacterial Leaf Blight ◽

Leaf Smut

Padi merupakan tanaman pangan penghasil beras, dan indonesia merupakan negera yang mayoritas penduduknya menjadikan beras sebagai makanan utama, jumlah penduduk yang semakin meningkat, perlu menjaga kualitas padi agar resiko gagal panen dapat dihindari. Banyak faktor yang dapat menimbulkan resiko gagal panen salahsatunya itu penyakit daun padi, Pada penelitian ini diusulkan Algoritma Convolutional Neural Network untuk klasifikasi penyakit daun padi yang berdasarkan citra. Arsitektur yang digunakan pada penelitian ini menggunakan MobileNetVI dengan menggunakan ekstraksi fitur. Dataset berasal dari UCI Repository sebanyak 120 yang terdiri dari 3 penyakit daun padi yaitu Bacterial leaf blight, Brown spot, Leaf smut. Berdasarkan hasil pelatihan dan pengujian menggunakan citra penyakit daun padi yang berukuran 224x224 piksel didapat hasil nilai akurasi pelatihan mencapai 1.0 dan nilai akurasi validasi mencapai 0.8333. Nilai akurasi pada Confusion Matrix yaitu sebesar 92%, hasil ini menjadi bukti bahwa dengan penerepan algorima CNN dan MobileNetVI dengan ekstraksi ciri memiliki akurasi yang baik sekali. Percobaan pada aplikasi yang dibangun hasil proses pengujian berbasis android terbukti dapa mengklasifikasikan jenis penyakit daun padi.

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Detection of Rice Plants Diseases Using Convolutional Neural Network (CNN)

Proceeding International Conference on Science and Engineering ◽

10.14421/icse.v3.535 ◽

2020 ◽

Vol 3 ◽

pp. 393-396

Author(s):

Achmad Ramadhanna’il Rasjava ◽

Aditya Wisnugraha Sugiyarto ◽

Yori Kurniasari ◽

Syaifullah Yusuf Ramadhan

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Convolutional Neural Network ◽

Rice Plant ◽

Leaf Blight ◽

Rice Production ◽

Training Data ◽

Brown Spot ◽

Bacterial Leaf Blight ◽

Rice Plants

As a rice-producing plant, rice plant (Oryza sativa L.) is one of the most important crops in Indonesia. Rice production is increasing every year along with an increase in rice demand and population.The amount of rice production is affected by the condition of the rice plants. The worse the condition of rice plants, the rice production will also lower. Rice plant is very susceptible to diseases or pests that can reduce its productivity, including brown spot disease, leaf smut and bacterial leaf blight. As the development of science and technology, currently known as Artificial Intelligence. Artificial intelligence is a combination of several scientific disciplines such as mathematics, statistics, computer science, and even social science. Using artificial intelligence, the system now have the ability to interpret external data correctly to learn from the data and then use the learning to achieve certain goals through flexible adaptation. The artificial intelligence fields consists of several branches, such as machine learning and deep learning. Neural Network (NN) is one of the methods used in the deep learning.NN has many types, one of which is the Convolutional Neural Network (CNN). CNN is the best-knownmethod used for processingimages data compared to other types of NN. Therefore, in this study the identification of rice plants diseases was carriedout using CNN method. From this study,better results were obtained compared to other methods, obtaining 100% accuracy for training data and 86,67% for testing data. The model obtained by the CNN method can be used for detecting 3 different types of rice plants diseases, there are brown spots, leaf smuts, or bacterial leaf blight disease based on the physical images of rice plant leaves.

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The Agrarian Question in Contemporary Zimbabwe

Africanus Journal of Development Studies ◽

10.25159/2663-6522/5713 ◽

2019 ◽

Vol 49 (1) ◽

Author(s):

Toendepi Shonhe

Keyword(s):

Capital Accumulation ◽

Crop Production ◽

Job Creation ◽

Food Crop ◽

Contract Farming ◽

Agrarian Question ◽

Food Crop Production ◽

The Government ◽

Agrarian Transition ◽

Depth Interviews

The reinvestment of rural agrarian surplus is driving capital accumulation in Zimbabwe's countryside, providing a scope to foster national (re-) industrialisation and job creation. Contrary to Bernstein's view, the Agrarian Question on capital remains unresolved in Southern Africa. Even though export finance, accessed through contract farming, provides an impetus for export cash crop production, and the government-mediated command agriculture supports food crop production, the reinvestment of proceeds from the sale of agricultural commodities is now driving capital accumulation. Drawing from empirical data, gathered through surveys and in-depth interviews from Hwedza district and Mvurwi farming area in Mazowe district in Zimbabwe, the findings of this study revealed the pre-eminence of the Agrarian Question, linked to an ongoing agrarian transition in Zimbabwe. This agrarian capital elaborates rural-urban interconnections and economic development, following two decades of de-industrialisation in Zimbabwe.

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