scholarly journals Machine Learning For Agribusiness using GIS

2019 ◽  
Vol 8 (2) ◽  
pp. 1249-1251

In present days we have discussed about the emerging concept of smart agriculture that makes agriculture more efficient, effective and farmers save money and time with the help of high precision algorithms and Geographic Information System (GIS).The component that drives it is GIS with Machine Learning the logical field that enables machines to learn without being carefully customized. It has developed together with huge information advances and elite registering to make new chances to disentangle, measures, and comprehends information concentrated procedures in farming operational conditions. For instance, ranchers use accuracy GPS on the field spare manure. Ranchers use precision agribusiness since they can lessen the proportion of manure fertilizer. Moreover, satellites and robots assemble vegetation, topography and atmosphere information from the sky. This information can go into developing maps for better fundamental activity.

In present days we have discussed about the emerging concept of smart agriculture that makes agriculture more efficient, effective and farmers save money and time with the help of high precision algorithms and Geographic Information System (GIS).The component that drives it is GIS with Machine Learning the logical field that enables machines to learn without being carefully customized. It has developed together with huge information advances and elite registering to make new chances to disentangle, measures, and comprehends information concentrated procedures in farming operational conditions. For instance, ranchers use accuracy GPS on the field spare manure. Ranchers use precision agribusiness since they can lessen the proportion of manure fertilizer. Moreover, satellites and robots assemble vegetation, topography and atmosphere information from the sky. This information can go into developing maps for better fundamental activity.


Author(s):  
Alaeddine Moussa ◽  
Sébastien Fournier ◽  
Bernard Espinasse

Data is the central element of a geographic information system (GIS) and its cost is often high because of the substantial investment that allows its production. However, these data are often restricted to a service or a category of users. This has highlighted the need to propose and optimize the means of enriching spatial information relevant to a larger number of users. In this chapter, a data enrichment approach that integrates recent advances in machine learning; more precisely, the use of deep learning to optimize the enrichment of GDBs is proposed, specifically, during the topic identification phase. The evaluation of the approach was completed showing its performance.


2018 ◽  
Vol 2 ◽  
pp. 223
Author(s):  
Humam Zarodi

<p>Erupsi Gunungapi Merapi tahun 2010 mengakibatkan banyak korban jiwa, kerusakan aset dan kerugian di berbagai bidang. Untuk meminimalkan korban jiwa, kerusakan dan kerugian, diperlukan upaya pengurangan risiko bencana (PRB). Salah satu upaya yang dilakukan adalah program desa bersaudara (<em>sister village</em>) yang digagas oleh Pemerintah Kabupaten Magelang melalui Badan Penanggulangan Bencana Daerah (BPBD). Program desa bersaudara ini bertujuan agar ada kepastian tempat pengungsian, mengurangi kesemrawutan proses pengungsian serta memudahkan pelayanan pengungsi. Program ini dapat memanfaatan Sistem Informasi Geografis/<em>Geographic Information System</em> (GIS) yang berbasis web (<em>WebGIS</em>). <em>WebGIS</em> mampu mendiseminasikan peta yang dihasilkan dalam program desa bersaudara, misalnya peta jalur evakuasi. Makalah ini bertujuan untuk mendiskripsikan pemanfataan <em>WebGIS</em> dalam mendukung program desa bersaudara, dengan mengambil kasus di Desa Ngargomulyo (desa rawan bencana) dan Desa Tamanagung (desa penyangga/ penerima pengungsi). Metodenya adalah memaparkan proses pemetaan jalur evakuasi. Proses penyusunan peta tersebut terbagi empat tahap:   survei lapangan, penyiapan data spasial, coding dan publikasi. Hasilnya adalah tampilan peta jalur evakuasi yang bisa diakses oleh siapapun tanpa menggunakan aplikasi GIS yang memudahkan masyarakat pengungsi, penerima pengungsi, pemerintah maupun parapihak, mengetahui asal pengungsi, jalur evakuasi dan titik pengungsian. Penelitian ini menyimpulkan bahwa pemetaan <em>WebGIS</em> dapat mendukung upaya PRB dengan keunggulan bisa dijangkau pengguna secara sangat luas.<strong></strong></p><p><strong>Kata kunci</strong>: desa bersaudara, <em>sister village</em>, pemetaan jalur evakuasi, <em>gis</em>, <em>webgis</em></p>


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