SMART FARMING 4.0 UNTUK MEWUJUDKAN PERTANIAN INDONESIA MAJU, MANDIRI, DAN MODERN

Smart farming 4.0 based on artificial intelligence is a flagship launched by the Ministry of Agriculture. Smart farming 4.0 encourages the farmers to work more efficient, measurable, and integrated. Through technology, farmers are able to carry out farm practice by relying on mechanization, not on the planting season, from planting to harvesting accurately. Several smart farming technologies such as blockchain for modern off farm agriculture, agri drone sprayer, drone surveillance (drone for land mapping), soil and weather sensors, intelligent irrigation systems, Agriculture War Room (AWR), siscrop (information systems) 1.0 have been implemented in some areas. However, farmers deal with various educational backgrounds, aging farmers phenomenon, and high cost of smart farming technology tools to implement smart farming. This paper aims to analyze the huge opportunities of smart farming by utilizing the potential of millennial farmers as actors and analyzing various government policies to support smart farming 4.0. The Ministry of PDTT has carried out pilot projects to implement smart farming in several locations. The Ministry of Agriculture also needs to play a role by creating a smart farming roadmap. The Government's Strategic Project 2020–2024 through food estate based on farmer corporations may support massive smart farming applications

Co-designing climate-smart farming systems with local stakeholders: A methodological framework for achieving large-scale change

The literature is increasing on how to prioritize climate-smart options with stakeholders but relatively few examples exist on how to co-design climate-smart farming systems with them, in particular with smallholder farmers. This article presents a methodological framework to co-design climate-smart farming systems with local stakeholders (farmers, scientists, NGOs) so that large-scale change can be achieved. This framework is based on the lessons learned during a research project conducted in Honduras and Colombia from 2015 to 2017. Seven phases are suggested to engage a process of co-conception of climate-smart farming systems that might enable implementation at scale: (1) “exploration of the initial situation,” which identifies local stakeholders potentially interested in being involved in the process, existing farming systems, and specific constraints to the implementation of climate-smart agriculture (CSA); (2) “co-definition of an innovation platform,” which defines the structure and the rules of functioning for a platform favoring the involvement of local stakeholders in the process; (3) “shared diagnosis,” which defines the main challenges to be solved by the innovation platform; (4) “identification and ex ante assessment of new farming systems,” which assess the potential performances of solutions prioritized by the members of the innovation platform under CSA pillars; (5) “experimentation,” which tests the prioritized solutions on-farm; (6) “assessment of the co-design process of climate-smart farming systems,” which validates the ability of the process to reach its initial objectives, particularly in terms of new farming systems but also in terms of capacity building; and (7) “definition of strategies for scaling up/out,” which addresses the scaling of the co-design process. For each phase, specific tools or methodologies are used: focus groups, social network analysis, theory of change, life-cycle assessment, and on-farm experiments. Each phase is illustrated with results obtained in Colombia or Honduras

Smart farming

Australia has always been a world leader in agricultural innovation. Our farmers, supported by researchers, industry groups and other stakeholders, remain at the global forefront of the invention and adoption of technologies. This enthusiasm for change and innovation has helped Australian agriculture to retain its competitive edge over other producers. Technological advances will be even more important to the future of Australian agriculture. The sector is a part of the broader boom in innovation across the Australian economy. Meanwhile, new technologies will support farm businesses to tackle heightened regional competition, growing resource scarcity, and other challenges. The agriculture sector must be able to make the most of the innovation boom in order to support productivity growth and to maintain its competitiveness. At the core of the agricultural innovation boom are individual farm businesses that make decisions to adopt new technologies. If the Government wishes to support innovation and growth, it must support these businesses in technology adoption

Review on Internet of Things (IOT) in agriculture: limitations and barriers of smart farming in oil palm plantation in Malaysia / Ahmad Safwan Abu Bakar

Smart farming or precision farming is not yet fully implemented in oil palm plantation. The component of smart farming includes the usage of wireless internet and Global Positioning System which connected with drone, machinery and equipment without visiting the farm. In some research, smart farming is stated to become the future of agriculture sector in which helps to overcome several problems nowadays. For example, labour shortage for plantation sector will cause the usage of smart farming to be implement in the next 10-15 years. The main of causes for this problem is because of high demand and supply for palm oil product. Oil palm production has brought about unlimited economic profits and currently it is an emerging economic sector in Malaysia. At present, Malaysia accounts for an overwhelming contribution to world's palm oil production and export which is 39% and 44%, respectively.. However the implementation of smart farming is restricted on several factors. This study is attempt to discuss current scenario of limitations and barriers of smart farming on oil palm plantation in Malaysia which causes by weak imperatives for change, interoperability of different standards and connectivity in rural areas

Climate Smart Farming for Women in East Africa

According to the United Nations Food and Agriculture Organization, 60% of East Africans live as subsistence farmers. This population is particularly vulnerable to the effects of climate change which has increased the duration and intensity of droughts and floods. Droughts and floods can destroy an entire season’s harvest, causing sustenance farmers and their families to struggle for food until the next season. In an attempt to mitigate the severe effects of climate change on these farmers and reduce food insecurity in East Africa, the team has designed a small-scale aquaponic farming system that simultaneously grows fish and vegetables. This system is founded on sustainability, as aquaponics uses significantly less water to grow crops than traditional farming, making it more resilient to both severe droughts and floods, the system also does not rely on external fertilizers, and it uses recycled materials as often as possible. This aquaponic system was designed for women’s collectives in East Africa who requested help in building a portfolio of projects that they can teach to women in rural East Africa. These women’s organizations work in rural villages throughout Uganda and Kenya to help local women and their families adapt to the changing climate. Currently, their efforts have been focused on improving the quality and supply of water in the villages by constructing latrines, water filters, and rainwater catchment systems. During the 2017-2018 academic year, team members designed and built the aquaponic system in Santa Clara, California, then deployed the first prototype in Kampala, Uganda, and trained several of the collective’s leaders how to build and operate the system

A module placement scheme for fog-based smart farming applications

As in Industry 4.0 era, the impact of the internet of things (IoT) on the advancement of the agricultural sector is constantly increasing. IoT enables automation, precision, and efficiency in traditional farming methods, opening up new possibilities for agricultural advancement. Furthermore, many IoT-based smart farming systems are designed based on fog and edge architecture. Fog computing provides computing, storage, and networking services to latency-sensitive applications (such as Agribots-agricultural robots-drones, and IoT-based healthcare monitoring systems), instead of sending data to the cloud. However, due to the limited computing and storage resources of fog nodes used in smart farming, designing a modules placement scheme for resources management is a major challenge for fog based smart farming applications. In this paper, our proposed module placement algorithm aims to achieve efficient resource utilization of fog nodes and reduce application delay and network usage in Fog-based smart farming applications. To evaluate the efficacy of our proposal, the simulation was done using iFogSim. Results show that the proposed approach is able to achieve significant reductions in latency and network usage

Paddy Rice Smart Farming

It is anticipated that machine learning (ML) and the internet of things (IoT) would significantly impact smart farming and engage the entire supply chain, in particular for the production of rice. Rice smart farming offers new capabilities to foresee changes and find possibilities thanks to the growing amount and variety of data gathered and obtained by emerging technologies in the Internet of Things (IoT). The accuracy of the models created through the use of ML algorithms is significantly impacted by the quality of the data obtained from sensor readings. These three components, machine learning (ML), the internet of things (IoT), and agriculture have been used extensively to improve all aspects of rice production processes in agriculture. As a result, traditional rice farming practices have been transformed into a new era known as rice smart farming or rice precision agriculture. We do a study of the most recent research that has been done on the application of intelligent data processing technology in agriculture, namely in the production of rice, in this paper. We analyze the applications of machine learning in a variety of scenarios, including smart irrigation for paddy rice, predicting paddy rice yield estimation, predicting droughts and floods, monitoring paddy rice disease, and paddy rice sample classification. In each of these scenarios, we describe the data that was captured and elaborate on the role that machine learning algorithms play in paddy rice smart agriculture. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modeling, and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice.

MENANAM KOLEKSI TANAMAN OBAT DI DESA SUBUR KECAMATAN AIR JOMAN KABUPATEN ASAHAN

Smart farming can simply be interpreted as precision agriculture or proper farming, because it can identify the conditions and needs of each plant. From this identification, farmers will better understand what actions to take on each plant. Which plants need water, which plants should be applied with pesticides, and which plants should be fertilized. Medicinal plants are plants that have been identified and are known based on human observations to have compounds that are useful for preventing and curing diseases, performing certain biological functions, and preventing insect and fungal attacks. This activity aims to provide knowledge and arouse the enthusiasm for planting medicinal plants to support smart farming in the creation of a collection of medicinal plants that can be used by the community to maintain body resistance. The methods used in PKM activities are lectures, discussions, demonstrations of direct practice of medicinal plant cultivation, as well as technical guidance and assistance. The result of this PKM is that there is an increase in knowledge of about 70 percent compared to when the smart farming technology was not implemented and the demonstration of the direct practice of planting medicinal plant collections. Keywords: Medicinal plants, smart farming, proper farmin