2021 IPB-FFTC International Online Workshop & Seminar
Speakers | 2021 IPB-FFTC International Online Workshop
Ph.D., Associate Professor, Kyushu University, Department of Agro-environmental Sciences.
Plant Phenotyping Technology to Enhance Smart Farming
Plant growth behavior is strongly influenced not only by gene property but also by environmental factors in general. Image processing and analyzing methods and tools are effective to extract biological, physiological, and ecological features of plants. By developing and expanding IoT (Internet of Things) and AI (Artificial Intelligence) technologies, high throughput plant phenotyping focusing on the comprehensive assessment of complex plant features such as growth, physical property, and yield have been also developed (Roberto, Aluízio, 2015) up to present. These researches will be actually utilized to solve problems related to food and biomass production under drastic change of climate, global warming, and increase in the global population. However, typical high throughput plant phenotyping systems are very expensive and then its introduction is limited. Therefore, development of the affordable system is required to improve the researches in this field. Authors have developed high throughput plant phenotyping platform based on affordable IoT devices and opensource software. The performance of the platform has been verified by feasibility study on plant growth feature extraction using leafy vegetable “spinaches”. On the other hand, number of flowers and fruits, and the ripening process and stage are also important plant growth feature. We have also applied deep learning methods to extract and track the features of plants. These features were used to optimize cultivation technique and evaluate the effectiveness of introduction of environmental control facilities in a greenhouse. In this workshop, several plant phenotyping technologies to enhance smart farming mentioned above will be introduced.
Ph.D, Japan International Research Center for Agricultural Sciences (JIRCAS)
Weed mapping with low-cost, small UAV for smallholder farming
Weed control is a common issue for the crop production. Timely detection of weeds in field is the first step of site-specific weed management. Nowadays, small unmanned aerial vehicles (UAV, or ‘drone’) have been introduced and adopted in various agricultural applications. Their high-resolution images also offer a potential alternative for early weed detection at the farm scale. However, conventional pixel-based image analysis does not always produce the best results and object-based image analysis (OBIA) has improved weed discrimination accuracy. In this paper, we introduce our current activities for weed detection at upland rice fields in Laos using small UAV with the simple linear iterative clustering (SLIC) and random forest (RF), and then, we discuss the performance and applicability to smallholder faming. The UAV images were obtained every two weeks in the growing season at a upland rice field in Laos. In the initial growth stage (days after sowing [DAS] = 29), we tested different combinations of input features for SLIC algorithm: three color spaces (red-green-blue [RGB], hue-saturation-brightness [HSV], CIE-L*a*b*), canopy height model (CHM), spatial texture (Texture) and four vegetation indices (excess green [exG], excess red [exR], green-red vegetation index [GRVI] and color index of vegetation extraction [CIVE]). Among the input features, the HSV+Texture combination used model showed the best performance with the highest out-of-bag (OOB) accuracy (0.915). Based on the SLIC-RF model, the OOB accuracy ranged between 0.915 (DAS 29) and 0.967 (DAS 42). These results suggest that with the SLIC-RF algorithm using HSV+Texture features, weeds can be mapped at the fields, using consumer grade UAV images with acceptable accuracy to meet the needs of site-specific weed management through the plant growing periods in upland rice field.
Professor, Department of Regional Agricultural Engineering, University of the Ryukyus (Japan)
Improvement of subtropical agriculture of sugarcane cultivation using ICT in Okinawa, Japan
In order to promote harvesting mechanization in the southern part of Okinawa island where there are many small-scale farmers, it is considered that a compact and high-performance small-sized sugarcane harvester is required than the currently used harvesters. In this research, we attached GNSS and on-board cameras to the smallest 36 kW harvester operating in Okinawa Prefecture and investigated the actual working condition of the harvester. The locus of harvester per second was recorded by GNSS and data was analyzed using GIS to analyze the mode of operation. From the travel locus of a GNSS device, it was possible to measure the area harvested with a small error. From the images of the on-board camera, it was confirmed that it was difficult to cut the stems due to the lodging of the sugarcane. The sugarcane could not be taken into the harvester, the reaping work stopped, and the working efficiency was lowered. Images were also analyzed to estimate the amount of the harvest. Working with smaller harvesters is important for sugarcane cultivation in Okinawa, where there are many small fields. Though the small-sized harvester used in this test is inferior to the popular small-sized machine in terms of performance, it is necessary to proceed with introduction in the southern part of the Okinawa island while considering work optimization methods using ICT.
Ph.D, Senior researcher, National Institute of Agricultural Science
Smart Farm R&D status and future direction in Korea
Korea’s agriculture has achieved productivity gains through the development of science and technology such as farmland expansion, improvement of varieties, pesticides, and fertilizers for over 100 years. In 21 century, Smart farms in Korea are focused on facility horticultural agriculture and livestock industry, and the first generation from 2014 to present, the second generation by 2030, and the third generation by 2040 are expected to be commercialized. The main function of the 1st generation is remote facility control with improved convenience, the 2nd generation aims at precise growth management by improving productivity, and the 3rd generation aims at full cycle automatic management using cutting-edge communication technology, robotics, big data and artificial intelligence. Ministry of Agriculture, Food and Rural Affairs, Rural Development Administration, and Ministry of Science and ICT started the smart farm multi-ministerial package innovation technology development project from 2021 to 2027. Through this, high productivity digital cultivation management technology, intelligent smart greenhouse integrated control technology, unmanned automation technology for greenhouse crop production, high productivity precision livestock management technology, intelligent livestock complex environment management technology, and unmanned autonomous smart livestock integrated control technology will be developed.
Roger Luyun Jr.
Professor, Institute of Agricultural and Biosystems Engineering, College of Engineering and Agro-industrial Technology, University of the Philippines Los Banos (IAE, CEAT, UPLB)
Future-proofing Philippine Agriculture with SARAI Technologies
Climate change has brought stronger typhoons that cause flooding during the rainy season and prolonged droughts in the dry season, as well as the attendant losses due to pests and diseases. Future-proofing Philippine agriculture requires developing means to minimize the effects of shocks and stresses of these future events. This can be achieved with the use of smart sensors, automation, the internet of things (IoT), information and communications technology (ICT), and many others. Project SARAI (Smarter Approaches to Reinvigorate Agriculture as an Industry in the Philippines), a DOST-PCAARRD-funded research program in the Philippines, is one of the local forerunners of promoting science-driven decision-making in the farm using smart technologies and management practices. The smart technologies developed through the project include the use of satellite remote sensing for agricultural monitoring, weather monitoring and forecasting, crop modeling, and farm-based decision support tools for soil and water management, and pest and disease management. The project aims to establish a national crop forecasting and monitoring network that will aid farmers, stakeholders, and policymakers to make informed decisions and strategies in strengthening local agriculture as a livelihood and industry. Through the initiatives of Project SARAI, the Philippine agriculture sector can be future-proofed to achieve sustainable production and increased resilience even with the impacts of climatic variability, disasters, the global pandemic, and other uncertainties that may threaten national food security.
Y. Aris Purwanto
Professor, Department of Mechanical and Biosystem Engineering, IPB University Indonesia
Portable Vis/near-infrared spectrometer for point-of-need quality assessment of agro-food products
The use of portable Vis/near-infrared (NIR) spectrometers is a promising technique for solving analytical problems in the quality assessment of agro-food products. Portable Vis/NIR spectrometer is an instrument offering several advantages for non-destructive, in situ analysis, small size, low cost, robustness, and simplicity of analysis. The emergence of portable Vis/NIR spectrometer allows the establishment of rapid protocols, which might increase quality control, implying that agro-food quality can be monitored along the entire supply chain. For the agro-food value chain, portable Vis/NIR at any point along the logistic chain could reduce the number of serious problems related to agro-food product’s quality and safety. This work discussed the development and potential of portable Vis/near-infrared in the quality assessment of agro-food products in Indonesia, such as predicting the quality of fruits, meat, aromatic oils, egg freshness, and adulteration of fresh milk.
Bayu Dwi Apri Nugroho
Ph.D , Faculty of Agricultural Technology, Universitas Gadjah Mada
Best Practice of Agri-tech Start-up for Farmers’ Welfare: Agri-Tech and Agribusiness Integration to Support Agricultural Ecosystem in Indonesia
Indonesia’s farmers face many challenges, from low productivity, pest attack, uncertainly commodities price, unscrupulous off-takers to difficulties in getting loans. One of the biggest challenges right now, however, is climate change. Speaking of weather conditions, level of weather predictions covers too wide area (per district or subdistrict). Meanwhile, micro-climate in village or land area is specific, sometimes every 2 or 3 km has different rainfall condition. Therefore, localized technology assuring precise weather prediction by installing localized weather station is needed. From technical innovativeness perspective, not only provide the current and raw data of what is happening on the farm to the farmers but notifications and recommendations precisely on what to do is more important because the conditions of Indonesian farmers that mostly are having low literacy capability, to give them precise and simple information is very beneficial for them. Access to the market is not the only problem that farmers have to face in terms of economic challenge. Farmers find it difficult to apply loan in the bank, thus they have to borrow the money from middlemen, which has the contract farming that farmers have to sell their product in a low price to them. So, not only technology but also ensure that farmers have access to the market by building agricultural ecosystem project. Agricultural Ecosystem project is a collaborating project among government, bank, insurance, agriculture inputs supplier, off-taker and also agri-tech start-up. By using technology and included in the agricultural ecosystem project, farmers can improve their productivity also certainty to market their products with fair price to a certain off-taker. Farmers also can be assured that if their production is damaged by natural disaster, they can claim to agriculture insurance provider. They also don’t have to worry about the agriculture input supply because they can obtain high quality products from the trusted supplier that collaborating in agricultural ecosystem project. Lastly, farmers also can receive micro financing from bank with low interest. Then, to help farmers gain higher sale price through using traceability feature. It can help them export their products and obtain more funding for their business since product transparency is presented. In a nutshell, implementing this project can solve four main problems within small holder farmer community: improving yield and productivity, gaining wider access for the right financing, obtaining higher value of product and also access to the market.
Ketut Gede Mudiarta
Director of Indonesian Institute for Agricultural Technology Transfer (IIATT), Indonesian Agency for Agricultural Research and Development (IAARD), Ministry of Agriculture, Republic of Indonesia
Management of Agricultural Technology Transfer in IAARD Indonesia
The Indonesian Agency for Agricultural Research and Development (IAARD) of the Ministry of Agriculture (MoA) has generated thousands of agricultural technologies, namely varieties of multiple commodities, fertilizers and bio-pesticides, agricultural machinery, food technology, bioenergy, and many others. There are 580 technologies that have been granted intellectual property rights and 146 plant variety protection. Referring to the regulations on technology transfer, therefore technology transfer management plays a significant role to ensure that the outcomes of R&D activities would be implemented by the users as solutions to agricultural problems. There are two channels of the technology transfer, namely technology dissemination of public domain technologies to end-users and license agreement (technology commercialization) with private sectors for massive scale production to reach wider targeted users. Until 2020, IAARD has agreed on 377 license agreements of 125 technologies that have produced royalties for research institutes and researchers. Nevertheless, how to accelerate the technology transfer from research institutes to end-users remain a challenge due to several problems, including technology readiness, company readiness to technology massive scale production, and how to produce technology that meets the market needs. Enhancing research and industry linkage can be considered as one of the solutions.
Application of advanced agricultural sensors and ICT in smart farming towards the Agriculture 4.0
SITI NOOR ALIAH BAHAROM
Ph.D, Malaysian Agricultural Research and Development Institute (MARDI), Department of Engineering Research Centre
Utilization of digital livestock farming for young farmers in Taiwan
Former Head of Animal Breeding and Genetics Division Taiwan Livestock Research Institute
Implementing Agro-Maritime 4.0 Research Agenda: Linking Science and Policy
Ph.D, Head of Research and Community Services (LPPM), IPB University Indonesia
Modern Agriculture: AI, IoT, Robotics and Cloud Computing
Department of Computer Science, IPB University
Application of Information Communication Technology (ICT) and Automation in Jin da Chicken Breeding
General manager, KAI SHING FOOD CORP, Taiwan.
Human Resource Development for Smart Agriculture in Taiwan
Professor, Department of Bio-Industry Communication and Development, National Taiwan University
Smart Agriculture with AWS
Lead Solution Architect AWS Public Sector Indonesia
Artificial Intelligence and Machine Learning for Intelligent IoT in Agriculture
Samsuzana Abd Aziz
Department of Biological and Agricultural Engineering, Universiti Putra Malaysia
Organized by IPB University and Food and Fertilizer Technology Center
Department of Mechanical and Biosystem Engineering, Faculty of Agricultural Engineering and Technology, Bogor Agricultural University IPB, Dramaga Bogor PO BOX 220. phone/fax : +62-251-8623026 | Contact Information :