This study aims to clarify the Japanese characteristics of the spread of smart agriculture utilizing digital technology, which is expected to spread worldwide, and to provide policy implications for further dissemination of the technology. We conducted a questionnaire survey on actual conditions related to smart agriculture on Japanese farms. We have also proposed creation of a Smart Agricultural Kaizen Level (SAKL) technology map by applying the evaluation method used in management technology theory for the manufacturing industry.
The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach.
Ornamental plants are constantly being improved by new technologies and cultivation systems to provide new, high-quality plant material for one of the most demanding markets in the horticulture sector. In addition, the ornamental production sector faces several challenges, such as an increase in costs of production, new and old pests and diseases, climate change and the need to adapt to environmental stresses, the need for conservation and environmental protection, and competition with other food and energy crops in terms of areas and natural resources.
This report provides an overview of the Tropical Agriculture Platform (TAP) since its inception in 2012, when it was officially launched by FAO at the first G20 Meeting of Agriculture Chief Scientists (MACS) in September 2012 in Mexico, until December 2018. The G20 Agriculture Deputies agreed on this stock taking exercise that started under the 2018 Argentinian G20 Presidency.
This exercise was done on the occasion of the G20 MACS meeting in April 2019 in Japan. Its purposes are the following:
The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse.
The impact of global warming on crop growth periods and yields has been evaluated by using crop models, which need to provide various kinds of input datasets and estimate numerous parameters before simulation. Direct studies on the changes of climatic factors on the observed crop growth and yield could provide a more simple and intuitive way for assessing the impact of climate change on crop production.
Soil texture is a key soil property influencing many agronomic practices including fertilization and liming. Therefore, an accurate estimation of soil texture is essential for adopting sustainable soil management practices. In this study, we used different machine learning algorithms trained on vis–NIR spectra from existing soil spectral libraries (ICRAF and LUCAS) to predict soil textural fractions (sand–silt–clay %). In addition, we predicted the soil textural groups (G1: Fine, G2: Medium, and G3: Coarse) using routine chemical characteristics as auxiliary.
Recent Society 5.0 efforts by the Government of Japan are aimed at establishing a sustainable human-centered society by combining new technologies such as sensor networks, edge computing, Internet of Things (IoT) ecosystems, artificial intelligence (AI), big data, and robotics. Many research works have been carried out with an increasing emphasis on the fundamentals of wireless sensor networks (WSN) for different applications; namely precision agriculture, environment, medical care, security, and surveillance.
Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field.
Rice is a primary food for more than three billion people worldwide and cultivated on about 12% of the world’s arable land. However, more than 88% production is observed in Asian countries, including Pakistan. Due to higher population growth and recent climate change scenarios, it is crucial to get timely and accurate rice yield estimates and production forecast of the growing season for governments, planners, and decision makers in formulating policies regarding import/export in the event of shortfall and/or surplus.