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.
This report summarizes studies conducted in a framework of TAP-AIS project implemented by FAO’s Research and Extension Unit, and funded by the European Union as a component of the European Union initiative on “Development Smart Innovation through Research in Agriculture” (DeSIRA).
The importance of extension services in helping smallholder farmers to address the many challenges of agricultural production cannot be over-emphasized. However, relatively few studies have been conducted that investigate how the capacities of agricultural extension agents can be built to more effectively assist smallholder farmers in managing climate risks and impacts. As climate change is a key threat to smallholder food production, addressing this issue is increasingly important.
In recent years, the agricultural industry has been experiencing an ever-increasing application of information and communication technologies globally. This new revolution has been touted to impact efficiency and productivity in the agricultural extension services within the agriculture sector. Notwithstanding this, empirical research need to be carried out amongst its users in the sector to ascertain these assertions.
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: