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.
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).
La participación de los pequeños productores en procesos de investigación asociados a los sistemas productivos agrícolas ha sido difícil de lograr. Por esto el objeto de la presente investigación fue el de lograr la vinculación de pequeños productores de yuca (Manihot esculenta Crantz) a procesos de investigación en la región caribe de Colombia. Por lo anterior, se implementaron ensayos de campo en los que se empleó un método de investigación participativa a través de modelos integrados de producción.
This paper seeks to understand what influences research and extension professionals’ intentions to use AIS approaches and to explore how this can inform implementation and design of more effective AIS. We applied the Reasoned Action Approach through focus groups and structured questionnaires with research and extension professionals from government and non-government organisations in Sierra Leone, where AIS approaches are not widely used although increasingly institutionalised in policy.
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.
In early 2020, GFRAS provided support to the Agricultural Extension in South Asia (AESA) Network and the Bangladesh Agricultural Extension Network (BAEN) in order to customize one of the NELK Modules in the context of Bangladesh. The BAEN Executive Committee selected the GFRAS NELK Module 7 on ‘Facilitation for Development’ for customization. AESA and BAEN jointly implemented the development of the customized module for Bangladesh. The process of customization consisted of five phases spread over a span of six months.