América Latina y el Caribe (ALC) se caracteriza por aplicar una estrategia de desarrollo que depende de manera notable de la explotación de sus recursos naturales. Debido a que la población de la región aumenta de forma sistemática, la presión sobre los recursos naturales ha tenido un incremento marcado.
Chile es un país líder en la exportación de alimentos, en donde la apicultura juega un rol fundamental y cuenta con más de 1 300 000 colmenas para apoyar la producción de alimentos a través de la polinización. Las buenas prácticas deben ser abordadas de generación en generación de apicultores para favorecer el mantenimiento de colmenas sanas y activas para la presentación de servicios sistémicos de polinización.
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.
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.
This document is part of the project “Strengthening the adaptive capacity to climate change in the fisheries and aquaculture sector of Chile”, executed by the Undersecretariat of Fisheries and Aquaculture and the Ministry of the Environment, and implemented by the Food and Agriculture Organization of the United Nations, with funding from the Global Environment Facility. The work was implemented in four pilot coves: Caleta Riquelme (Tarapacá); Caleta Tongoy (Coquimbo); Caleta Coliumo (Biobío); and Caleta El Manzano-Hualaihué (Los Lagos).
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.