Tuning the Proportional-Integral-Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach.

Nowadays, trajectory control is a significant issue for unmanned micro aerial vehicles (MAVs) due to large disturbances such as wind and storms. Trajectory control is typically implemented using a proportional-integral-derivative (PID) controller. In order to achieve high accuracy in trajectory tracking, it is essential to set the PID gain parameters to optimum values. For this reason, separate gain values are set for roll, pitch and yaw movements before autonomous flight in quadrotor systems. Traditionally, this adjustment is performed manually or automatically in autotune mode. Given the constraints of narrow orchard corridors, the use of manual or autotune mode is neither practical nor effective, as the quadrotor system has to fly in narrow apple orchard corridors covered with hail nets. These reasons require the development of an innovative solution specific to quadrotor vehicles designed for constrained areas such as apple orchards. This paper recognizes the need for effective trajectory control in quadrotors and proposes a novel neural network-based approach to tuning the optimal PID control parameters. This new approach not only improves trajectory control efficiency but also addresses the unique challenges posed by environments with constrained locational characteristics. Flight simulations using the proposed neural network models have demonstrated successful trajectory tracking performance and highlighted the superiority of the feed-forward back propagation network (FFBPN), especially in latitude tracking within 7.52745 × 10-5 RMSE trajectory error. Simulation results support the high performance of the proposed approach for the development of automatic flight capabilities in challenging environments.

Adoption of ICT4D and its determinants: A systematic review and meta-analysis.

Various Digital Agricultural Technologies (DAT) have been developed and implemented around the world. This study aims to estimate the overall adoption rate and identify the determinant factors for a better adoption perspective after decades of innovation and dissemination. A systematic review was conducted on published studies that reported adoption rates and determinant factors using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. We used meta-regression and the partial correlation coefficient to estimate the effect size and establish the correlation between socioeconomic characteristics and the adoption of various technologies reported. Fifty-two studies with 32400 participants met the selection criteria and were included in the study. The results revealed an overall pooled adoption rate of 39 %, with the highest adoption rates in developing countries in Africa and South America. Socioeconomic factors such as age, education, gender, and income were found to be the main determinants and should be considered when designing technology for sustainable adoption. The study also found that young farmers were more susceptible to adoption. Moreover, farmers with higher income levels and educational attainment are more likely to use technology linked to agricultural production, market access, and digital advising, implying that high-income farmers with more education are more tech-savvy. However, this does not exclude low-income and low-educated farmers from adopting the technologies, as many models and strategies with socioeconomic considerations were developed. It is one of the reasons behind the underlying enthusiasm for digital agricultural adoption in low and middle-income countries.

How does Internet use affect farmers' low-carbon agricultural technologies in southern China?

Agricultural low-carbon emission reduction is an essential part of China's ecological civilization construction. Farmers' low-carbon agricultural technology (LCAT) adoption has become an important means to achieve agricultural low-carbon emission reduction. Based on the survey data of 1080 farmers in Sichuan Province, farmers' LCAT adoption has been empirically studied using the combined estimate conditional mixed treatment model (CMP). The results show that the use of the Internet will substantially promote farmers' low-carbon tillage technology adoption and low-carbon fertilization technology adoption but has no significant impact on farmers' low-carbon pharmaceutical application technology adoption, low-carbon irrigation technology adoption, low-carbon agricultural film recycling technology adoption, and straw recycling technology adoption. Mechanism analysis shows that Internet use mainly affects farmers' low-carbon fertilization technology adoption through economic benefit cognition and affects farmers' low-carbon tillage technology adoption through ecological benefit cognition. This study can enhance our understanding of the relationship between Internet use and LCAT adoption and serve as a resource for rural digital infrastructure development and LCAT adoption-related policy design.

Linking farmers' risk attitudes, livelihood diversification and adoption of climate smart agriculture technologies in the Nyando basin, South-Western Kenya.

Climate smart agriculture (CSA) technologies are innovations meant to reduce the risks in agricultural production among smallholder farmers. Among the factors that influence farmer adoption of agricultural technologies are farmers' risk attitudes and household livelihood diversification. This study, focused on determining how farmers' risk attitudes and household livelihood diversification influenced the adoption of CSA technologies in the Nyando basin. The study utilized primary data from 122 households from two administrative regions of Kisumu and Kericho counties in Kenya. The study employed the multivariate probit (MVP) and ordered probit (OP) models and descriptive statistics in data analysis using Stata 14.0. Results from the study indicated that farmers' risk attitudes had a significant negative influence in the adoption of terraces, ridges and bunds as well as the intensity of adoption of given CSA technologies. Household livelihood diversification had a significant negative influence in the adoption of stress tolerant livestock but did not have a significant effect on the intensity of adoption of given CSA technologies. The study recommends that relevant stakeholders should introduce an appropriate agricultural index insurance product to Nyando basin farmers to encourage the broader adoption of CSA technologies.

Enfermedad de Chagas: El impacto de las tecnologías de explotación agropecuaria

Diversos fueron los factores en el proceso evolutivo, desde el punto de vista epidemiológico, que tuvieron influencia sobre la enfermedad de Chagas en la provincia de Tucumán (República Argentina). Dentro de estos factores se incluyen las influencias generadas por las tecnologías de punta aplicadas en la explotación agropecuaria, la tala del bosque, en procura del incremento de las áreas de cultivo y en búsqueda de mayor productividad, la desocupación, las migraciones humanas y entre las mismas la de los infectados por la tripanosomiasis chagásica con sus consecuencias: la urbanización de la enfermedad. Las circunstancias naturales y la acción del hombre ubican a la enfermedad. Son cambios dinámicos y a veces muy rápidos, detectables en todo el continente, corresponsables por la aparición de nuevos focos o por la desaparición de otros. En el presente artículo es una revisión de lo acontecido en la provincia de Tucumán (Argentina), respecto a las influencias de las tecnologías de explotación agropecuarias hacia fin del siglo XX y comienzo de este siglo, y como impactaron las mismas sobre aspectos epidemiológicos de la enfermedad de Chagas.

From an epidemiological point of view, various factors in the evolutionary process had an influence on Chagas disease in the province of Tucumán (Argentine Republic). These factors include the influences generated by the technologies applied in the agricultural exploitation, the logging of the forest, in search of the increase of the cultivation areas and in search of greater productivity, unemployment, human migrations and among them the of those infected by chagasic trypanosomiasis with its consequences: urbanization of the disease. The natural circumstances and the action of man locate the disease. They are dynamic and sometimes very rapid changes, detectable throughout the continent, co-responsible for the appearance of new outbreaks or the disappearance of others. This article is a review of what happened in the province of Tucumán (Argentina), regarding the influences of agricultural exploitation technologies towards the end of the 20th century and beginning of this century, and how they impacted on epidemiological aspects of the Chagas disease.

Do ponto de vista epidemiológico, vários fatores no processo evolutivo influenciaram a doença de Chagas na província de Tucumán (República Argentina). Entre esses fatores estão incluídas as influências geradas pelas tecnologias aplicadas na exploração agrícola, na exploração florestal, na busca do aumento das áreas de cultivo e na busca de maior produtividade, desemprego, migrações humanas e entre elas a daqueles infectados por tripanossomíase chagásica com suas consequências: urbanização da doença. As circunstâncias naturais e a ação do homem localizam a doença. São mudanças dinâmicas e, às vezes, muito rápidas, detectáveis em todo o continente, corresponsáveis pelo surgimento de novos surtos ou pelo desaparecimento de outros. Este artigo é uma revisão do que aconteceu na província de Tucumán (Argentina), sobre as influências das tecnologias de exploração agrícola no final do século XX e início deste século e como elas impactaram os aspectos epidemiológicos da doença de Chagas.

Dataset of the survey on e-registration and geo-referenced of rice value chain actors for the diffusion of technologies: Case of Benin and Côte d'Ivoire.

The paper presents a dataset of the e-registration of rice value chain actors in Benin and Côte d'Ivoire for assessing the adoption of innovations and the diffusion of new rice technologies. Data were collected from actors after a census conducted in three steps. In the first step, main rice production regions and rice value chain actors were identified. In the second step, we updated the list of actors based on membership of actors' associations. In third step, we did the census of all individual actors and geo-localized all farmers' fields and villages using GPS device. Data were collected for the 2018 growing seasons. The dataset contains 17,639 observations (9,000 in Benin and 8,639 in Côte d'Ivoire) with 159 variables divided into six sections: (i) preliminary information on the respondents; (ii) socio-economic characteristics; (iii) information on the rice plots; (iv) knowledge, use and access to rice varieties; (v) knowledge, use and access to agricultural equipment and methods; and (vi) information on post-harvest activities. Six categories of actors were identified: foundation seed producers (420), certified seed producers (1,212), paddy rice producers (14,230), parboilers (1,735), millers (188) and traders (1,429). The dataset is available online at Mendeley data repository. The dataset is valuable for the diffusion at large scale of improved technologies and an effective monitoring of the dissemination. Data can be used by scientists to have better understanding of the rice value chains, rice production systems, the level of knowledge, accessibility and adoption of improved rice varieties and agricultural technologies, for further research regarding rice value chain development, technologies testing and socioeconomics study of rice value chain actors. Because of the large number of observations (17,639), data can be used as sampling frame for further experiment or surveys based on random samples. Moreover, the dataset has the potential of generating descriptive statistics at the most disaggregated level of administrative units or villages for different equipment, methods and varieties adopted by gender and country.

Who cares what parents think or do? Observational learning and experience-based learning through communication in rice farmers' willingness to adopt sustainable agricultural technologies in Hubei Province, China.

Sustainable agricultural technologies are of great significance in fully utilizing agricultural resources and promoting agricultural production. However, the adoption rates of these technologies are often characterized as low in rural areas in China. To figure out the potential salient determinants of rice farmers' willingness to adopt sustainable agricultural technologies, this paper, by employing the multivariate probit model and ordered probit model, particularly and firstly explores the roles of observational learning and experience-based learning through communication from parents within the household on rice farmers' willingness to adopt these technologies. Results show that there are strong complementarities and substitutabilities between sustainable agricultural technologies that rice farmers are willing to adopt, and that observational learning and experience-based learning through communication within the household do have pronounced effects on rice farmers' willingness to adopt some sustainable agricultural technologies and on their intensive use intentions. Therefore, while formulating policies to improve the adoption rates and adoption intensity of these technologies, relevant government agencies should take the complementarities and substitutabilities between sustainable agricultural technologies as well as observational learning and experience-based learning through communication from parents into consideration.

Agricultural technologies and carbon emissions: evidence from Jordanian economy.

Theoretically, agriculture can be the victim and the cause of climate change. Using annual data for the period of 1970-2014, this study examines the interaction between agriculture technology factors and the environment in terms of carbon emissions in Jordan. The results provide evidence for unidirectional causality running from machinery, subsidies, and other transfers, rural access to an improved water source and fertilizers to carbon emissions. The results also reveal the existence of bidirectional causality between the real income and carbon emissions. The variance error decompositions highlight the importance of subsidies and machinery in explaining carbon emissions. They also show that fertilizers, the crop and livestock production, the land under cereal production, the water access, the agricultural value added, and the real income have an increasing effect on carbon emissions over the forecast period. These results are important so that policy-makers can build up strategies and take in considerations the indicators in order to reduce carbon emissions in Jordan.