Research on Innovative Training on Smart Greenhouse Technologies for Economic and Environmental Sustainability

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

Smart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts' opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts' weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are "camera," "power system," and "radar system," respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.

The Effect of Technical Training Provided by Agricultural Cooperatives on Farmers' Adoption of Organic Fertilizers in China: Based on the Mediation Role of Ability and Perception

Organic fertilizers can be crucial in promoting sustainable agricultural development, but they are not used in a wide-ranging way among smallholder farmers in many developing countries. In China, cooperatives are considered essential subjects of agricultural technical training, but it is more common to join cooperatives without participating in their technical training. Thus, joining cooperatives or not cannot simply be used to assess the role of cooperatives in influencing the farmers' production behavior. Based on survey data of 1160 citrus farmers in Sichuan Province, China, this study estimated the effect of the technical training provided by agricultural cooperatives on farmers' adoption of organic fertilizers, taking into account the farmers' ability and perception as the mediation variables. The findings showed that participating in the technical training provided by agricultural cooperatives could significantly enhance the likelihood that farmers will adopt organic fertilizers. The impact was 81.6% in influencing the farmer's abilities and 7.64% in their perceptions of organic fertilizers. Furthermore, other variables, such as farm sizes, land transfers, and education levels, also make a difference in the effectiveness of the agricultural cooperatives' technical training. This study provides support for developing pertinent policies to promote the complete adoption of agricultural cooperatives' technical training functions and the widespread use of organic fertilizers.

Application of Internet of Things (IoT) for Optimized Greenhouse Environments

This review presents the state-of-the-art research on IoT systems for optimized greenhouse environments. The data were analyzed using descriptive and statistical methods to infer relationships between the Internet of Things (IoT), emerging technologies, precision agriculture, agriculture 4.0, and improvements in commercial farming. The discussion is situated in the broader context of IoT in mitigating the adverse effects of climate change and global warming in agriculture through the optimization of critical parameters such as temperature and humidity, intelligent data acquisition, rule-based control, and resolving the barriers to the commercial adoption of IoT systems in agriculture. The recent unexpected and severe weather events have contributed to low agricultural yields and losses; this is a challenge that can be resolved through technology-mediated precision agriculture. Advances in technology have over time contributed to the development of sensors for frost prevention, remote crop monitoring, fire hazard prevention, precise control of nutrients in soilless greenhouse cultivation, power autonomy through the use of solar energy, and intelligent feeding, shading, and lighting control to improve yields and reduce operational costs. However, particular challenges abound, including the limited uptake of smart technologies in commercial agriculture, price, and accuracy of the sensors. The barriers and challenges should help guide future Research & Development projects and commercial applications.