This paper contributes to smart greenhouses and IoT (Internet of Things) research. Our pioneering achievement centers on successfully designing, constructing, and testing a 30m²smart greenhouse, explicitly focusing on the cultivation and development of Brassica Juncea, a mustard variety commonly grown in Vietnam. The construction phase entailed the meticulous integration of diverse IoT technologies and systems, culminating in the creation of a finely tuned environment to meet the unique needs of Brassica Juncea cultivation. Notably, our research team has realized the physical infrastructure and developed and implemented a robust web interface. This interface empowers users to monitor and remotely control the smart greenhouse conveniently. It provides real-time visualization of critical parameters, including temperature, humidity, soil moisture, and light intensity, enabling precise monitoring and supporting informed decision-making in crop management. In addition to the web interface, we have meticulously designed and completed an Android mobile application, further enhancing accessibility and convenience. This mobile app allows users to monitor and control the smart greenhouse while on the move. It is imperative to underscore that this work marks a significant milestone as the first complete smart greenhouse IoT solution dedicated to developing Brassica Juncea. Our pioneering accomplishments not only advance the frontiers of innovative greenhouse and IoT research but also contribute substantially to the progress of intelligent agriculture.

In this paper we propose an agent-based approach to the modeling and simulation of invasions of brown plant hoppers in the Mekong Delta. Although these seasonal invasions are now heavily studied and their factors quite well known, it is still difficult to predict in advance where and when the next occurrence will appear. As a matter of fact, the conjunction of particular local weather conditions, global winds, biological characteristics of rice crops, agricultural decisions in Vietnam and in the surrounding countries (mainly Cambodia and Thailand), prevent analytical models from delivering interesting results. One of the only options is then to build predictive simulation models, with the constraint that they have to rely on accurate spatial and weather data, and have to represent the knowledge about the various heterogeneous "actors" that play a role in these phenomena (from the insects themselves to the farmers or local authorities). After a short review on similar researches, we argue that agent-based modeling is likely the most interesting approach to fulfill these requirements and we present the methodology we have used to design our model (BPHSim). This model and its implementation are detailed, and followed by an overview of its validation against real data. Then, we discuss the different research directions that this approach will allow us to take in order to tackle the more complicated problem of decision-support when an invasion is predicted.

DOI= http://doi.acm.org/10.1145/2077489.2077517International audienceIn this paper, a new agent-based model is proposed to model and simulate visually growth and invasion of brown plant hoppers in different localities and at different times under effects of weather conditions such as temperature, humidity, rainfall and wind factors. The results from simulation are validated with light traps data collected in communes in the Mekong Delta region and show that brown plant hoppers life is affected by weather

This paper proposed a novel first-order single-valued neutrosophic hesitant fuzzy time series (SVNHFTS) forecasting model. Our aim is to improve the previously proposed neutrosophic time series (NTS) model by incorporating the degree of the hesitancy using single-valued neutrosophic hesitant fuzzy set (SVNHFS) model instead of single-valued neutrosophic set (SVNS). Our paper's novelty is that we incorporate an algorithm that automatically converts the crisp dataset into the neutrosophic set that eliminates the need for experts' input or opinions in determining the membership in each of the partitioned neutrosophic set. We also incorporate Markov Chain algorithm in the de-neutrosophication process to include the weightage of the repeating neutrosophic logical relationships (NLRs). Our paper's significant contribution is to add to the existing body of knowledge related to fuzzy time series (FTS) by developing a new FTS model based on SVNHFS, one of the improved version of fuzzy sets, since this area of research is still relatively underdeveloped. To determine our proposed model's capability, we apply our proposed SVNHFTS model to three real datasets while also comparing the result to the other FTS models based on improved versions of fuzzy sets. Our datasets include benchmark enrollment data of University of Alabama, IDX Composite (Indonesian composite stock index), and MERVAL index (Argentinian composite stock index). The result shows that our proposed SVNHFTS model outperforms most of the other FTS models in terms of AFE and RMSE, especially the previously proposed NTS model.