Search results

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

Food systems research is typically focused on productivity and efficiency. But in the face of impending challenges of climate, investment, markets, and incomes small holders may do well to shift to diversity and sufficiency. The transition requires institutions such as Farmer Producer Organisations (FPOs) to play the role of intermediaries. This paper aims to understand this challenging phenomenon using a case from India.

In this article, drawing from the emerging literature of PO as a sustainability transition intermediary, this paper uses the case study of a women-owned FPO and explores its role in contributing to sustainable food systems through practices of non-pesticide management of agriculture. This paper explores, through non-participant observer methods, focus group discussions and interviews with multiple stakeholders how an FPO embeds sustainability in its purpose and the challenges faced in transforming producer and consumers towards sustainable food systems.

The study argues for early articulation of the “sustainability transition intermediary” role in the FPO’s vision and mission. Second, FPOs’ role of being a transition intermediary is impacted by the key stakeholders and the durability of relationship with them.

By studying FPOs in India, from the framework of sustainability transitions, this article adds to the limited literature that looks as POs as sustainability transition intermediaries.

The ongoing transition in the attitude of consumers toward health and environment has a direct implication on the organic food industries, making it necessary to examine the drivers of the purchase intention of organic food items, specifically in developing economies like India. Therefore, this study tries to frame and validate the instrumental scale by collecting data from 574 organic food item consumers to examine the determinants of purchase intention among consumers in India.

This study has employed advanced statistical tools i.e. Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Harmon’s single factor test and other statistical measures using SPSS and AMOS 23, for framing and validating the instrumental scale for this study.

The results of EFA explain 67.714% variance of total research variable variance with six major constructs. Moreover, the result of the CFA confirms the six factors and the proposed instrumental scale. The finding explicates that health consciousness, ecological trustworthiness and functional value are the major drivers of the purchase intention of organic food items.

This study has major policy implications for organic producers, processor and marketers for understanding the complex phenomenon of organic consumer behavior. The result explains that marketers and producers should adopt ad hoc marketing strategies that aim to promote the organic food items as healthy and safe.

There is hardly any study that has proposed and validated an instrumental scale with these factors collectively in India for studying the purchase intention of organic food consumers in India.

The purpose of this study is to offer a better and more effective hexacopter design for a 3 kg payload using finite element analysis (FEA), facilitating the use of different materials for different components that too without compromising strength.

A 3D computer-aided design (CAD) model of a hexacopter with a regular hexagonal frame is presented. Furthermore, a finite element model is developed to perform a structural analysis and determine Von Mises stress and strain values along with deformations of different components of the proposed hexacopter design.

The results establish that carbon fibre outperforms acrylonitrile butadiene (ABS) with respect to deformations. Within the permissible limits of the stress and strain values, both carbon fiber and ABS are suggested for different components. Thus, a proposed hexacopter offers lighter weight, high strength and low cost.

The use of different materials for different components is suggested by making use of static structural analysis. This encourages new research work and helps in developing new applications of hexacopter, and it has never been reported in literature. The suggested materials for the components of the hexacopter will prove to be suitable considering weight, strength and cost.

The purpose of this paper is to develop a practical implementation blueprint for the attainment of food security for all Nigerians based on sustainable agricultural practices.

This paper conducted a critical review of 66 peer-reviewed empirical articles on various sustainable agricultural case studies. The evidence obtained from this review and the in-depth knowledge of the authors regarding the Nigerian agricultural landscape was used to develop a practical implementation blueprint for achieving food security in the country.

The food security for all Nigerians (FOSFAN) blueprint was developed and comprising of eight practical and interconnected steps. These steps take into consideration the synergistic effort of the government, the ministry of agriculture and its corresponding agencies in ensuring that farmers attain sustainable practices in their endeavour.

The findings of this paper will contribute to existing literature on food security and will also serve as a baseline for deeper empirical exploration of the impact of sustainable agricultural practices on food security in Nigeria (a country in the Sub-Saharan region).

The FOSFAN blueprint provides a practical and comprehensive step-by-step guide, which the Nigerian Ministry of Agriculture can use to achieve food security for all Nigerians.

This paper is addressed towards the development of food security plan in the Nigerian context in which the Nigerian Government ensures the availability of food to all its citizens in a bid to achieve “Zero Hunger”, which is the second Sustainable Development Goal.

The FOSFAN blueprint is a proactive guide that takes into consideration the importance of cultural and socioeconomic nuances in the development of a sustainable agriculture policy in a developing country context.

This study aims to assess the spread of environmental literacy graduation requirements at public universities in the USA, and to highlight factors that mediate the adoption of this curriculum innovation.

The author analyzed the undergraduate general education curriculum requirements at all 549 public BA-granting higher education institutions in the USA between 2020 and 2022.

The study found that only 27 US public universities out of 540 have an environmental literacy graduation requirement, which represents 5% of universities and is substantially lower than previous estimates.

First, this study provides a more complete, more reliable and more current assessment of the graduation requirement’s presence at US tertiary institutions, and shows the number of universities that have implemented this innovation is lower than was estimated a decade ago. Second, it draws from the scholarship on the infusion of sustainability into the university curriculum to provide a comprehensive discussion of factors that mediate the pursuit and implementation of the graduation requirement. As well, it identifies factors that played a key role in one pertinent case.

The Baduy tribe has its own uniqueness and values regarding the forest; it manages the forest using customary law to keep it sustainable. This research aims to describe the position of customary law used by the Baduy tribe to conserve forest areas.

This research is a qualitative research conducted in September 2019 and 2020 at Baduy. The data were collected through a literature study and in-depth interviews with informants related to the Baduy tribe. The collected data included documentation and interview transcripts that were translated into English. Data analysis was conducted in a descriptive manner, equipped with related evidence.

The Baduy community holds firm to its customs and culture called pikukuh. The Baduy community applies the concept of sustainable forest management in that local communities are directly involved in forest management activities to improve welfare and implement sustainable forests.

The implication of this research is that it is beneficial for forest conservation based on customary law, using the conservation approach of the Baduy tribe as a local community in protecting the sustainability of forest resources and their sustainability for the next generation. This study contributes as a guide for the government to formulate policies that will include local communities into conservation programs and government policies. It may apply to a study of coordination with related institutions such as the Ministry of Environment and Forestry in implementing forest conservation.

This study uses primary data from the Baduy tribe, which has unique local traditional values regarding the territory and the important role of the forest. The originality of the findings from the excavation of each activity was based on the procedures and beliefs regulated in customary law regarding forest management. Preservation of traditional knowledge in customary law has contributed to the urgency of sustainable forest conservation and biodiversity conservation, which is part of the traditional knowledge of the Baduy tribe.

This study aims to investigate the determinants of United Arab Emirates (UAE) and Qatar consumers’ perceptions of organic food.

Using a survey approach and focusing on six known determinants identified from empirical studies, data were analysed using a mean difference analysis and a multi-regression model.

Citizens in both countries had similar access to organic food products, yet consumer perceptions differed significantly. Given the geographical, cultural and topographical proximity and similarities of these countries, differences in the mean values indicate that targeted or bespoke marketing is required to attract customers.

One limitation of this study its sample size that may limit the generalizability of its results. However, the highly statistically significant results offset this to some extent as the differences the authors have demonstrated are likely to be real. These do require confirmation by further similar studies. The study relied on a random sample of respondents who might have exhibited bias in answering questions.

The two countries central to this research study, the UAE and Qatar, have vested interests in supporting the organic food product market that mainly concern supporting and promoting the health of their citizens and the environment. As shown in this paper, the key obstacles preventing consumers’ accessing organic products are availability and price. With appropriate governmental subsidies and logistics support, however, these barriers can be overcome.

If governments are sincere in their declarations that the health and welfare of their citizens is of prime importance, they will support attempts to promote the sale and consumption of organically grown food. The results of this study can help to inform such policies.

This study provides insights into the most important factors that influence consumers’ attitudes towards organic foods. This study fills a gap in the existing research on consumer buying behaviour for organic food in two Middle Eastern countries.

In this case study, we examine how a citrus peel valorising company based in the Netherlands was able to adopt a circular business model while navigating regulatory, managerial, and supply chain-related barriers.

In-depth, semi-structured interviews with key personnel in the company, notes from field observations, photographs of the production process, and documents from a legal judgement served as data for this single, qualitative case study. Data were coded inductively using the in vivo technique and were further developed into four themes and a case description.

Results from our study indicate that the regulatory and political contexts in the Netherlands were critical to the company’s success. Like in the case of most fruitful industrial symbioses, partnerships founded on mutual trust and economically appealing value propositions played a crucial role in ensuring commercial viability. Collaborating with larger corporations and maintaining transparent communication with stakeholders were also significant contributing factors. Lastly, employees’ outlook towards circularity combined with their willingness to learn new skills were important driving factors as well.

In addition to expanding the scholarship on the adoption of circular business models, this research offers novel insights to policymakers and practitioners. It provides empirical evidence regarding the importance of public awareness, adaptable legislation, and harmonised policy goals for supporting sustainable entrepreneurship in the circular economy.