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From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

This paper aims to assess a measurement model of green cleaning for green buildings in Malaysia. Being one of the contributors to the indoor environmental quality performance, green cleaning has become one of the significant aspects that need to be considered for the well-being and performance of a building, particularly in a green building's operations and maintenance performance. Green buildings without green cleaning practices would hinder the benefits that should be rendered economically, socially and environmentally. However, the absence of clear green cleaning components and requirements in Malaysia has become a motivation to undertake this research.

A questionnaire survey involving cleaning service providers and green building index (GBI) facilitators was carried out, and the data was then analyzed using partial least squares structural equation modeling. However, this paper will only be focusing on the measurement model assessment.

Most of the green cleaning components and requirements are acceptable in the model except integrated pest management (in the cleaning procedure component) and hand soaps (in the product and materials component) due to lower factor loadings. Therefore, these two requirements were removed from the measurement model.

Due to a paucity of professionals in the field of green cleaning, the researchers have selected GBI facilitators and cleaning service providers as respondents for this research. The researchers assumed that GBI facilitators are aware of acceptable products and materials for green buildings; meanwhile, cleaning service providers know what is the best cleaning technique and process that helps in achieving cost and resource efficiency. This research also assumed that the green cleaning components identified can be applied to any type of green building, regardless of the differences in needs in each type of building.

This discovery will give the industry, particularly cleaning service providers and green building management teams, a first look at the green cleaning components and requirements.

This paper fulfills the need to study how green cleaning helps in achieving the benefits rendered by green buildings.

The purpose of this study is to evaluate food supply chain stakeholders’ intention to use Industry 5.0 (I5.0) drones for cleaner production in food supply chains.

The authors used a quantitative research design and collected data using an online survey administered to a sample of 264 food supply chain stakeholders in Nigeria. The partial least square structural equation model was conducted to assess the research’s hypothesised relationships.

The authors provide empirical evidence to support the contributions of I5.0 drones for cleaner production. The findings showed that food supply chain stakeholders are more concerned with the use of I5.0 drones in specific operations, such as reducing plant diseases, which invariably enhances cleaner production. However, there is less inclination to drone adoption if the aim was pollution reduction, predicting seasonal output and addressing workers’ health and safety challenges. The findings outline the need for awareness to promote the use of drones for addressing workers’ hazard challenges and knowledge transfer on the potentials of I5.0 in emerging economies.

To the best of the authors’ knowledge, this study is the first to address I5.0 drones’ adoption using a sustainability model. The authors contribute to existing literature by extending the sustainability model to identify the contributions of drone use in promoting cleaner production through addressing specific system operations. This study addresses the gap by augmenting a sustainability model, suggesting that technology adoption for sustainability is motivated by curbing challenges categorised as drivers and mediators.

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.

The United Nations (UN) adopted Sustainable Development Goals (SDGs); agenda 2030 focuses on Climate Action (goal 13), targeting climate adaptability, as well as resilience, awareness and improving policy mechanisms on climate change. In order to enhance climate adaptability, climate-smart agricultural practices (CSAP) is a necessary step. CSAP is a sustainable agriculture approach with a strong focus on climate dimensions. The three pillars of climate-smart agriculture (CSA) are ‘Adaptation’: adapting to climate change; ‘Resilience’: building resilience against it and ‘Remove’: reducing carbon emissions. The new world economy uses Industry 4.0 technologies for sustainable advancement, including blockchain technology, big data analytics, artificial intelligence (AI), augmented and virtual reality, industrial Internet of Things (IoT) and services. Hence, technology plays a significant role in climate sustainable agriculture practices. This chapter shall consider three technologies consisting of IoT, AI and blockchain technology which contribute to CSAP in pre-harvesting (monitoring climate as well as fertility status, soil testing, etc.), harvesting (tilling, fertilisation, seed operations, etc.) and post-harvesting (predicting weather factors, seed varieties, etc.) periods of agriculture. All these three technologies work like the human nervous system; IoT helps in converting various information regarding demography, climate change, local agricultural needs, etc. into world data; AI works like a brain in combination with IoT, helps predict the use of climate-smart technology and blockchain, the memory part of the nervous system which deals with supply-side and ensures traceability as well as transparency for consumers as well as farmers. Hence, this chapter shall contribute to the importance of these three technologies in adopting CSAP in three stages of agriculture.

This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.

The authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.

Fifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.

The authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.

Earlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

This paper aims to analyse the governance structures of the Indonesian chilli value chain, price volatility issues across the chain and to critically explore the value chain actors' perceptions and responses to price volatility.

The authors used semi-structured interviews with 148 primary actors of the Indonesian chilli value chain. In-depth interviews with 22 key stakeholders – from local, provincial and national levels – were conducted in order to obtain additional information about their roles and the current policies and challenges in the chilli industry. The authors also conducted focus group discussions (FGDs) with farmers and support providers and held a national workshop to gather governance and price volatility risk-related information.

The Indonesian chilli value chains are long, complex and involve multiple actors. Most relationships within the value chains are based on market governance in which price regulates transactions. Most value chain actors shared a similar perception of price volatility and its causes. Under different governance structures, the value chain actors identified production, product characteristics and marketing as a major cause of price volatility. Although strategies applied by the value chain actors varied, in the main they are all aimed at minimising the impact of price volatility. Contractual arrangements are viable alternatives to minimising price risk.

This research relies primarily on qualitative data derived from purposive data collection methods, which may reduce the ability to generalise the findings. A quantitative analysis is required to validate the level of price volatility perceived by the stakeholders and to assess the cause and impact of price volatility across the chain. Future research should focus on proposing and assessing potential policy interventions that address price volatility, in order to facilitate the development of the Indonesian chilli industry.

To the best of the author’s knowledge, this study is the first to investigate the governance structures of the Indonesia chilli value chain, the value chain actors' perceptions of price volatility and their responses under the different types of governance in a developing country context.

The purpose of this paper is to investigate the effects of climate smart agriculture knowledge transfers. As well as to examine the application of climate-smart agricultural (CSA) knowledge such as conservation agriculture, irrigation systems, integrated soil fertility management, bioenergy and agroforestry by smallholder farmers in Kenya.

The study applied comparative research methodology to compare climate smart agriculture knowledge application between smallholder participants in farmer field schools (FFS) and no FFS participation. This study used household data from 759 randomly selected rural agricultural households in three counties in Kenya. The study applied multivariate probit model to estimate CSA knowledge application by farmers who participated in field trainings and non-FFS participation farmers.

This study established that climate smart agriculture knowledge transfer through FFS increases farmers’ application of critical aspects of climate smart agriculture knowledge practices such as irrigation system, conservation agriculture and soil and water conservation. Such aspects have been noted as effective interventions against adverse climate change effects such as persistent droughts and flooding and soil infertility. Further findings illustrated that farmers who received CSA knowledge transfers applied agricultural insurance to mitigate rising climatic risks on their farms. Knowledge transfer interventions targeting affordability through subsidizing agricultural insurance are probable and more cost-effective measures that can be used to reduce smallholder farmers’ exposure to climate change-related risks.

This study provides information that was previously unknown about climate smart agriculture knowledge transfers and application among farmers who participated in field trainings and non-FFS participation farmers by using empirical data.

In this article, we offer a contribution to the ongoing study of food by advancing a conceptual framework and interdisciplinary research agenda – what we term “food system resilience”. In recent years, the concept of resilience has been extensively used in a variety of fields, but not always consistently or holistically. Here we aim to theorise systematically resilience as an analytical concept as it applies to food systems research. To do this, we engage with and seek to extend current understandings of resilience across different disciplines. Accordingly, we begin by exploring the different ways in which the concept of resilience is understood and used in current academic and practitioner literatures – both as a general concept and as applied specifically to food systems research. We show that the social-ecological perspective, rooted in an appreciation of the complexity of systems, carries significant analytical potential. We first underline what we mean by the food system and relate our understanding of this term to those commonly found in the extant food studies literature. We then apply our conception to the specific case of the UK. Here we distinguish between four subsystems at which our “resilient food systems” can be applied. These are, namely, the agro-food system; the value chain; the retail-consumption nexus; and the governance and regulatory framework. On the basis of this conceptualisation we provide an interdisciplinary research agenda, using the case of the UK to illustrate the sorts of research questions and innovative methodologies that our food systems resilience approach is designed to promote.

This study aims at navigating the effects of climate change on the right to access to food within the East African Community region, using the case study of Uganda.

The author used doctrinal review of different policies and strategies that have been developed and implemented by the EAC to address the growing patterns of food insecurity and climate change.

Findings show that besides climate change, there are other factors that have played a major role in contributing to food insecurity in the region such as the impact of the ongoing Russia–Ukraine war, absence of food storage reserves/banks, scarring effects of the COVID 19 pandemic, inadequate implementation of agricultural policies on climate change, high post-harvest losses and food waste amongst others.

This research paper is the author’s sole writing and has never been submitted for publication in any journal.