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Plant cultivation holds a pivotal role in agriculture, necessitating precise disease identification for the overall health of plants. This research conducts a comprehensive comparative analysis between two prominent deep learning algorithms, convolutional neural network (CNN) and DenseNet121, with the goal of enhancing disease identification in tomato plant leaves.

The dataset employed in this investigation is a fusion of primary data and publicly available data, covering 13 distinct disease labels and a total of 18,815 images for model training. The data pre-processing workflow prioritized activities such as normalizing pixel dimensions, implementing data augmentation and achieving dataset balance, which were subsequently followed by the modeling and testing phases.

Experimental findings elucidated the superior performance of the DenseNet121 model over the CNN model in disease classification on tomato leaves. The DenseNet121 model attained a training accuracy of 98.27%, a validation accuracy of 87.47% and average recall, precision and F1-score metrics of 87, 88 and 87%, respectively. The ultimate aim was to implement the optimal classifier for a mobile application, namely Tanamin.id, and, therefore, DenseNet121 was the preferred choice.

The integration of private and public data significantly contributes to determining the optimal method. The CNN method achieves a training accuracy of 90.41% and a validation accuracy of 83.33%, whereas the DenseNet121 method excels with a training accuracy of 98.27% and a validation accuracy of 87.47%. The DenseNet121 architecture, comprising 121 layers, a global average pooling (GAP) layer and a dropout layer, showcases its effectiveness. Leveraging categorical_crossentropy as the loss function and utilizing the stochastic gradien descent (SGD) Optimizer with a learning rate of 0.001 guides the course of the training process. The experimental results unequivocally demonstrate the superior performance of DenseNet121 over CNN.

The purpose of this paper was to examine smallholder farmers’ perceptions of climate change risks, adaptation responses and the links between adaptation strategies and perceived/experienced climate change risks in South Gondar, Ethiopia.

This paper used a convergent mixed methods design, which enables us to concurrently collect quantitative and qualitative data. Survey data was collected from 352 households, stratified into Lay Gayint 138 (39%), Tach Gayint 117 (33%) and Simada district 97 (28%). A four-point Likert scale was used to produce a standardised risk perception index for 14 climate events. Moreover, using a one-way analysis of variance, statistical differences in selecting adaptation strategies between the three districts were measured. A post hoc analysis was also carried out to identify the source of the variation. The findings of this paper are supplemented by qualitative data gathered through focus group discussions and key informant interviews of households who were chosen at random.

The standardised climate change risk perception index suggests that persistent drought, delayed onset of rainfall, early termination of rainfall and food insecurity were the major potentially dangerous climate change risks perceived by households in the study area. In response to climate change risks, households used several adaptation strategies such as adjusting crop planting dates, crop diversification, terracing, tree planting, cultivating drought-tolerant crop varieties and off-farm activities. A Tukey’s post hoc test revealed a significant difference in off-farm activities, crop diversification and planting drought-tolerant crop types among the adaptation strategies in the study area between Lay Gayint and Simada districts (p < 0.05). This difference reconfirms that adaptation strategies are location-specific.

Although many studies are available on coping and adaptation strategies to climate change, this paper is one of the few studies focusing on the linkages between climate change risk perceptions and adaptation responses of households in the study area. The findings of this paper could be helpful for policymakers and development practitioners in designing locally specific, actual adaptation options that shape adaptation to recent and future climate change risks.

Background: A range of local social and environmental factors has an impact on farmers' views of climate change and choices on the use of coping mechanisms. This study examines the factors that are limiting farmers' perceptions of climate change and their coping mechanisms in Gimbi district, Western Ethiopia.

Methods: A household survey and focus group discussion were employed to collect relevant data. A total of 402 randomly selected households and six focus group discussions containing 72 participants were used to gather data. Binary logit models were used to analyze the collected data.

Results: Farmers noted that some of the signs of climate change included increasing temperature, erratic rainfall, late onset of rainfall, and early cessation of rainfall. We discovered that there are three distinct sets of climate adaption strategies used by farmers: crop management, soil and water conservation and intensive farm management. The primary determinants of farmers' perceptions of climate change and adaptation techniques were household head age, education, soil fertility, market access, and agricultural training. Age, education, and soil fertility level were the characteristics that significantly impacted farmers' perspectives and coping mechanisms among the primary drivers evaluated in the area. Use of agroforestry, shifting planting dates, and fertilizer application were all essential farming practices used as climate adaptation measures.

Conclusions: Both socioeconomic and environmental factors have found to affect farmers' perceptions of climate change in the area. The existing socioeconomic and environmental factors, in turn, affect their choice of strategies to adapt to climate change. When implementing climate change adaption strategies, it is critical to assess farmers' level of awareness of climate change and their coping strategies, as well as the factors limiting their ability to adapt to climate change.

This study aims to investigate the adaptation strategies they practice and the factors that influence their use of adaptation strategies.

The mixed-method sequential explanatory design was used to triangulate the data collected. Multistage sampling was used to select 400 sampled households for household surveys. Eight focus groups, each with eight to ten participants, and 24 key informants, were specifically chosen based on their farming experiences. Chi-square tests, one-way ANOVA and a binary logit model were used to analyze the data.

The majority of farmers used simple and low-cost adaptation strategies like changing planting dates, selling livestock and off-farm and nonfarm work. A minority of farmers used advanced adaptation strategies like crop diversification and water harvesting for irrigation. The result further revealed that: the age of the household head, educational status of household heads, farm size, livestock ownership, farming experiences, household income, access to credit and access to climate information significantly influenced the adoption of the adaptation strategies. Public policy should provide water harvesting and irrigation technology, climate-related information and the provision of microcredit facilities to enhance the farmers’ resilience to climate change risks.

Although several studies on climate change adaptation strategies are available, this paper is one of the few studies focusing on a particular agro-ecological zone, an essential precursor to dealing with current and projected climate change in the area. It provides helpful insights for developing successful adaptation policies that improve adaptive capacity and agricultural sustainability in southern Ethiopia’s lowlands.

There is an increasing need for greater awareness and understanding of the risks climate change poses to farming communities so as to inform appropriate adaptive responses. The purpose of this study is to investigate farmers’ climate change impacts, awareness, risk perception and current adaptation strategies adopted to deal with the impacts of climate change on their livelihood.

This research was undertaken with 67 farmers in Bayelsa State, Nigeria. This study used a combination of focus group discussion and quantitative survey to obtain data. Surveyed farmers were invited to an initial workshop and asked to take photos of climate change impacts on their land and the adaptation strategies being adopted. The photos were analysed and discussed with the farmers in a second workshop. Then, in a third workshop, farmers and other stakeholders came together to rank the most important consequences of climate change and shared knowledge on adaptation strategies. The survey and photovoice data were analysed using descriptive and inferential statistics.

The results of this study showed that a majority of the farmers were knowledgeable of climate change, mostly got climate information through media. Floods and high temperatures were perceived as the most occurring climate change-related disaster risks. Majority of the farmers perceived climate change as high risk and have taken up multiple adaptation strategies in response to it, including changing planting times, mulching their land and digging irrigation pits. Farmers’ responses indicated that they want to do more but are restricted by financial resources.

This study outcomes provide evidence for a need to consider stakeholders’ participation in planning climate change responses to effectively address the challenges posed by climate change, particularly in coastal agricultural communities. Government and relevant agencies as recommended need to support farmers to undertake needed adaptive strategies to adapt with future flooding, high temperature and drought, providing them with necessary facilities to enhance their adaptive capacities.

To the best of the authors’ knowledge, this was one of the first studies to use photovoice to investigate climate change awareness, impacts and adaptations strategies with majority female farmers in west Africa. This study highlights the importance of participatory approaches to capture grassroots climate adaptation approaches.

This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

Climate change is an undeniable reality that threatens people’s livelihoods. Flooding and saltwater intrusion, along with the rising sea levels, are affecting agricultural and aquaculture livelihoods in Myanmar’s coastal areas. Although climate change adaptation is gaining popularity as a resilience strategy to cope with the negative effects of climate change, both agriculture- and aquaculture-farmers are more often deterred from implementing climate change adaptation strategies due to practical availability and socioeconomic barriers to adaptation. This study aims to evaluate the barriers and factors that influence farm household’ choice of climate change adaptation measures.

This study was conducted with 599 farm households (484 rice-farmers and 115 fish farmers) based in the coastal areas of Myanmar during 2021–2022 to explore the farmer’s choice of climate change adaptation measures and the determining factors. The multinomial logit regression (MLR) model was used to examine the factors influencing the farmers’ choice of climate change adaptation strategies.

The study found out that farm households use a variety of adaptation methods at the farm level, with building embankment strategy (23.4%) in agriculture and net-fencing measure (33.9%) in fish farming being the most popular adaptation strategies. Farmers’ decisions to adopt climate change adaptation strategies are influenced by factors such as distance to market, education level of the household head, remittance income and the availability of early warning information, among others. The study also discovered that COVID-19 has had an impact on the employment opportunities of household members and the income from farming as well had a consequential effect on the adoption of climate change adaptation measures. Furthermore, lack of credit (42.4%), labor shortage (52.8%), pest and disease infestation (58.9%), high input costs (81%) and lower agricultural product prices (73%) were identified as major barriers to the adoption of climate change adaptation measures by both agriculture and aquaculture farm households.

This study demonstrates that the COVID-19 pandemic and farm-level barriers are the major factors influencing farm households’ choice of climate change adaptation measures, and that removing practical farm-level barriers and encouraging the adoption of adaptation techniques as potential COVID-19 recovery actions are required. This study also highlighted that the adaptive capacity of agriculture and aquaculture farm households should be strengthened through formal and informal training programs, awareness raising, the exchange of early warning information and the development of proper credit scheme programs.

This paper examines why farmers self-select out of formal credit markets even though they need external funds.

We use probit and Bayesian probit estimators to detect the determinants of self-selection behavior based on a primary dataset of 2,212 rice farmers in Vietnam. After that, we use the multinomial probit (MNP) and Bayesian MNP estimators to reveal the impact of relevant factors on the decision to self-select for farmers belonging to each self-selection category.

The probit and Bayesian probit estimators show that the decision to self-select depends on household head age, income per capita, farm size, whether or not to have relatives or friends working for banks, the number of previous borrowings, risks related to natural disasters, diseases, and rice price, and the number of banks with which the farmer has relationships. The MNP and Bayesian MNP estimators give further insights into the decision of farmers to self-select in that determinants of the self-selection behavior depend on the reasons to self-select. In concrete, farm size and the number of previous borrowings mitigate the self-selection of farmers who did not apply for loans due to having access to other preferred sources of credit. The self-selection of farmers not applying for loans because of unfavorable loan terms is conditional on household head age, farming experience, income, farm size, the number of previous borrowings, natural disaster risk, and the number of banks the farmer has relationships with. Several factors, including education, income, the distance to the nearest bank, whether or not having relatives or friends working for banks, the number of previous borrowings, risks, and the number of banks the farmer has relationships with, affect the self-selection of farmers not applying for loans because of high borrowing costs. The self-selection of farmers not applying for loans because of complex application procedures depends on income and the number of previous borrowings. Finally, the household head’s age, gender, experience, income, farm size, the amount of trade credit granted, the number of previous borrowings, natural disaster risk, and the number of banks the farmer has relationships with are the determinants of the self-selection of farmers not applying for loans because of a fear not being able to repay.

This paper fills the knowledge gap by investigating why farmers self-select out of formal credit markets. It provides evidence of how the farmers’ subjective perceptions of rural credit markets contribute to their self-selection.

This paper shows that demand-side constraints are also vital for farmers’ access to bank credit. Improving credit access via easing supply-side constraints may not increase credit uptake without addressing demand-side factors. Given that finding, it recommends policies to improve access to bank credit for farmers regarding the demand side.

Collaboration in agricultural value chains (AVCs) has the potential to increase smallholders’ participation in international value chains and increase their benefits from participation. This scoping review explores existing collaboration models among stakeholders of AVCs in developing countries, examines enablers and constraints of collaboration and identifies policy gaps.

We systematically searched three databases, CAB Abstracts, Econlit (EBSCO) and Agricola, for studies published between 2005 and 2023 and included 59 relevant studies on AVC collaboration.

The primary motivations for collaboration are to enhance market access and improve product quality. Key outcomes of collaboration include improvements in farmers’ welfare, market participation and increased production; only a few studies consider improved risk management as an important outcome. Robust support from government and non-governmental entities is a primary enabler of collaboration. Conversely, conflicts of interest among stakeholders and resource limitations constrain collaboration possibilities. Collaboration involving high-value crops prioritizes income increases, whereas collaboration involving staple crops focuses on improving household food security.

This study may have publication bias as unsuccessful instances of collaboration are less likely to be published.

This study is unique in highlighting collaboration models’ characteristics and identifying AVC policy and programmatic areas where private firms, farmers’ groups, local governments and donor agencies can contribute.

Artificial Intelligence (AI) is a growing technology impacting several business fields. The agricultural sector is facing several challenges, which may be supported by the use of such a new advanced technology. The aim of the paper is to map the state-of-the-art of AI applications in agriculture, their advantages, barriers, implications and the ability to lead to new business models, depicting a future research agenda.

A structured literature review has been conducted, and 37 contributions have been analyzed and coded using a detailed research framework.

Findings underline the multiple uses and advantages of AI in agriculture and the potential impacts for farmers and entrepreneurs, even from a sustainability perspective. Several applications and algorithms are being developed and tested, but many barriers arise, starting from the lack of understanding by farmers and the need for global investments. A collaboration between scholars and practitioners is advocated to share best practices and lead to practical solutions and policies. The promising topic of new business models is still under-investigated and deserves more attention from scholars and practitioners.

The paper reports the state-of-the-art of AI in agriculture and its impact on the development of new business models. Several new research avenues have been identified.