Search Results (880)

In the current era, Indian agriculture faces a significant demand for increased food production, which has led to the integration of advanced technologies to enhance efficiency and productivity. Drones have emerged as transformative tools for enhancing precision agriculture, reducing costs, and improving sustainability. This study provides a comprehensive review of drone adoption in Indian agriculture by examining its effects on precision farming, crop monitoring, and pesticide application. This research evaluates technological advancements, regulatory frameworks, infrastructure, farmers’ perceptions, and the financial accessibility of drone technology in the Indian agricultural context. Key findings indicate that, while drone adoption enhances efficiency and sustainability, challenges such as high costs, lack of training, and regulatory barriers hinder widespread implementation. This paper also explores the growing market for agricultural drones in India, highlighting key industry players and projected market growth. Furthermore, it addresses regional differences in adoption rates and emphasizes the increasing social acceptance of drones among Indian farmers. To bridge the gap between potential and practice, the study proposes several policy and institutional recommendations, including government-led financial incentives, training programs, and public–private partnerships to facilitate drone integration. Moreover, this review article also highlights technological advancements, such as AI and IoT, in agriculture. Finally, open issues and future research directions for drones are discussed. Full article

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture. Full article

Tea cultivation faces significant threats from red spider mite (RSM: Oligonychus coffeae) infestations, which reduce yields and economic viability in major tea-producing regions. Current automated detection methods rely on supervised deep learning models requiring extensive labeled data, limiting scalability for smallholder farmers. This article proposes AIM-Net (AI-based Infestation Mapping Network) by evaluating SwAV (Swapping Assignments between Views), a self-supervised learning framework, for classifying RSM infestation severity (Mild, Moderate, Severe) using a geo-referenced, field-acquired dataset of RSM infested tea-leaves, Cam-RSM. The methodology combines SwAV pre-training on unlabeled data with fine-tuning on labeled subsets, employing multi-crop augmentation and online clustering to learn discriminative features without full supervision. Comparative analysis against a fully supervised ResNet-50 baseline utilized 5-fold cross-validation, assessing accuracy, F1-scores, and computational efficiency. Results demonstrate SwAV’s superiority, achieving 98.7% overall accuracy (vs. 92.1% for ResNet-50) and macro-average F1-scores of 98.3% across classes, with a 62% reduction in labeled data requirements. The model showed particular strength in Mild_RSM-class detection (F1-score: 98.5%) and computational efficiency, enabling deployment on edge devices. Statistical validation confirmed significant improvements (p < 0.001) over baseline approaches. These findings establish self-supervised learning as a transformative tool for precision pest management, offering resource-efficient solutions for early infestation detection while maintaining high accuracy. Full article

Postharvest losses amongst small-scale farmers in developing countries are high due to inadequate resources and infrastructure. Among the various affected crops, tomatoes are particularly vulnerable; however, studies on postharvest losses of most fruits and vegetables are limited. Therefore, this study aimed to assess postharvest tomato losses under different production systems within the small-scale supply chain using the indirect assessment (questionnaires and interviews) and direct quantification of losses. Farmers reported tomato losses due to insects (82.35%), cracks, bruises, and deformities (70.58%), and diseases (64.71%). Chemical sprays were the main form of pest and disease control reported by all farmers. The direct quantification sampling data revealed that 73.07% of the tomatoes were substandard at the farm level, with 47.92% and 25.15% categorized as medium-quality and poor-quality, respectively. The primary contributors to the losses were decay (39.92%), mechanical damage (31.32%), and blotchiness (27.99%). Postharvest losses were significantly higher under open-field production systems compared to closed tunnels. The fungi associated with decay were mainly Geotrichum, Fusarium spp., and Alternaria spp. These findings demonstrate the main drivers behind postharvest losses, which in turn highlight the critical need for intervention through training and support, including the use of postharvest loss reduction technologies to enhance food security. Full article

Biosecurity measures applied on poultry farms, with a recent history of highly pathogenic avian influenza virus infection, were monitored using 24 h/7 days-per-week video monitoring. Definition of biosecurity breaches were based on internationally acknowledged norms. Farms of four different production types (two broiler, two layer, two breeder broiler, and one duck farm) were selected. Observations of entry to and exit from the anteroom revealed a high degree of biosecurity breaches in six poultry farms and good biosecurity practices in one farm in strictly maintaining the separation between clean and potentially contaminated areas in the anteroom. Hand washing with soap and water and/or using disinfectant lotion was rarely observed at entry to the anteroom and was almost absent at exit. Egg transporters did not disinfect fork-lift wheels when entering the egg-storage room nor change or properly disinfect footwear. The egg-storage room was not cleaned and disinfected after egg transport by the farmer. Similarly, footwear and trolley wheels were not disinfected when introducing young broilers or ducklings to the poultry unit. Biosecurity breaches were observed when introducing bedding material in the duck farm. This study shows a need for an engaging awareness and training campaign for poultry farmers and their co-workers as well as for transporters to promote good biosecurity practices. Full article

Brucellosis is caused by Brucella spp.; it can result in fetal loss and abortion, resulting in economic losses and negative effects on human health. Herein, a cross-sectional study on the epidemiology of Brucella spp. in aborted livestock in Ningxia from 2022 to 2023 was conducted. A total of 749 aborted tissue samples from 215 cattle and 534 sheep were collected from farmers who reported abortions that were supported by veterinarians trained in biosecurity. The samples were analyzed using qPCR and were cultured for Brucella spp. when a positive result was obtained; the samples were speciated using AMOS-PCR. MLST and MLVA were employed for genotype identification. The results demonstrated that 8.68% of the samples were identified as being positive for Brucella spp. based on qPCR results. In total, 14 field strains of Brucella spp. were subsequently isolated, resulting in 11 B. melitensis, 2 B. abortus, and 1 B. suis. being identified via AMOS-PCR. Four sequence types were identified via MLST—ST7 and ST8 (B. melitensis), ST2 (B. abortus), and ST14 (B. suis)—with ST8 predominating. Five MLVA-8 genotypes and seven MLVA-11 genotypes were identified, with MLVA-11 GT116 predominating in livestock. Thus, at least three Brucella species are circulating in aborted livestock in Ningxia. This suggests a significant risk of transmission to other animals and humans. Therefore, disinfection and safe treatment procedures for aborted livestock and their products should be carried out to interrupt the transmission pathway; aborted livestock should be examined to determine zoonotic causes and targeted surveillance should be strengthened to improve the early detection of infectious causes, which will be of benefit to the breeding industry and public health security. Full article

Small-scale fisheries and aquaculture play a crucial role in securing food, income, and nutrition for millions, especially in the Global South. Rural small-scale aquaculture (SSA) is characterized by limited investment and technical training among farmers, diversification and dispersion of farms over large areas, reduced access to competitive markets for inputs and products, and family labor. Small-scale integrated circular aquaponic (ICAq) systems, in which systems’ component outputs are transformed into component inputs, have significant potential to increase circularity and promote economic development, especially in a rural context. We offer an integrated and comprehensive approach centered on aquaponics or aquaponic farming for small-scale aquaculture units. It aims to identify and describe a series of circular processes and causal links that can be implemented based on deep study in SSA and ICAq. Circular processes to treat by-products in ICAq include components like composting, vermicomposting, aerobic and anaerobic digestion, silage, and insect production. These processes can produce ICAq inputs such as seedling substrates, plant fertilizers, bioenergy, or feed ingredients. In addition, the plant component can supply therapeutic compounds. Further research on characterization of aquaponic components outputs and its quantifications, the impact of using circular inputs generated within the ICAq, and the technical feasibility and economic viability of circular processes in the context of SSA is needed. Full article

The relationships between farmers and livestock are multifaceted. The aim of this study was to describe the sociodemographic, biogeographic, and institutional factors that influence the relationships between humans and animals in the natural savanna. Visits were made to 65 farms, followed by interviews (n = 13) and three focus group interviews (n = 24) directed at farmers and institutional representatives. The results were triangulated to extract the key findings. The following findings were obtained: (a) cultural gender transitions and the lack of generational succession have transformed livestock farming; (b) the relationships between farmers and livestock have favored the implementation of new productive practices and innovations, as well as improvements in animal welfare practices; (c) conditioning factors affecting these relationships include gender discriminatory norms, low profitability and credit access, poor sanitation, animal handling infrastructure, security, and resistance to change; and (d) improvement opportunities include the inclusion of young people and women in livestock farming, education for work practices, credit facilitation, access to technologies, governance, and improvement in the cattle logistics chain. The results are useful for enhancing the relationships between farmers and livestock, guiding training activities, and responsible governance. Full article

The European Union’s developing rural development plan places digital and precision agriculture at the centre of agricultural modernisation and economic development. This article examines how agricultural practices in rural EU regions are being influenced by smart technology, such as drones, IoT sensors, satellite-based research, and AI-driven platforms, through an analysis of recent data from sources across the European Union. This study applies a mixed-methods approach, combining quantitative analysis of strategic policy documents and EU databases, to evaluate the ways in which precision agriculture reduces input consumption, increases productivity, reduces labour shortages and rural area depopulation, and improves sustainability. By investing in infrastructure, developing communities for data exchange, and organising training for farmers, European policies such as the Strategic Plans of the Common Agricultural Policy (CAP), the SmartAgriHubs initiative, and the AgData program actively encourage the transition to digital agriculture. Cyprus is analysed as a case study to show how targeted investments and initiatives supported by the EU can help smaller countries, with limited natural resources, to realise the benefits of digital transformation in agriculture. A special focus is placed on how solutions adapted to agro-climatic and socioeconomic conditions can contribute to strengthening the competitiveness of the agricultural sector, attracting young people to get involved in this field and opening up new economic opportunities. The results of previous research indicate that digital agriculture not only improves productivity but also proves to be a strategic mechanism for attracting and retaining young people in rural areas. Thus, this work additionally contributes to the broader goal of the European Union—the development of smart, inclusive, and sustainable rural areas, in which digital technologies are not only seen as tools for efficiency but also as key means for integrated and long-term rural development. Full article

Information asymmetry leads to farmers’ opportunistic behavior of disobeying pest control regulations in certified vegetable areas, but the improvement of farmers’ digital literacy has become an important means to break through the constrained dilemma of pest control information and change farmers’ pest control behaviors. Based on survey data from certified vegetable areas of Shaanxi, Gansu, and Ningxia provinces in China, this study used Heckman two-stage model to analyze the impact of the improvement of farmers’ digital literacy on opportunistic behavior in pest control. The results are as follows. Firstly, the improvement of farmers’ digital literacy can restrain their opportunistic behavior in pest control. Secondly, the improvement of farmers’ digital literacy restrain their opportunistic behavior through three paths, namely, enhancing the awareness of obeying pest control regulations for certified vegetables, reducing the cost and risk of pest control in obeying the certification standards. Thirdly, the traceable certification label plays a positive moderating role in the process of improving digital literacy to restrain farmers’ opportunistic behavior. Accordingly, this study suggests strengthening the training of farmers’ digital literacy, promoting the digitalized traceability system for certified vegetables, establishing examination mechanisms for online pesticide purchases and logistics distribution, and imposing severe penalties for opportunistic behaviors. Full article

Global changes and society’s development necessitate the improvement of water use and irrigation water saving, which require a set of water management measures to best deal with the necessary changes. This study considers the framework of the change process for water management in the Hetao Irrigation District (HID) of the Yellow River Basin. This paper presents the main measures that have been applied to ensure the sustainability and modernization of Hetao, mitigating water scarcity while maintaining land productivity and environmental value. Several components of modernization projects that have already been implemented are characterized, such as the off-farm canal distribution system, the on-farm surface irrigation, innovative crop and soil management techniques, drainage, and salinity control, including the management of autumn irrigation and advances of drip irrigation at the sector and farm levels. This characterization includes technologies, farmer training, labor needs, energy consumption, water savings, and economic aspects, based on data observed and reported in official reports. Therefore, this study integrates knowledge and analyzes the most limiting aspects in some case studies, evaluating the effectiveness of the solutions used. Full article

The increasingly severe ecological and environmental problems in rural areas pose a serious threat to agricultural sustainability and human well-being. Protecting the ecological environment of cultivated land is fundamental to ensuring food security and achieving sustainable development goals. The effective integration of digital technology into farmers’ production and daily life is a key driver for transforming farming practices and advancing the ecological protection of cultivated land. This study draws on data from the 2020 China Rural Revitalization Survey (CRRS) to systematically examine the impact of farmers’ digital participation on the ecological protection of cultivated land. The main findings are as follows: (1) Digital participation significantly promotes ecological conservation of cultivated land, with each unit increase associated with a 7.8% reduction in fertilizer use intensity; (2) the results are robust across various empirical strategies, including instrumental variable estimation, the ERM approach, residual analysis, and alternative indicator specifications; (3) mechanism analysis indicates that digital participation reduces fertilizer use through three main channels: expansion of social networks (accounting for 7.10%), enhancement of subjective cognition (29.66%), and adoption of agricultural technologies (10.18%); and (4) heterogeneity analysis shows that the protective effects on cultivated land are more pronounced among households with off-farm employment experience, in villages where leaders have higher educational attainment, and in regions with more advanced digital environments. Based on these findings, the following policy recommendations are proposed: enhancing digital infrastructure in rural areas, strengthening the training of agricultural practitioners, and developing localized digital environments tailored to local conditions. Full article

Eliminating all forms of poverty is a core component of the United Nations’ Sustainable Development Goals. At the household level, poverty and income inequality significantly threaten farmers’ sustainable development and food security. Based on a sample of 1234 kiwi farmers from the Shaanxi and Sichuan provinces in China, this paper empirically examines the impact of participation in agricultural industry organizations (AIOs) on household income and income inequality, as well as the underlying mechanisms. The results indicate the following: (1) Participation in AIOs increased farmers’ average household income by approximately 19,570 yuan while simultaneously reducing the income inequality index by an average of 4.1%. (2) Participation increases household income and mitigates income inequality through three mechanisms: promoting agricultural production, enhancing sales premiums, and improving human capital. (3) After addressing endogeneity concerns, farmers participating in leading agribusiness enterprises experienced an additional average income increase of 21,700 yuan compared to those participating in agricultural cooperatives. Therefore, it is recommended to optimize the farmer–enterprise linkage mechanisms within agricultural industry organizations, enhance technical training programs, and strengthen production–marketing integration and market connection systems, aiming to achieve both increased farmer income and improved income distribution. Full article

Agriculture in India supports millions of livelihoods and is a major force behind economic expansion. Challenges in modern agriculture depend on environmental factors (such as soil quality and climate variability) and biotic factors (such as pests and diseases). These challenges can be addressed by advancements in technology (such as sensors, internet of things, communication, etc.) and data-driven approaches (such as machine learning (ML) and deep learning (DL)), which can help with crop yield and sustainability in agriculture. This study introduces an innovative deep neural network (DNN) approach for identifying leaf diseases in paddy crops at an early stage. The proposed neural network is a hybrid DL model comprising feature extraction, channel attention, inception with residual, and classification blocks. Channel attention and inception with residual help extract comprehensive information about the crops and potential diseases. The classification module uses softmax to obtain the score for different classes. The importance of each block is analyzed via an ablation study. To understand the feature extraction ability of the modules, extracted features at different stages are fed to the SVM classifier to obtain the classification accuracy. This technique was experimented on eight classes with 7857 paddy crop images, which were obtained from local paddy fields and freely available open sources. The classification performance of the proposed technique is evaluated according to accuracy, sensitivity, specificity, F1 score, MCC, area under curve (AUC), and receiver operating characteristic (ROC). The model was fine-tuned by setting the hyperparameters (such as batch size, learning rate, optimizer, epoch, and train and test ratio). Training, validation, and testing accuracies of 99.91%, 99.87%, and 99.49%, respectively, were obtained for 20 epochs with a learning rate of 0.001 and sgdm optimizer. The proposed network robustness was studied via an ablation study and with noisy data. The model’s classification performance was evaluated for other agricultural data (such as mango, maize, and wheat diseases). These research outcomes can empower farmers with smarter agricultural practices and contribute to economic growth. Full article

This study investigates the influence of agricultural mechanization on maize productivity in Tanzania’s Ruvuma region, a major maize-producing area vital to national food security. It addresses gaps in understanding the cumulative effects of mechanization across the maize production cycle and identifies region-specific barriers to adoption among smallholder farmers. Focusing on five key stages—land preparation, planting, plant protection, harvesting, and drying—this research evaluated mechanization uptake at each stage and its relationship with yield disparities. Statistical analyses using Python libraries included regression modeling, ANOVA, and hypothesis testing to quantify mechanization–yield relationships, controlling for farm size and socioeconomic factors, revealing a strong positive correlation between mechanization and maize yields (r = 0.86; p < 0.01). Mechanized land preparation, planting, and plant protection significantly boosted productivity (β = 0.75–0.35; p < 0.001). However, harvesting and drying mechanization showed negligible impacts (p > 0.05), likely due to limited adoption by smallholders combined with statistical constraints arising from the small sample size of large-scale farms (n = 20). Large-scale farms achieved 45% higher yields than smallholders (2.9 vs. 2.0 tons/acre; p < 0.001), reflecting systemic inequities in access. These inequities are underscored by the barriers faced by smallholders, who constitute 70% of farmers yet encounter challenges, including high equipment costs, limited credit access, and insufficient technical knowledge. This study advances innovation diffusion theory by demonstrating how inequitable resource access perpetuates low mechanization uptake in smallholder systems. It underscores the need for context-specific, equity-focused interventions. These include cooperative mechanization models for high-impact stages (land preparation and planting); farmer training programs; and policy measures such as targeted subsidies for harvesting equipment and expanded rural credit systems. Public–private partnerships could democratize mechanization access, bridging yield gaps and enhancing food security. These findings advocate for strategies prioritizing smallholder inclusion to sustainably improve Tanzania’s maize productivity. Full article