Search Results (98)

As global food demand continues to rise, conventional agricultural practices face increasing difficulty in sustainably meeting production requirements. In response, deep learning-driven automated systems have emerged as promising solutions for enhancing precision farming. Nevertheless, accurate fruit segmentation remains a significant challenge in orchard environments due to factors such as occlusion, background clutter, and varying lighting conditions. This study proposes the Depthwise Asymmetric Bottleneck with Attention Mechanism Network (DABAMNet), an advanced convolutional neural network (CNN) architecture composed of multiple Depthwise Asymmetric Bottleneck Units (DABou), specifically designed to improve apple segmentation in RGB imagery. The model incorporates the Convolutional Block Attention Module (CBAM), a dual attention mechanism that enhances channel and spatial feature discrimination by adaptively emphasizing salient information while suppressing irrelevant content. Furthermore, the CBAM attention module employs multiple global pooling strategies to enrich feature representation across varying spatial resolutions. Through comprehensive ablation studies, the optimal configuration was identified as early CBAM placement after DABou unit 5, using a reduction ratio of 2 and combined global max-min pooling, which significantly improved segmentation accuracy. DABAMNet achieved an accuracy of 0.9813 and an Intersection over Union (IoU) of 0.7291, outperforming four state-of-the-art CNN benchmarks. These results demonstrate the model’s robustness in complex agricultural scenes and its potential for real-time deployment in fruit detection and harvesting systems. Overall, these findings underscore the value of attention-based architectures for agricultural image segmentation and pave the way for broader applications in sustainable crop monitoring systems. Full article

China holds the largest apple cultivation area globally, yet yields per hectare remain relatively low. Despite substantial government investment in modern orchard technologies, adoption remains limited among farmers. This study investigates the economic and sociological drivers of technology uptake, focusing on how information sources shape adoption behavior. Based on 382 farmer surveys across major apple-producing provinces, the study examines (1) farmers’ preferences for agricultural information sources, (2) the influence of demographic characteristics on those preferences, and (3) the differential effects of specific sources on the adoption of key technologies, including dwarf rootstocks and virus-free seedlings. Results show that agri-chemical dealers (ACDs) and farmer peers (FPs) are the most commonly used information channels. Access to advice from local experts (EXPs) significantly increases the likelihood of adopting dwarf rootstocks, while information from ACDs promotes the use of virus-free seedlings. In contrast, reliance on personal farming experience is negatively associated with technology uptake. These findings highlight the need to strengthen formal information dissemination systems and better integrate trusted local actors like ACDs and EXPs into agricultural extension. Targeted information delivery can improve adoption efficiency, promote evidence-based decision-making, and support the modernization and sustainability of China’s apple sector. Full article

With the increasing importance of green transformation in agricultural production, green pest control technologies (GPCTs), defined as a set of eco-friendly methods aimed at managing agricultural pests with reduced reliance on synthetic chemical pesticides, play a key role in improving agricultural production efficiency, ensuring product quality, and protecting the ecological environment. Based on field survey data from apple farmers in Yantai and Linyi cities, Shandong Province, collected in 2022, this paper employs endogenous treatment effects regression (ETR) and instrumental variable quantile regression (IVQR) models to analyze the impact of adopting green pest control technologies on household income and explores the heterogeneity of this effect across different income levels. The results show that the adoption of green pest control technologies significantly increases apple farmers’ net apple income and household income, confirming their income-boosting effect. Moreover, the income-boosting effect is more significant for lower-income farmers, suggesting that these farmers benefit more from the adoption of green pest control technologies by improving pest management and thus enhancing apple production efficiency. This study provides empirical evidence for the promotion of green pest control technologies and offers valuable references for policymakers, especially in supporting technology adoption among lower-income farmers. Full article

Based on a sample of 412 apple farmer households across Gansu, Shaanxi, Shanxi, and Shandong provinces in China, this study estimates production efficiency and its determinants for apple growers. The stochastic frontier analysis model estimates technical efficiency while the Tobit model identifies influencing factors. Results show that the average production efficiency of smallholder apple farmers is relatively low at 0.45, indicating significant room for improvement. Production efficiency exhibits an inverted “U” relationship with farm scale, and excessive pesticide inputs have a significant negative impact on efficiency. Computer use to search for information among farmers was found to significantly improve apple production efficiency, indicating the potential benefits of ICT adoption. However, membership in cooperatives had no significant effect on efficiency. Overall, these findings suggest approaches to enhance the productivity of China’s apple growers through improved resource allocation, optimized farm scale, and the promotion of information technology. Full article

With the surge in digital farming, real-time quality management of fresh produce has become essential. For apples (Malus domestica Borkh.), consumer demand extends beyond sweetness, texture, and appearance to internal quality factors such as moisture content. Existing non-destructive methods, however, involve costly equipment, complex calibration, and sensitivity to environmental conditions. This study hypothesizes that thermal diffusivity indices derived from surface heating and cooling patterns can accurately predict apple moisture content non-destructively. A total of 823 apples from seven varieties were analyzed using a thermal imaging sensor in a 120-s process comprising 40 s of heating and 80 s of cooling. Key thermal diffusivity indices—minimum, maximum, mean, and max–min values—were extracted and correlated with actual moisture content measured via the drying method. Multiple linear regression and leave-one-out cross-validation confirmed that mean temperature-based models provided the most stable predictions ($R_{CV}^2$ ≥ 0.90 for some varieties). Frame optimization and artificial neural networks further improved prediction accuracy for varieties exhibiting higher variability. The proposed method is cost-effective, requires minimal calibration, and is less affected by surface reflectance, outperforming conventional optical methods (e.g., NIR spectroscopy, hyperspectral imaging), especially regarding robustness against surface reflectance variability and calibration complexity. This offers a practical solution for monitoring apple freshness and quality during sorting and distribution processes, with expanded research on sugar content and acidity expected to accelerate commercialization. Full article

Grey mould caused by Botrytis cinerea presents significant challenges to apple production including organic farming. Biocontrol yeasts and their consortia can limit fungal diseases. This study evaluates the efficacy of selected yeast isolates and their consortia in suppressing B. cinerea in stored apples. The yeast strains tested—Wickerhamomyces anomalus 114/73, Naganishia albidosimilis 117/10, and Sporobolomyces roseus 117/67—were assessed at 4 °C and 23 °C, individually and in consortia. The results demonstrate the superior efficacy of a consortium combining all three isolates, which achieved the highest reduction in spore germination and disease severity. A two-strain consortium of isolates 114/73 and 117/10 also showed substantial biocontrol activity, outperforming single-strain treatments. These combinations effectively suppressed B. cinerea growth and displayed rapid colonization of apple wounds. The study highlights the potential of yeast isolates and their consortia to manage postharvest fungal decay, addressing a critical need for sustainable, eco-friendly solutions in organic apple production. Full article

This study proposes an object detector for apple trees as a first step in developing agricultural digital twins. An original dataset of orchard images was created and used to train Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) models. Performance was evaluated using mean Average Precision (mAP). YOLO significantly outperformed SSD, achieving 91.3% mAP compared to the SSD’s 46.7%. Results indicate YOLO’s Darknet-53 backbone extracts more complex features suited to tree detection. This work demonstrates the potential of deep learning for automated data collection in smart farming applications. Full article

The present study explores the impact of farm scale on environmental efficiency to provide theoretical support and policy reference for the modernization and sustainable development of the apple industry. The study is based on research data from apple farmers in three counties of the Shaanxi and Gansu provinces in 2021. Firstly, the life cycle approach is applied to assess the comprehensive environmental pollution emissions in apple production and to clarify the non-desired outputs. Secondly, the environmental efficiency of apple production is measured using the SBM model, based on which the Tobit model is utilized to explore the impact of operation scale on the environmental efficiency of apple production and its potential mechanism of action. The results of the study show the following: (1) The mean environmental efficiency of the farmers in the sample is 0.278, indicating that the overall environmental efficiency of apple production is low; (2) there is an inverted U-shaped relationship between the scale of operation and the environmental efficiency of apple production, and the results are robust. This analysis was conducted after addressing endogeneity. Thirdly, the study found that the intensity of the adoption of green technology and farmers’ environmental awareness play a significant mediating role in the influence of business scale on the environmental efficiency of apple production. The potential mechanism of the effect of the scale of operation on the environmental efficiency of apple production was also investigated. Consequently, it is recommended to expedite the promotion of moderate-scale orchard operations, to proactively cultivate new management entities, and to enhance the adoption level of green technology and environmental cognition among farmers. These measures are proposed to encourage sustainable and high-quality development in the apple industry. Full article

The widespread use of pesticides has long been a cornerstone of modern agriculture, but their overuse has led to several challenges, including increased production costs, food safety risks, and environmental damage. Green pest control technologies (GPCTs) have emerged as a promising alternative to traditional chemical methods, although their widespread adoption is still in progress, and their environmental and economic impacts require further examination. This study aims to evaluate the adoption of GPCT in apple orchards by employing a rigorous framework to measure pesticide intensity per unit, assess the impact of GPCT on pesticide reduction, and analyze the associated environmental effects in large-scale apple farming systems in China. Based on survey data collected from apple farmers across key production regions in China, we apply an Endogenous Treatment Effect Regression (ETR) model to evaluate the effects of these technologies on pesticide usage and concentration. This model allows for more accurate estimates of the treatment effects by addressing selection bias and accounting for both observable and unobservable factors. Our results show that the adoption of GPCT leads to a significant reduction in pesticide use intensity. Notably, the reductions are more pronounced among specific groups of farmers, particularly those who are less risk-averse and those with larger or more fragmented landholdings. These findings underscore the dual ecological and economic benefits of GPCT, providing strong support for policy initiatives that promote sustainable agricultural practices and encourage land consolidation. Full article

Fruit growing is important in the global agricultural economy, contributing significantly to food security, job creation, and rural development. With the advancement of technologies, mapping fruits using remote sensing and machine learning (ML) and deep learning (DL) techniques has become an essential tool to optimize production, monitor crop health, and predict harvests with greater accuracy. This study was developed in four main stages. In the first stage, a comprehensive review of the existing literature was made from July 2018 (first article found) to June 2024, totaling 117 articles. In the second stage, a general analysis of the data obtained was made, such as the identification of the most studied fruits with the techniques of interest. In the third stage, a more in-depth analysis was made focusing on apples and grapes, with 27 and 30 articles, respectively. The analysis included the use of remote sensing (orbital and proximal) imagery and ML/DL algorithms to map crop areas, detect diseases, and monitor crop development, among other analyses. The fourth stage shows the data’s potential application in a Southern Brazilian region, known for apple and grape production. This study demonstrates how the integration of modern technologies can transform fruit farming, promoting more sustainable and efficient agriculture through remote sensing and artificial intelligence technologies. Full article

Flavor is the most important feature consumers use to examine fruit ripeness, and it also has an important influence on taste sensation. Nowadays, more and more consumers pay much attention not only to the appearance but also to the fruit’s aroma. Exploiting the potential of headspace solid-phase microextraction (HS-SPME) combined with sensitive two-dimensional gas chromatography and the time-of-flight mass spectrometry (GC/GC-ToF-MS) method within 30 old/traditional cultivars of apples (Malus domestica Borkh) coming from the same germplasm and 7 modern/commercial cultivars, 119 volatile organic compounds (VOCs) were identified. The largest group was esters (53), followed by alcohols (20), aldehydes (17), ketones (10), and acids (10). The richest volatile profile was ‘Grochówka’, with 61 VOCs present. The results revealed a visible difference based on VOC levels and profiles between the different apple cultivars, as well as visible similarities within the same cultivar coming from different farms. Based on a PCA, the commercial cultivars were separated into 7 clusters, including (1) ‘Gala’, (2) ‘Melrose’, (3) ‘Red Prince’, (4) ‘Lobo’, (5) ‘Ligol’, and (6) ‘Szampion’. The results of this study indicate that the profile of volatile compounds may be a useful tool for distinguishing between commercial and old apple cultivars, as well as for the varietal classification of apples from different locations. The developed method can also be used to identify other fruit varieties and origins based on their VOC composition. This may prove to be particularly valuable in the case of establishing a Protected Designation of Origin or Protected Geographical Indication. Full article

The adoption of organic fertilizers is essential for advancing China’s green agricultural transformation, ensuring food security, and supporting agricultural adaptations. However, several challenges hinder its widespread use in rural areas. This study examines how part-time farming and agricultural service provision influences organic fertilizer use, employing fixed and random effects models on data from 523 households in Shaanxi Province, one of China’s main apple-producing regions. The results reveal: (1) Part-time farming reduces organic fertilizer use by 7.6%, primarily due to labor shortages; (2) Higher non-farm income exacerbates this decline, particularly for Type II part-time farmers; and (3) Mechanized fertilization services help mitigate this reduction. These findings offer valuable policy insights for promoting organic fertilizer adoption in the context of shifting rural labor dynamics and highlight the complex interactions between farming practices and labor migration in the broader trajectory of organic fertilizer use. Moreover, this study highlights the role of organic fertilizer use in enhancing food security while also helping to reduce the carbon footprint of the crop sector in China. Full article

It is debated whether the ecotoxicity of active substances (ASs) contained in synthetic pesticides applied in conventional agriculture (conASs) differs from nature-based ASs used in organic agriculture (orgASs). Using the official pesticide use statistics, we evaluated the ecotoxicity of ASs used in apple and grapevine production in Austria. In 2022, 49 conASs and 21 orgASs were authorized for apple production and 60 conASs and 23 orgASs were authorized for grapevine production in Austria. Based on the latest publicly available data on the actual use of pesticides in apple and grapevine production (from the year 2017), we evaluated their ecotoxicity based on information in the freely accessible Pesticide Properties and Bio-Pesticides Databases. The results showed that although the amount of ASs applied per hectare of field was higher in organic farming, the intrinsic toxicities of ASs used in conventional farming were much higher. The number of lethal toxic doses (LD₅₀) of ASs applied in conventional apple orchards was 645%, 15%, and 6011% higher for honeybees, birds, and earthworms, respectively, than in organic apple production. In conventional vineyards, lethal doses for honeybees, birds, and earthworms were 300%, 129%, and 299% higher than in organic vineyards. We conclude that promoting organic farming would therefore contribute to the better protection of biodiversity on agricultural land and beyond. Full article

This study investigates the economic and ecological impacts of straw returning technology among apple growers in Shaanxi and Gansu provinces, China. Using Propensity Score Matching (PSM) and survey data, the findings reveal that straw returning significantly increases farmers’ incomes by 20.33% compared to those who do not adopt the technology. Additionally, the technology mitigates soil fertility decline by 11.07%, offering substantial ecological benefits. The heterogeneity analysis highlights that older farmers benefit more from the technology in terms of both income and soil fertility improvement, likely due to their experience and reliance on farming. Smaller-scale farmers also show greater gains in income and soil health, while larger-scale farms face complexities that may delay visible benefits. However, land fragmentation did not significantly influence the outcomes. The study recommends promoting straw returning through enhanced farmer training, financial incentives, and improved access to credit. Policymakers should consider tailoring support to different farmer demographics and orchard sizes. Future research should focus on long-term evaluations of straw returning’s sustainability in terms of soil fertility and crop yields. Overall, straw returning technology offers a promising solution for enhancing both economic returns and environmental sustainability in apple production. Full article

Modern agricultural research, including fruit tree rootstock evaluations, began in England. In the mid-1800s, field plots were established at the Rothamsted Research Station to evaluate cultivars and fertilizer treatments for annual crops. By the early 1900s, farmers questioned the value of field experimentation because the results were not always valid due to inadequate randomization and replication and poor data summarization. During the first half of the 20th century, Rothamsted statisticians transformed field plot experimentation. Field trials were tremendously improved by incorporating new experimental concepts, such as randomization rather than systematically arranging treatments, the factorial arrangement of treatments to simultaneously test multiple hypotheses, and consideration of experimental error. Following the classification of clonal apple rootstocks at the East Malling Research Station in the 1920s, the first rootstock trials were established to compare rootstocks and evaluate rootstock performance on different soil types and with different scion cultivars. Although most of the statistical methods were developed for annual crops and perennial crops are more variable and difficult to work with, rootstock researchers were early adopters of these concepts because the East Malling staff included both pomologists and statisticians. Many of the new statistical concepts were incorporated into on-farm demonstration plots to promote early farmer adoption of new practices. Recent enhancements in computing power have led to the rapid expansion of statistical theory, the development of new statistical methods, and new statistical programming environments, such as R. Over the past century, in many regions of the world, the adoption of new statistical methods has lagged their development. This review is intended to summarize the adoption of error-controlling experimental designs by rootstock researchers, to describe statistical methods used to summarize the resulting data, and to provide suggestions for designing and analyzing future trials. Full article