Agriculture is considered one of the most important sectors that play a strategic role in ensuring food security. However, with the increasing world‘s population, agri-food demands are growing — posing the need to switch from traditional agricultural methods to smart agriculture practices, also known as agriculture 4.0. To fully benefit from the potential of agriculture 4.0, it is significant to understand and address the problems and challenges associated with it. This study, therefore, aims to contribute to the development of agriculture 4.0 by investigating the emerging trends of digital technologies in the agricultural industry. For this purpose, a systematic literature review based on Protocol of Preferred Reporting Items for Systematic Reviews and MetaAnalyses is conducted to analyse the scientific literature related to crop farming published in the last decade. After applying the protocol, 148 papers were selected and the extent of digital technologies adoption in agriculture was examined in the context of service type, technology readiness level, and farm type. The results have shown that digital technologies such as autonomous robotic systems, internet of things, and machine learning are significantly explored and open-air farms are frequently considered in research studies (69%), contrary to indoor farms (31%). Moreover, it is observed that most use cases are still in the prototypical phase. Finally, potential roadblocks to the digitization of the agriculture sector were identified and classified at technical and socio-economic levels. This comprehensive review results in providing useful information on the current status of digital technologies in agriculture along with prospective future opportunities.
Maulshree Singh, Rupali Srivastava, E. Fuenmayor
et al.
One of the most promising technologies that is driving digitalization in several industries is Digital Twin (DT). DT refers to the digital replica or model of any physical object (physical twin). What differentiates DT from simulation and other digital or CAD models is the automatic bidirectional exchange of data between digital and physical twins in real-time. The benefits of implementing DT in any sector include reduced operational costs and time, increased productivity, better decision making, improved predictive/preventive maintenance, etc. As a result, its implementation is expected to grow exponentially in the coming decades as, with the advent of Industry 4.0, products and systems have become more intelligent, relaying on collection and storing incremental amounts of data. Connecting that data effectively to DTs can open up many new opportunities and this paper explores different industrial sectors where the implementation of DT is taking advantage of these opportunities and how these opportunities are taking the industry forward. The paper covers the applications of DT in 13 different industries including the manufacturing, agriculture, education, construction, medicine, and retail, along with the industrial use case in these industries.
A comprehensive understanding of the relationships among carbon emissions, the industrial structure and economic growth holds great significance for China's transition to a low-carbon economy, industrial structure optimization, and achievement of energy conservation and emission reduction targets. We selected six major industrial sectors (agriculture, industry, construction, transportation, retail and accommodation and other industries) as research objects, introduced the extended STIRPAT decomposition model, Tapio decoupling model and the grey relation analysis to discuss the relationship among the three. Results showed that (i) since 2000, the proportions of value added of agriculture, manufacturing, and transportation are negatively correlated with carbon emissions, while those of construction, retail and accommodation, and other industries are positively correlated with carbon emissions. (ii) The overall economic growth and carbon emissions of these six major industries have experienced the process of decoupling-coupling-decoupling-coupling-decoupling. (iii) The relevance of these six industries to GDP is ranked as follows: transportation > manufacturing > retail andaccommodation > agriculture > construction > other industries. Additionally, accelerating the achievement of a clean energy structure, strengthening the strength and speed of industrial structure adjustment and reducing the dependence on fossil energy are the key steps for China to reach carbon emissions peak goal.
Tofu production produces solid waste and liquid waste which can pollute the environment. Industrial wastewater
treatment has been carried out using various methods, one of which is the electrocoagulation method. The
purpose of this study was to analyze the effect of distance between electrodes and contact time on reducing TSS,
TDS, turbidity, and pH levels of tofu industrial wastewater and to obtain the best treatment in reducing pH, TSS,
TDS, turbidity, and COD levels. The research was conducted using a Factorial Randomized Block Design with 3
replications. The electrocoagulation process was carried out using variations of distance between electrode of 1.0,
1.5, and 2.0 cm and contact times of 30, 45, and 60 minutes. The experimental data were analyzed using Analysis
of Variance (ANOVA) and Honestly Significant Difference (HSD) test at a significance level of 5%. Based on the
results of the study, it was concluded that the distance between electrodes and contact time affected the reduction
of wastewater pollutants. The results showed that a distance between electrodes of 1.0 cm with a contact time of
60 minutes was the best treatment, which resulted in the largest reduction in TSS, TDS, and turbidity values of up
to 69.05 percent, 59.34 percent, and 98.15 percent, respectively, the largest increase in pH of up to 28.45 percent,
and reduction of COD of up 48.90 percent.
Keywords: electrocoagulation; electrode distance; contact time
Reducing methane emissions from dairy goat production is essential for meeting sustainability targets and consumer expectations. Accurate quantification of enteric methane per unit of feed intake is a prerequisite for developing emission inventories and mitigation strategies. This pilot study developed and validated a methodology for measuring methane emissions from dairy goats using respiration chambers originally designed for cattle. Ten nonlactating Saanen dairy goats (mean ± standard deviation liveweight: 63 ± 9 kg) were group‐housed for 2 days and, then paired and housed in small pens for 9 days to adapt to the diet and environment. They were fed pasture silage ad libitum , supplemented with 200 g of maize grain daily. Four pairs were selected for methane measurements in cattle chambers following a 2‐day acclimation period. Methane emissions were recorded over 48 h at an airflow rate of 756 L/min. The mean methane yield was 22.9 ± 1.4 g/kg dry matter intake, consistent with values reported for sheep and cattle. Power analysis indicated that six pairs per treatment group are required to detect a 10% difference in methane yield with 80% power. These findings establish a validated protocol for using cattle respiration chambers to measure methane emissions from dairy goats.
The evolutionary history of Yunnanopilia longistaminea, a vulnerable plant endemic to the Yuanjiang-Honghe River Valley in southwestern China, was investigated using cpDNA and nrDNA sequences along with ecological niche modeling. Understanding the genetic diversity and population structure of Y. longistaminea is crucial for developing effective conservation strategies and managing its genetic resources. This study comprehensively sampled 295 individuals from 16 populations, which represent the species’ entire global distribution range, ensuring a thorough and representative analysis of its genetic diversity and population structure. The results revealed high genetic diversity and population structure, with significant genetic differentiation among populations. Specifically, the total nucleotide diversity was 2.40 × 10−3 for cpDNA and 1.51 × 10−3 for nrDNA, while the total haplotype diversity was 0.605 for cpDNA and 0.526 for nrDNA. The divergence time of ancestral haplotypes of Y. longistaminea was estimated to be around 2.19 million years ago based on nrDNA and 2.72 million years ago based on cpDNA. These divergence times are comparable to those of other ancient plant species, suggesting a long evolutionary history. The population size of Y. longistaminea was found to have significantly declined around 30,000 years ago. The current distribution model suggests that Y. longistaminea primarily inhabits the warm temperate zone of China, and the LGM distribution model predicts a concentration of the species in Yuanjiang-Honghe River Valley in southwestern China. This study concludes that the southwestern region of China may have served as a glacial refuge for Y. longistaminea. These findings suggest that establishing protected areas in these regions and creating gene banks for ex situ conservation could be effective strategies to preserve the genetic diversity of Y. longistaminea. Further research on its population dynamics and genetic adaptation to climate change is valuable for understanding the species’ evolutionary history and conservation.
Agricultural carbon emissions pose a significant challenge in combating climate change and achieving sustainable development objectives. These emissions predominantly stem from the decisions made by stakeholders, and the potential economic and social benefits associated with agricultural product brands determine their capacity to influence stakeholder behavior. This study utilizes panel data from 30 provinces in China spanning from 2008 to 2021 to examine the impact of agricultural product brands on agricultural carbon emission intensity and its underlying mechanisms. The findings indicate that: (1) Agricultural product brands contribute to lowering agricultural carbon emission intensity within a region, while also exerting a negative spillover effect on neighboring areas. (2) Agricultural product brands foster a decline in agricultural carbon emission intensity by bolstering the agricultural industry agglomeration. (3) Agricultural scale operation exhibits a threshold effect between agricultural product brands and agricultural carbon emission intensity, with the mitigation effect becoming increasingly pronounced as the threshold range expands. The research findings can offer valuable insights into leveraging the advantages of agricultural product brands to facilitate the reduction of agricultural carbon emissions.
The use of plant protection products (PPP) for plant protection can be substantially reduced by applying PPP on plants only. A novel variable–rate sprayer with a stereo vision system to apply PPP only to grapevines was developed for vineyard applications. The stereo vision system was evaluated as a grapevine shoot detection sensor under various conditions in average grapevine shoot lengths (4.1–29.1 cm), travel speeds (3.2–8.0 km h-1) and outdoor illuminations (12,718 – 67,912.0 lx). A real-time variable–rate sprayer prototype with two air assistance levels at the air outlet (low (air speed of 5.9 m s-1) and high (11.3 m s-1)) tested for its spray performance against a conventional sprayer. The results show that the stereo vision system detected 95.1 % to 99.8 % of grapevine shoots in average under various conditions, and no influences of the shoot size, outdoor illuminations, and travel speeds on the shoot detection were observed under the test conditions. The prototype sprayer had up to approximately 2.8 times more spray deposit averages compared to the conventional sprayer, which was a significant increase (p < 0.05). Insignificant increases of spray deposit on grapevine shoots next to the sprayer, and spray drift to an adjacent row were observed from spray applications with low and high air assistance. A similar trend was observed in spray coverage data. These results demonstrate that the novel stereo vision real-time variable rate sprayer is feasible for vineyard applications.
Although poly (lactic acid) (PLA) is a good environmentally-friendly bio-degradable polymer which is used to substitute traditional petrochemical-based polymer packaging films, the barrier properties of PLA films are still insufficient for high-barrier packaging applications. In this study, oxygen scavenger hydroxyl-terminated polybutadiene (HTPB) and cobalt salt catalyst were incorporated into the PLA/poly (butylene adipate-co-terephthalate) (PLA/PBAT), followed by melting extrusion and three-layer co-extrusion blown film process to prepare the composite films. The oxygen permeability coefficient of the composite film combined with 6 wt% oxygen scavenger and 0.4 wt% catalyst was decreased significantly from 377.00 cc·mil·m−2·day−1·0.1 MPa−1 to 0.98 cc·mil·m−2·day−1·0.1 MPa−1, showing a remarkable enhancement of 384.69 times compared with the PLA/PBAT composite film. Meanwhile, the degradation behavior of the composite film was also accelerated, exhibiting a mass loss of nearly 60% of the original mass after seven days of degradation in an alkaline environment, whereas PLA/PBAT composite film only showed a mass loss of 32%. This work has successfully prepared PLA/PBAT composite films with simultaneously improved oxygen barrier property and degradation behavior, which has great potential for high-demanding green chemistry packaging industries, including food, agricultural, and military packaging.
Arindam Sikdar, Abir.U. Igamberdiev, Shangpeng Sun
et al.
Vaccinium vitis-idaea L. (lingonberry), globally recognized as a superfruit for its medicinal properties, has long been cultivated and consumed by Canadian Indigenous communities. This study introduces an AI-powered surveillance system that leverages an optimized You Only Look Once (YOLOv12) architecture to revolutionize yield estimation, phenomic profiling, and genomic/epigenomic analysis in micropropagated lingonberry. A custom multi-class annotated dataset was developed to evaluate model performance under real-world conditions. The YOLOv12 model, built on a RELAN backbone with flash-attention mechanisms, excelled in global context modeling, enabling accurate detection of berries and regenerated shoots in both ex vitro and in vitro environments. In contrast, YOLOv8 and YOLOv9, which rely on CNN-based feature extraction, demonstrated computational efficiency but suffered from overfitting and reduced operational robustness. In multi-class detection scenarios, YOLOv12 achieved the highest mean Average Precision with 67.3 % mAP@50 in yield detection, 1.0–99.5 % mAP@50 in micropropagated plant trait detection (shoots, berries, flowers), and 32.2–74 % accuracy in gel electrophoresis band detection. These results reflect a 22 % increase in throughput and a 38 % reduction in error rates compared to conventionally human-monitored methods, significantly reducing labor cost for plant breeders and agricultural biotechnologist. The integrated system enables simultaneous monitoring of phenotypic traits across growth stages and precise molecular band analysis, establishing a new paradigm for precision agriculture and lingonberry improvement.This work establishes YOLOv12 as the first unified framework for micropropagated lingonberry phenotyping across biological scales, demonstrating labor reduction in breeding programs while maintaining operational reliability. The technology's mobile compatibility and cloud-integration potential offer immediate applications for the global $2.3B lingonberry market, particularly in precision nurseries and nutraceutical production.
Khaoula Bakas, Amine Saddik, Azzedine Dliou
et al.
In recent years, arid and semi-arid regions have faced severe and persistent drought, with rainfall at most 200 mm per year. In addition, intensive irrigation practices in agribusiness areas aimed at boosting production further increased irrigation water consumption. These practices call for innovative applications that enable optimized yield estimation and tree health monitoring. This paper aims to predict crop yield in a citrus orchard farm using UAV imagery and Deep Learning approaches. It emphasizes the use of a lightweight Tiny U-Net model for tree detection and a CNN-based architecture for crop yield estimation based on vegetation indices and in-situ measurement data. The study was designed to provide a cost-effective solution for precision orchard management and monitoring under climate stress. The CNN model outperformed other machine learning models in yield prediction, achieving the highest coefficient of determination (R2) of 88%. The Tiny U-Net architecture, developed for semantic segmentation and counting, effectively distinguished individual citrus trees and rows. The model reached high accuracy, with overall precision and recall reaching 94.74% and 94.88%, respectively, and maintained a low inference time of 12.55 ms, making it suitable for real-time and on-boarding processing. The segmentation output enabled an accurate counting of both trees and rows, with a R2 exceeding 99%, confirming the reliability of the model for structural orchard analysis. The pipeline supports precision agriculture through reliable and high-resolution yield monitoring, enabling informed decision-making for citrus orchard management and resource optimization.
In the current scenario of changing climatic conditions and the rising global population, there is an urgent need to explore novel, efficient, and economical natural products for the benefit of humankind. Biosurfactants are one of the latest explored microbial synthesized biomolecules that have been used in numerous fields, including agriculture, pharmaceuticals, cosmetics, food processing, and environment-cleaning industries, as a source of raw materials, for the lubrication, wetting, foaming, emulsions formulations, and as stabilizing dispersions. The amphiphilic nature of biosurfactants have shown to be a great advantage, distributing themselves into two immiscible surfaces by reducing the interfacial surface tension and increasing the solubility of hydrophobic compounds. Furthermore, their eco-friendly nature, low or even no toxic nature, durability at higher temperatures, and ability to withstand a wide range of pH fluctuations make microbial surfactants preferable compared to their chemical counterparts. Additionally, biosurfactants can obviate the oxidation flow by eliciting antioxidant properties, antimicrobial and anticancer activities, and drug delivery systems, further broadening their applicability in the food and pharmaceutical industries. Nowadays, biosurfactants have been broadly utilized to improve the soil quality by improving the concentration of trace elements and have either been mixed with pesticides or applied singly on the plant surfaces for plant disease management. In the present review, we summarize the latest research on microbial synthesized biosurfactant compounds, the limiting factors of biosurfactant production, their application in improving soil quality and plant disease management, and their use as antioxidant or antimicrobial compounds in the pharmaceutical industries.
For over a decade, bovine anaemia caused by <i>Theileria orientalis</i> Ikeda has been a significant disease in the Australian cattle industry. In this study, we conducted a spatial and temporal analysis of theileriosis in Australia using historic data from submissions to the New South Wales Department of Primary Industries (NSW DPI) from 2006 to 2022, where herd history, clinical signs, and PCR results were available. Since the first detections of bovine theileriosis in the Sydney area in 2006, the disease spread north- and southward and is now endemic to the southeast coast of Australia, closely mirroring the distribution of the principal vector <i>Haemaphysalis longicornis.</i> Across all years, the prevalence of the Ikeda genotype was 88%, while the prevalence of the benign Chitose and Buffeli genotypes was 55% and 38%, respectively. The majority of submissions were from beef cattle in coastal NSW, with anaemia, fever, jaundice, abortion, and lethargy the most frequently reported clinical signs. Transportation was identified as the major risk factor for disease. Until 2015, the majority of cases were reported in adult cattle, while in later years, calves made up the majority of cases, most likely due to the widespread acquisition of immunity in adults. Calves were significantly more likely to present with diarrhoea, lethargy, and anaemia, and to suffer mortality, while adults were significantly more likely to present with jaundice. Instances of abortion were observed to be significantly associated with beef cattle. The relationship between the level of parasitaemia and anaemia revealed a strong negative correlation for all animals examined.
Shabbir Ahmed Osmani, Jongjin Baik, Roya Narimani
et al.
Agricultural productivity is highly correlated with climatic variations, including drought events. This study is aimed at performing a comprehensive assessment of agricultural drought conditions in the Köppen–Geiger climate zones in Bangladesh, based on the temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI). The zones are classified as follows: temperate dry winter with a hot summer zone, tropical savannah zone, and tropical monsoon zone. The range of VHI is optimized based on its correlation with TCI and VCI. The correlation among the stratified drought indices is also examined to quantify and measure the strength and direction of the linear relationships between them. Moreover, Mann–Kendall test is conducted to assess the statistical trends in the spatiotemporal propagation of droughts across different climate zones. The balanced correlation approach for VHI reveals that vegetation health is governed by the temperature conditions. Certain variations in the drought intensity, frequency, and duration are observed across climatic zones in earlier years while the recent years are noted with less droughts. Normal or no drought conditions are noticed mostly in the tropical monsoon zone through VCI and VHI. The correlations among the drought classes indicate that VHI is more strongly correlated with TCI than with VCI, while NDVI exhibits stronger correlations with VCI than with either VHI or TCI. The Mann-Kendall test revealed that VCI has significant downward trends in drought categories and an upward trend in normal conditions, whereas VHI and TCI displayed inconsistency in statistical trends. By extensively exploring the agricultural drought conditions within specific climate zones, this study offers valuable insights for agronomists and stakeholders involved in climate resilience planning and agricultural development in Bangladesh.
Due to increasing global water scarcity pressure, researchers, policy makers and industry are looking for innovative solutions to increasing agricultural water productivity. Motivated by recent success within complex decision-making environments, Deep Reinforcement Learning (DRL) is being proposed as a method for optimizing irrigation strategies. Early research has hinted towards increased profits with DRL compared to heuristic approaches such as soil-moisture thresholds or fixed schedules. However, an assessment of the value of DRL for irrigation scheduling that incorporates local climate variability and water-use restrictions has yet to be performed. To address this gap in the literature, we created aquacrop-gym, an open-source Python framework for researchers to train and evaluate customized irrigation strategies within the crop-water model AquaCrop-OSPy. In this analysis, aquacrop-gym was used to quantify the value of DRL in comparison to conventional irrigation scheduling techniques (e.g., optimized soil-moisture heuristic) for maize production in an intensively irrigated region of the central United States. The DRL and heuristic approaches were both trained on 70 years of weather data produced from the weather generator LARS-WG, and evaluated on 30 unseen validation years of generated weather data. Findings from this analysis show that in the presence of high rainfall variability, DRL does not outperform conventional optimized heuristics. However, in the scenario where rainfall is set to zero, DRL approaches achieve higher profits on the unseen validation years. Similarly, DRL approaches also outperform optimized heuristics when severe water-use restrictions are introduced. Our analysis demonstrates that DRL approaches are a promising method of irrigation scheduling, notably in regions where farmers are faced with significant physical or regulatory water scarcity.
Muhammad Ricza Irhamni, Khoirul Muna, Wildan Yusrul Falah
This paper examines the impact of motivation and the work environment in supporting the productivity of chili farmers in Magelang. We use managerial work motivation theory to identify farmer motivation and use the work environment as a measuring tool to determine the environmental conditions around agricultural land. The quantitative model is estimated to show that motivation and work environment can significantly boost farmer productivity. The sample in this study was 64 respondents, regression analysis was used in this study using SPSS 22. Farmers with high motivation will increase the productivity of their crops, although this is not significant. Finally, farmers with a good working environment will increase farmer productivity significantly.