The bioconversion of lignocellulosic biomass is a promising method for the production of bio-energy, biomolecules and biomaterials. Pretreatment of the lignocellulosic biomass is an essential step in this process. The choice of pretreatment process is a difficult one, and there are currently no clear criteria on which to base this choice. This project, with its sustainability and agri-food perspective, used environmental impacts to assess the various processes and their panels of technologies. The approach developed integrates big data, to improve sustainability management in supply chain design, with the aim of valorising agricultural waste. In five main steps, this approach combines concepts from industry 4.0, sustainability and the agri-food industry. We apply this approach to a case study in the domain of agricultural waste valorisation: the pretreatment of lignocellulosic biomass in the rice supply chain.
Over the last century, agriculture transformed from a labour-intensive industry towards mechanisation and power-intensive production systems, while over the last 15 years agricultural industry has started to digitise. Through this transformation there was a continuous labour outflow from agriculture, mainly from standardized tasks within production process. Robots and artificial intelligence can now be used to conduct non-standardised tasks (e.g. fruit picking, selective weeding, crop sensing) previously reserved for human workers and at economically feasible costs. As a consequence, automation is no longer restricted to standardized tasks within agricultural production (e.g. ploughing, combine harvesting). In addition, many job roles in agriculture may be augmented but not replaced by robots. Robots in many instances will work collaboratively with humans. This new robotic ecosystem creates complex ethical, legislative and social impacts. A key question, we consider here, is what are the short and mid-term effects of robotised agriculture on sector jobs and employment? The presented work outlines the conditions, constraints, and inherent relationships between labour input and technology input in bio-production, as well as, provides the procedural framework and research design to be followed in order to evaluate the effect of adoption automation and robotics in agriculture.
Abstract Agriculture in developing countries is a vulnerable sector and is subsidized. Environmental restrictions on agriculture sector are minimal. The excessive use of chemical fertilizers and pesticides has caused serious non-point source water pollution. Although previous studies have focused on agricultural water pollution, the cooperation between agricultural and industrial sectors in water pollution mitigation has not been thoroughly discussed. In this context, this paper introduces a cross-sectoral water pollution dynamic model. We studied the feedback (subgame perfect) equilibrium of the non-cooperative game between two sectors. To discern the impact of environmental policy, we compared the equilibrium solutions of two scenarios that differ in terms of environmental policy for agricultural sector mitigation. The results show that when agricultural and industrial sectors work together to mitigate water pollution, the accumulated common pollutants level decreases; when sectors consider mitigation measures in their objectives, they tend to emit more pollutants; the damage produced by both sectors is lower when both sectors participate in mitigation. Our policy recommendation is that cross-sectoral water pollution control is essential and developing countries should include both agricultural and industrial sectors in environmental governance to maximize the coordination of cross-sectoral environmental policies.
Accurate data acquisition of crop morphological parameters is crucial for effective greenhouse management decision-making and remote sensing technologies are increasingly being applied to automate the data collection process. This research utilised an RGB-D based computer vision method to investigate the correlation between the computer vision features and the lettuce morphological parameters, including leaf area, plant height, diameter, and fresh weight. A dataset of lettuce containing over 300 RGB images and depth images of the 3rd Autonomous Greenhouse Challenge was used, and Random Forest, XGBoost and linear regression models were applied in the prediction. The best NRMSE values for diameter, dry matter content, dry weight, fresh weight, height, and leaf area are 0.08, 0.08, 0.07, 0.07, 0.08, and 0.07, which showed a promising accuracy compared to similar studies. This research demonstrates a novel approach to non-destructively estimate greenhouse leafy vegetable morphological parameters.
The tomato crop is an important and major vegetable crop on the Iraqi table, And his entry into a lot of food industries like tomato paste and some other products, Among them, the research aimed to derive the resource demand function and the output supply function of the production function and identify the optimal resource combinations of tomato farmers in the bath of Al-Alil for the 2023 agricultural season as one of the cultivation areas of this crop in Nineveh. The production function was estimated as Cobb-Douglas type and in the long term as optimal amounts of maximized profit resources were achieved and compared to actual reality. Among them was derived output display function and resource demand function, Through data collected by a questionnaire form prepared for this purpose for a random sample of 31 farms produced, It accounted for 40% of the study community, and one of the most important findings of the research was that the volume of the maximum production of profits was 15,634 kg/dunum, and profits reached (3544937) dinars/dunums compared to the actual production of 12,799 kg/dunums and profits (2269187) dinars/dunums,, in the light of these findings the study recommends tomato farmers to use optimal amounts of maximized profit resources in order to increase their profits.
Star gooseberry provides immense health benefits and is widely recognized in the Indian medicinal system. It holds significant importance in the food production, pharmaceuticals, and cosmetics industries due to the presence of therapeutic and pharmacological properties. Due to its beneficial properties, gooseberry fruit is widely used in treating various ailments. Therefore, cultivating these fruits presents an opportunity to generate revenue, benefiting both farmers and the agricultural sector. The post-harvest process of fruit typically performs the quality assessment by segregating fruits based on visual characteristics, which is tedious and prone to human error. Hence, there is a need to develop an automated computer vision model to assess the fruit quality more accurately. This study focuses on dataset collection, including image samples of both single and multiple-star gooseberry fruits to automate fruit grading. This dataset has been specifically developed for research purposes, contributing to fruit detection, quality assessment, weight estimation, and classification of fruits at various ripeness stages. Further, it provides researchers with an opportunity to develop an automated system for detecting overlapping fruits and touching contours using machine learning, deep learning, and computer vision systems. Image samples of star gooseberry at different growth stages were collected from orchids in Mysuru, India. The dataset, named “AmlaNet” comprises 792 image samples of star gooseberry, captured against a plain background from varying angles, sizes, brightness levels, and distances. The dataset is organized into four folders such as single star gooseberry fruit, multiple fruits, overlapped, and annotated samples of overlapped star gooseberry fruits including fruit samples with different ripeness stages. This publicly accessible dataset is expected to benefit the research community, enabling advancement in computer vision and AI Applications. It can be accessed at DOI: 10.17632/2255bdy9mm.1
Computer applications to medicine. Medical informatics, Science (General)
Economic, environmental and social sustainability is increasingly gaining the attention of academia and commitment in the policies of national economies. Global warming and climate change, weather disasters and disruptions are just some of the challenges that can drastically disrupt stable business models, and thus jeopardize sustainability. Agriculture, as an activity at the center of the value chain, is facing these problems, and, at the same time, contributes to feeding the growing world population and is responsible for conserving resources and ensuring sustainability. Process automation, data analysis and processing, control and management of agricultural operations have transformed the agricultural business and contributed to sustainability, survival and development within supply chains. This article analyzes the literature in the field of sustainability and agriculture led by digital technologies of Industry 4.0. The analysis of the research field showed the high importance of the topic in academic and national circles, given the number of published articles. Secondly, the concepts of sustainable agriculture and sustainability driven agriculture in the context of digital transformation were analyzed and it was shown that transformed agriculture can successfully cope with today’s challenges. Finally, guidelines for sustainable development driven by agriculture through the determinants of digital transformation are defined.
The use of plasma-activated water (PAW) treatment is a promising technology that has many advantages, such as high efficiency, flexibility, environmental safety, no residue, etc. Thus, PAW has been applied in the agriculture industry to increase agricultural production. The application of PAW technology in agricultural production should emphasize its systematicness, controllability, and operability, making it practical. This review systematically illustrated the production of PAW and its influencing factors. Moreover, the application of PAW in agriculture and its mechanism were discussed, including affecting seed germination, promoting plant growth, controlling disease and pest of plant. The conclusions of PAW to agriculture production and some of the related challenges and prospects are proposed. This review provides a deeper understanding of the viability of PAW technology in agriculture production. This article is protected by copyright. All rights reserved.
Abstract Temperatures below or above optimal growth conditions are among the major stressors affecting productivity, end‐use quality, and distribution of key staple crops including rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays L.). Among temperature stresses, cold stress induces cellular changes that cause oxidative stress and slowdown metabolism, limit growth, and ultimately reduce crop productivity. Perception of cold stress by plant cells leads to the activation of cold‐responsive transcription factors and downstream genes, which ultimately impart cold tolerance. The response triggered in crops to cold stress includes gene expression/suppression, the accumulation of sugars upon chilling, and signaling molecules, among others. Much of the information on the effects of cold stress on perception, signal transduction, gene expression, and plant metabolism are available in the model plant Arabidopsis but somewhat lacking in major crops. Hence, a complete understanding of the molecular mechanisms by which staple crops respond to cold stress remain largely unknown. Here, we make an effort to elaborate on the molecular mechanisms employed in response to low‐temperature stress. We summarize the effects of cold stress on the growth and development of these crops, the mechanism of cold perception, and the role of various sensors and transducers in cold signaling. We discuss the progress in cold tolerance research at the genome, transcriptome, proteome, and metabolome levels and highlight how these findings provide opportunities for designing cold‐tolerant crops for the future.
Different irrigation methods can impact the micro-ecological environment of paddy soil, affecting crop growth. This study focuses on County A, a key grain production area on China’s East Coast, where 20 plots each of intermittent and flood irrigation were selected and labeled as Group I and Group F, respectively. Soil's basic physicochemical properties and heavy metal content were measured in the laboratory, and high-throughput sequencing was used to analyze soil bacterial communities. The study evaluated the impact mechanisms of different irrigation methods on these bacterial communities. Results indicate: (1) Intermittent irrigation significantly increased bacterial richness, evenness, and diversity, enhancing microbial community diversity and stability. (2) Intermittent irrigation notably raised the abundance of 9 bacterial genera (e.g., Nitrospira, H16) while reducing the abundance of 7 genera (e.g., Geobacter, Sphingomonas). (3) The soil environment in Group I was stable, with bacterial communities influenced by SMC, pH, and Cu; Group F was affected by multiple environmental factors. Intermittent irrigation provides a stable environment that supports bacterial diversity and abundance, contributing to a richer community structure. (4) Intermittent irrigation optimizes moisture, significantly enhancing bacterial functions such as energy production, cell wall biosynthesis, repair, and signal transduction. These findings suggest that intermittent irrigation improves bacterial community stability and diversity, which is crucial for enhancing the overall quality of paddy fields.
Total acid is an important indicator of the internal quality of red globe grapes. This paper proposed a non-destructive method for the determination of total acid of red globe grapes based on hyperspectral fusion technique. A non-linear LSSVM prediction model based on spectral information, image information and the fusion of the two was built respectively for total acid. The results showed that the MSC-CARS-SPA-LSSVM model of samples about total acid built by fusing the spectra after feature wavelength extraction using the MSC-CARS-SPA algorithm and the image information after dimensionality reduction by the PCA algorithm using the graph fusion technique worked best. The correlation coefficients of the prediction sets of the optimal LSSVM model was 0.9907, which improved the accuracy over the unilateral models based on spectral or image information. A new method for nondestructive detection of total acid of red globe grapes by map fusion technique was discovered.
The integration of rural industries will inevitably lead to new business forms and new models, which put forward new requirements for traditional agricultural finance. The development of digital inclusive finance will provide new momentum for the integration of rural industries. Based on the provincial panel data from 2011 to 2020, the evaluation index system is constructed from three dimensions: industrial integration method, integration subject and integration format, and the development index of rural industrial integration is calculated. This paper establishes double fixed effect model and intermediary effect model to test the effect and path of digital inclusive finance on the integration of rural industries, and further explores the regulatory role and spatial difference of financial support. The results show that: (1) The integration of rural industries shows a growing trend, the eastern region develops more rapidly, while the central and western regions develop more slowly; (2) The digital inclusive finance can promote the integration of rural industries, digitization degree is remarkable, but coverage breadth and using depth are not significant, increasing the rate of per capita electricity consumption and urbanization can promote the integration of rural industries, consumption has limited pulling effect on the integration of rural industries, the per capita investment in fixed assets has no significant effects on the integration of rural industries; (3) The financial availability and the agricultural digitization play a complete intermediary effect; (4) Financial support has a negative moderating effect on the relationship between the two; (5) The eastern and central regions have a significant promoting effect, while the western region has a negative effect.