Hasil untuk "Agricultural industries"

P. K. Sadh, S. Duhan, J. S. Duhan

Agricultural residues are rich in bioactive compounds. These residues can be used as an alternate source for the production of different products like biogas, biofuel, mushroom, and tempeh as the raw material in various researches and industries. The use of agro-industrial wastes as raw materials can help to reduce the production cost and also reduce the pollution load from the environment. Agro-industrial wastes are used for manufacturing of biofuels, enzymes, vitamins, antioxidants, animal feed, antibiotics, and other chemicals through solid state fermentation (SSF). A variety of microorganisms are used for the production of these valuable products through SSF processes. Therefore, SSF and their effect on the formation of value-added products are reviewed and discussed.

A. Kamilaris, Andreas Kartakoullis, F. Prenafeta-Boldú

P. C. Nagajyoti, K. Lee, T. Sreekanth

M. Rafatullah, O. Sulaiman, R. Hashim et al.

B. J. Alloway

F. Bakkali, S. Averbeck, D. Averbeck et al.

A. Moure, J. M. Cruz, D. Franco et al.

N. Balasundram, K. Sundram, S. Samman

I. Kapdan, F. Kargı

B. Soares-Filho, D. Nepstad, L. Curran et al.

B. Smit, M. Skinner

K. Kadirvelu, K. Thamaraiselvi, C. Namasivayam

Xianpeng Zhou, Zakir Hayat, Dong-Dong Zhang et al.

Zinc oxide nanoparticles (ZnO-NPs) have gained significant interest in the agricultural and food industry as a means of killing or reducing the activity of microorganisms. The antibacterial properties of ZnO-NPs may improve food quality, which has a direct impact on human health. ZnO-NPs are one of the most investigated inorganic nanoparticles and have been used in various related sectors, with the potential to rapidly gain attention and increase interest in the agriculture and food industries. In this review, we describe various methods for preparing ZnO-NPs, their characterizations, modifications, applications, antimicrobial activity, testing procedures, and effects, including bactericidal and bacteriostatic mechanisms. It is hoped that this review could provide a better understanding of the preparation and application of ZnO nanoparticles in the field of food and agriculture, and promote their development to advance the field of food and agriculture.

Anam Saleem, Sobia Anwar, Taufiq Nawaz et al.

Climate alteration poses a consistent threat to food security and agriculture production system. Agriculture sector encounters severe challenges in achieving the sustainable development goals due to direct and indirect effects inflicted by ongoing climate change. Although many industries are confronting the challenge of climate change, the impact on agricultural industry is huge. Irrational weather changes have raised imminent public concerns, as adequate output and food supplies are under a continuous threat. Food production system is negatively threatened by changing climatic patterns thereby increasing the risk of food poverty. It has led to a concerning state of affairs regarding global eating patterns, particularly in countries where agriculture plays a significant role in their economies and productivity levels. The focus of this review is on deteriorating consequences of climate alteration with the prime emphasis on agriculture sector and how the altering climatic patterns affect food security either directly or indirectly. Climate shifts and the resultant alteration in the temperature ranges have put the survival and validity of many species at risk, which has exaggerated biodiversity loss by progressively fluctuating the ecological structures. The indirect influence of climate variation results in poor quality and higher food costs as well as insufficient systems of food distribution. The concluding segment of the review underscores the emphasis on policy implementation aimed at mitigating the effects of climate change, both on a regional and global scale. The data of this study has been gathered from various research organizations, newspapers, policy papers, and other sources to aid readers in understanding the issue. The policy execution has also been analyzed which depicted that government engrossment is indispensable for the long-term progress of nation, because it will guarantee stringent accountability for the tools and regulations previously implemented to create state-of-the-art climate policy. Therefore, it is crucial to reduce or adapt to the effects of climate change because, in order to ensure global survival, addressing this worldwide peril necessitates a collective global commitment to mitigate its dire consequences.

L. Sarubbo, M. C. D. Silva, I. Durval et al.

, and oil dispersion activities, which are desirable traits in different industries. Several types of biosurfactants are commercially produced for applications in the pharmaceutical and cosmetic industries while others have promising roles in the food, petroleum, and agricultural industries. In this paper, we offer an extensive review of knowledge on microbial biosurfactants accumulated over the years. We also discuss current and promising industrial applications of biosurfactants as well as the advantages and challenges for their development and applications.

P. Nath, A. K. Mishra, Ramesh Sharma et al.

Artificial intelligence has the potential to alter the agricultural and food processing industries, with significant ramifications for sustainability and global food security. The integration of artificial intelligence in agriculture has witnessed a significant uptick in recent years. Therefore, comprehensive understanding of these techniques is needed to broaden its application in agri-food supply chain. In this review, we explored cutting-edge artificial intelligence methodologies with a focus on machine learning, neural networks, and deep learning. The application of artificial intelligence in agri-food industry and their quality assurance throughout the production process is thoroughly discussed with an emphasis on the current scientific knowledge and future perspective. Artificial intelligence has played a significant role in transforming agri-food systems by enhancing efficiency, sustainability, and productivity. Many food industries are implementing the artificial intelligence in modelling, prediction, control tool, sensory evaluation, quality control, and tackling complicated challenges in food processing. Similarly, artificial intelligence applied in agriculture to improve the entire farming process, such as crop yield optimization, use of herbicides, weeds identification, and harvesting of fruits. In summary, the integration of artificial intelligence in agri-food systems offers the potential to address key challenges in agriculture, enhance sustainability, and contribute to global food security.

Kazuya Asao, Seika Matsumoto, Haruka Mori et al.

Phytic acid is a phosphorus-rich molecule, which is produced by plants using water-soluble phosphates absorbed from soil. It can potentially serve as a phosphorus source in the syntheses of organic phosphates; however, this approach has not been utilized for the preparation of phosphate esters. In this study, we report the first successful synthesis of phosphate esters using phytic acid as a phosphorus source. Crude products of phosphate diesters were obtained through the reactions of commercially available phytic acid and aromatic alcohols with 31P nuclear magnetic resonance yields up to 83%. We also isolated a portion of the reaction substrates with yields up to 60%. Next, we extracted phytic acid from rice bran with a recovery of 4.2% and then conducted an esterification reaction using the extracted phytic acid and phenol. As a result, diphenyl phosphate with a yield of 44% was obtained. This work can facilitate the development of an environmentally friendly method for producing phosphate esters that does not rely on phosphate rock but instead uses biomass as a phosphorus source.

Yi Liu, Wenzhi Zeng, Chang Ao et al.

Soil salinization impairs fertility and reduces crop productivity across more than 6 % of the world’s arable land. Traditional remediation approaches, like chemical amendments, are often costly and involve ecological compromises. This study investigates an innovative nano-bio strategy that integrates multi-walled carbon nanotubes (MWCNTs) with Bacillus subtilis (B. subtilis) under drip irrigation to boost maize tolerance in saline environments. Germination tests and field studies were conducted in soils treated with 50 mM NaCl. The results from four comparative treatments revealed that MWCNTs markedly improved seed germination (achieving 52 % by day two versus 24 % in controls) and enhanced root elongation by 52.36 %. These effects were linked to the upregulation of key ion transporters (ZmSKOR). Furthermore, MWCNTs application enhanced the expression of aquaporin genes ZmPIP1;1 and ZmPIP2;1. Although B. subtilis alone had a minimal impact on germination, its combination with MWCNTs fostered stronger soil-microbe-nanomaterial interactions under drip irrigation. This synergy increased maize yield by 20.6 %, raised the 1000-grain weight by 3.08 %, lowered the leaf Na⁺/K⁺ ratio by 19.93 %, and improved antioxidant defense mechanisms, such as a 10.44 % rise in SOD activity. Importantly, while MWCNTs alone decreased soil nitrogen in non-saline conditions, adding B. subtilis helped rebalance nutrients, an effect that was reinforced by the uniform distribution provided by drip irrigation. The mechanism involves improved nutrient assimilation, better stomatal control, and reduced reactive oxygen species under salt stress. These findings indicate that the MWCNTs and B. subtilis act synergistically with drip irrigation via molecular soil-root interactions to mitigate salt toxicity. This integrated approach, which combines nanotechnology, microbiome engineering, and water-efficient irrigation, offers a sustainable and effective solution for reclaiming saline soils and advancing stress-resistant agriculture.

Syed Ijaz Ul Haq, Guobin Wang, Shahid Nawaz Khan et al.

Early and accurate detection of crop stress is essential for sustainable agriculture and food security, particularly as climate change and environmental degradation intensify agricultural challenges. This comprehensive review examines advanced crop stress monitoring strategies that leverage multi-dimensional optical remote sensing approaches, specifically integrating spectral, angular, and spatial perspectives across diverse observation scales. We systematically analyze how biotic stresses (diseases, pests) and abiotic stresses (drought, nutrient deficiency, temperature extremes) manifest through detectable changes in plant spectral signatures, from chlorophyll degradation in the visible spectrum to water content variations in shortwave infrared regions. Our review encompasses sensing technologies spanning RGB, multispectral, hyperspectral, thermal infrared, and chlorophyll fluorescence sensors deployed across three complementary scales: proximal ground-based systems for detailed physiological assessment, unmanned aerial vehicles (UAVs) for field-scale monitoring, and satellites for regional surveillance. A key innovation of this work is the emphasis on multi-angle remote sensing, which captures bidirectional reflectance distribution function (BRDF) effects that reveal stress-induced changes in canopy structure and leaf orientation invisible to conventional nadir-only observations. We demonstrate how viewing geometry significantly affects vegetation indices (NDVI, PRI) and sun-induced fluorescence (SIF) measurements, requiring sophisticated angular correction methods for accurate stress assessment. Through synthesis of 138 recent studies spanning 12 major crop types, we identify critical research gaps including: (1) inconsistent angular reflectance modeling across stress types, (2) inadequate sensor calibration protocols for variable field conditions, and (3) lack of standardized frameworks for integrating multi-source, multi-scale data streams. Our analysis reveals that advanced machine learning approaches particularly deep learning and transformer networks show exceptional promise for extracting meaningful stress signatures from complex, high-dimensional datasets while maintaining interpretability for agricultural decision-making. We propose a hierarchical monitoring architecture supported by physics-aware artificial intelligence models that address three fundamental challenges: temporal optimization for capturing stress progression dynamics, spatial integration across observation scales, and angular standardization for consistent stress quantification. This framework aims to transform crop stress monitoring from reactive management to predictive intervention, enabling real-time diagnostics suitable for diverse agricultural systems ranging from high-value specialty crops to extensive grain production. The review concludes with a strategic roadmap for operational implementation, addressing economic constraints, technological limitations, and knowledge transfer requirements necessary for widespread adoption. Our findings indicate that successful deployment requires service-based delivery models, simplified decision support interfaces, and staged implementation approaches that demonstrate incremental value while building organizational capacity. The literature selection was conducted using Scopus, Web of Science, and IEEE Xplore databases, covering publications from 2018 to 2024. Search terms included “crop stress monitoring,” “spectral remote sensing,” “multi-angle sensing,” and “UAV agriculture.” A total of 138 peer-reviewed studies meeting relevance and methodological rigor criteria were included. These studies span 12 major crop types: wheat, maize, rice, soybean, cotton, sugarcane, potato, grapevine, tomato, barley, sorghum, and rapeseed, ensuring broad coverage across cereal, legume, fiber, tuber, and horticultural crops.

Sabrina Sharmin, Md. Tazel Hossan, Mohammad Shorif Uddin

Accurate and timely yield prediction of hydroponically grown lettuce is essential for financial planning, strategic decision-making, and enhancing farmers' profitability. In controlled hydroponic environments, this prediction remains challenging, mainly due to complex factors influencing growth. Machine Learning (ML) offers advanced methods to address these challenges. This review analyzes ML techniques for forecasting lettuce yield in hydroponic systems, starting with an overview of global trends in lettuce production. It then explores core ML methodologies, key model characteristics, and application-specific features that contribute to yield prediction. A comparative analysis of existing ML models also highlights their strengths and limitations. Current challenges, such as data integration and prediction accuracy, are discussed alongside potential improvements through remote sensing, monitoring, and feature optimization. This paper concludes by proposing a framework aimed at efficient yield prediction in hydroponics, offering insights for future research and applications in agricultural technology.