THE IMPACT OF ARBUSCULAR MYCORRHIZAL FUNGAL INOCULANTS ON GROWTH, NUTRIENTS, AND YIELD OF VEGETABLE PLANTS: A REVIEW
DOI:
https://doi.org/10.33003/fjs-2025-0903-3353Keywords:
Arbuscular mycorrhizal fungi (AMF), Photosynthates, Inoculants, Mycelium, SymbiosisAbstract
Arbuscular mycorrhizal fungi (AMF), belonging to the phylum Glomeromycota, establish symbiotic associations with plant roots, enhancing nutrient uptake through extensive hyphal networks. These networks facilitate the acquisition of essential nutrients, particularly phosphorus, while the host plants supply the fungi with photosynthates. This review examines the impact of AMF inoculation on onion, tomato, cucumber, and pepper. The findings highlight the numerous benefits conferred by AMF symbiosis, which includes significant enhancements in plant growth and development. AMF inoculation has been shown to improve photosynthetic efficiency, increase plant height, leaf area, root length, and both fresh and dry biomass, as well as boost fruit yield in terms of number, size, and weight. Furthermore, AMF contribute to improved nutrient and water absorption by extending their hyphae into deeper soil layers, thereby enhancing resource availability for plants. Additionally, AMF inoculation plays a crucial role in mitigating biotic and abiotic stresses in vegetable crops while also improving soil stability by reducing leaching and erosion.
References
Awujoola A A Adebisi S A Lawal A S 2020 Cybersecurity challenges in cloud computing environments International Journal of Cybersecurity and Digital Forensics 82 4556
Choudhary R Kesswan A 2020 Enhancing intrusion detection in network security using the NSLKDD dataset Journal of Cybersecurity Applications 51 1224
Gad I M 2021 Evaluation metrics for machine learning models Machine Learning Quarterly 144 2335
Ghani A A Usman M T Bello I S 2023 Advancements in intrusion detection using feedforward neural networks Journal of Advanced Machine Learning 113 6780
Gron M 2019 Confusion matrix and its role in machine learning performance evaluation Journal of Data Science Techniques 72 3341
Heydarian N 2022 Improving machine learning accuracy through model evaluation techniques Data Science Journal 91 4558
Kadam P Gupta N Sharma V 2022 The role of artificial intelligence in modern cybersecurity Computational Intelligence Review 101 2337
Kim J W Park J H Lee H S 2016 Using LSTMRNN for anomaly detection in network traffic Cybersecurity Studies 34 102118
Khalid S Ahmad R 2014 Feature selection techniques for reducing computational complexity in network intrusion detection Applied Machine Learning Journal 82 3450
Lee H 2019 IoT advancements and cybersecurity challenges IoT Applications Quarterly 62 1527
Lee H 2020 Proliferation of cyber threats in IoT environments Journal of IoT Security 71 2138
Meghdouri M Sadighian A 2018 Deep neural networks for intrusion detection systems Journal of Network Security 53 7888
Platanios E Bello I K Adil T 2017 Error rates in machine learning models Machine Intelligence Journal 42 5672
Rullo M De Sanctis M Lorenzo G 2023 Tackling cybersecurity threats with AI European Journal of Cybersecurity 91 3142
Santamaria M Costa P Xavier T 2018 Understanding error rates and their implications in predictive modeling Statistics and AI Quarterly 51 1228
Shone N Ngoc T N Bandyopadhyay T 2018 Machine learning techniques for anomaly detection in network traffic Journal of Machine Learning Applications 62 1429
Vijay R Anwar S Prakash D 2020 The impact of intrusion detection systems on cybersecurity Global Journal of Network Security 84 89105
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences
