Deep Learning Techniques for Microscopic Identification and Detection of Plant-Parasitic Nematodes - Furkan Ulaş; Muhammad Aasim; Refik Bozbuğa; Mustafa İmren (Journal of Crop Health, 2026)

Integrated optimization of Lantana camara-based biopesticide for Sitophilus granarius control: Chemical profiling, residual efficacy, and machine-learning-driven decision support - Pervin Erdogan; Muhammad Aasim; Zemran Mustafa; Amjad Ali; Seyid Amjad Ali; Rashid Abbas Khan; Abdul Waheed (Journal of Stored Products Research, 2026)

Global research trends in bioherbicides for sustainable weed management: A bibliometric analysis (1981–2025) - Furkan Ulaş; Hilmi Torun; Muhammad Aasim; Khawar Jabran; Hüsrev Mennan (Phytoparasitica, 2026)
Bioherbicides have gained increasing attention as sustainable alternatives to synthetic herbicides in modern weed management. This study presents a comprehensive bibliometric analysis of global bioherbicide research published between 1981 and 2025 using data retrieved from the Web of Science Core Collection. A total of 992 publications were analysed using VOSviewer (version 1.6.20) and the Biblioshiny interface of the Bibliometrix package in RStudio to evaluate publication trends, citation patterns, collaboration networks, thematic evolution, and conceptual structures within the field. The retrieved publications accumulated 18,123 citations and achieved an H-index of 56, indicating considerable scientific impact and sustained scholarly influence. Publication output remained limited before 2000 but increased markedly after 2010, reaching peak productivity after 2020. Keyword co-occurrence analysis identified 117 keywords grouped into 10 clusters, with a total of 974 links and a total link strength (TLS) of 2273, revealing a highly interconnected conceptual structure centred around microbial biological control, allelopathy, natural products, sustainability, and integrated weed management. Thematic evolution and Multiple Correspondence Analysis (MCA) demonstrated a progressive transition from early pathogen-based and allelopathic studies toward interdisciplinary approaches integrating sustainable agriculture, molecular research, and bioactive natural compounds. The United States, Canada, and Italy emerged as the leading contributors, while institutions such as the USDA played central roles within international collaboration networks. Despite substantial scientific progress, major challenges related to formulation technologies, environmental stability, field-level consistency, regulatory constraints, and commercialization continue to limit large-scale adoption of bioherbicides. Emerging interdisciplinary approaches, including artificial intelligence-assisted discovery approaches, metabolomics integration, and advanced formulation technologies, may further support the future development of sustainable bioherbicide research and application.

Nano-enabled spray-induced RNA interference against fungal pathogens of oilseed crops: carrier performance, delivery evidence and field translation - Amjad Ali; Sana Hanif; Afifa Azad; Kanza Azad; Tehreem Eraf; Muhammad Aasim; Ashrit Gulfraz; Faheem Shehzad Baloch; Muhammad Shahbaz (World Journal of Microbiology and Biotechnology, 2026)

Nano-enabled RNAi strategies for sustainable citrus protection against viral pathogens - Amjad Ali; Ameel Riaz; Muhammad Faiq; Jaya Seelan Sathiya Seelan; Faheem Shehzad Baloch; Muhammad Aasim; Muhammad Shahbaz (Planta, 2026)

Grad-CAM++ Guided Deep Learning-Based Genus-Level Identification of Plant-Parasitic Nematodes in Microscopic Images - Furkan Ulaş; Kemal Adem; Dilek Dinçer; Muhammad Aasim (Journal of Crop Health, 2026)

Elicitors in Plant Defense: Exploring Their Role in Biotic and Abiotic Stress Resistance - Muhammad Tanveer Altaf; Waqas Liaqat; Muhammad Aneeq Ur Rahman; Chidera Anuforo; Asia Maqbool; Amjad Ali; Muhammad Faheem Jan; Muhammad aasim; Heba I. Mohamed; Faheem Shehzad Baloch (Journal of Plant Growth Regulation, 2026)

Machine learning and design of experiment for enhanced biomass using N2 and CO2-enriched biochar at different pyrolysis temperatures - Muhammad Aasim; Ebubekir Siddik Aydin; Ibrahim Korkut; Başak Temur Ergan; Ahmet Say; Salma Naimatullah Soomro; Sabeen Rehman Soomro; Seyid Amjad Ali; Sezai Ercişli; Ramazan Katırcı (Biomass and Bioenergy, 2026)

Artificial intelligence applications in abiotic and biotic plant stress management: A comprehensive bibliometric and literature review - Furkan Ulaş; Rachid Lahlali; Salah-Eddine Laasli; Muhammad Aasim; Abdelfattah Dababat; Muhammad Sameeullah; Mustafa İmren (Physiological and Molecular Plant Pathology, 2026)

Artificial intelligence-driven validation of silver and titanium nanomaterials impact on morpho-chemical potential of industrial hemp (Cannabis sativa L.) - Ozlem Akgur; Muhammad Aasim; Seyid Amjad Ali (Rendiconti Lincei. Scienze Fisiche e Naturali, 2025)

Utilization of physicochemical approach to propose a strategy for biofilm inhibition - Ajab Khan; Muhammad Aasim; Noor Shad Bibi; Haris Saddique; Hizb Ullah; Zohaib Khan; Umar Aziz; Nadir Zaman Khan; Waqar Ali; Noor Muhammad (Biofouling, 2025)

Precision in vitro propagation by integrating response surface methodology and machine learning for Glossostigma elatinoides (Benth) Hook. F - Esra Özcan; Seyid Amjad Ali; Muhammad Aasim; Hasan Hüseyin Atar (In Vitro Cellular & Developmental Biology - Plant, 2025)
Optimization of in vitro organogenesis of Glossostigma elatinoide s (Benth) Hook. f. was targeted in this study. The experiment was designed with the help of design of experiment (DOE) of response surface methodology (RSM) model. Murashige and Skoog (MS) basal salt, sucrose, and agar were used as input factors and a total of 15 runs were used for optimization. Results were analyzed by ANOVA and response surface regression analysis (RSRA) followed by prediction and validation via different machine learning (ML) models. Results of ANOVA revealed the impact of different combinations on output parameters. Results of RSRA illustrated the relationship between input and output parameters. Pareto chart analysis showed the significant impact of MS on clump diameter, fresh wt., and dry wt. Normal plot analysis illustrated the positive impact of MS on all output parameters and increased proportionally with MS concentration. Results of heatmap and network analysis also demonstrated the significance of MS on all output parameters. Comparison of ML models depicted the better performance of multilayer perceptron (MLP) model for rooting ( R 2 = 0.957), fresh wt ( R 2 = 0.806), and dry wt ( R 2 = 0.812). Conversely, the support vector regression (SVR) model demonstrated superior prediction for clump diameter ( R 2 = 0.809). Among the tested models, the SVR model showed the weakest performance, aside from clump diameter, while LightGBM achieved scores close to those of the RF and MLP models across all metrics. The findings clearly indicate that the adopted protocol is well-suited for the effective commercial propagation of the aquatic G. elatinoides plant.

Assessment of Climate Change Impact on Potato-Potato Cropping System Under Semi-arid Environment and Designing of Adaptation Strategies - Sahrish Naz; Mukhtar Ahmed; Ghulam Abbas; Zartash Fatima; Sajjad Hussain; Pakeeza Iqbal; Abdul Ghani; Muhammad Ali; Tahir Hussain Awan; Noreen Samad; Muhammad Aasim; Sezai Ercisli; et al. (Potato Research, 2025)

Development of tetraploids in tissue culture: modern techniques and biotechnological innovations - Sabeen Rehman Soomro; Salma Naimatullha Soomro; Muhammad Tanveer Altaf; Waqas Liaqat; Muhammad Azhar Nadeem; Faheem Shehzad Baloch; Muhammad Aasim; Heba I. Mohamed (Plant Cell, Tissue and Organ Culture (PCTOC), 2025)

Applicability of Start Codon Targeted (SCoT) markers for the assessment of genetic diversity in bread wheat germplasm - Muhammad Tanveer Altaf; Muhammad Azhar Nadeem; Amjad Ali; Waqas Liaqat; Mehmet Bedir; Nurettin Baran; Aleksandra Ilić; Muhammad Kashif Ilyas; Abdul Ghafoor; Hamide Dogan; Muhammad Aasim; Faheem Shehzad Baloch (Genetic Resources and Crop Evolution, 2025)
Research endeavors focused on examining genetic diversity play a pivotal role in advancing agricultural practices, as they furnish valuable insights for breeding programs and the preservation of germplasm. Wheat holds significant global importance as a cereal crop, serving as a primary source of sustenance for almost one-third of the global human population. In the current study, a total of 80 bread wheat genotypes were characterized using the start codon targeted (SCoT) marker system. A set of 14 polymorphic primers yielded a cumulative count of 399 bands with an average of 28.05 bands per primer. The range of diversity markers, such as polymorphism information content (0.26–0.59), effective numbers of alleles (1.27–1.59), Shannon’s information index (0.30–0.51), and gene diversity (0.18–0.34), provided evidence of elevated genetic differences within the examined germplasm. According to the findings of the molecular variance analysis, greater genetic variations are found within populations (92%). The model-based structural approach partitioned the 80 bread wheat genotypes into two distinct populations (Population A and B). The diversity indices based on the structures of the populations revealed that population A had greater diversity compared to population B. Both the principal coordinate analysis and the neighbor-joining analysis also differentiated the 80 different bread wheat genotypes based on their respective collection points. The present crop diversity offers great opportunities for enhancing crop quality and increasing tolerance to environmental challenges, both of which will lead to a reduction in the problem of food insecurity.

Phosphate-solubilizing fungus (PSF) - mediated phosphorous solubilization and validation through Artificial intelligence computation - Fatih Ölmez; Zemran Mustafa; Şahimerdan Türkölmez; Aslıhan Esra Bildirici; Seyid Amjad Ali; Muhammad Aasim (World Journal of Microbiology and Biotechnology, 2024)

Comparing quantum machine learning and classical machine learning for in vitro regeneration of cowpea (Vigna unguiculata) - Ramazan Katırcı; Muhammad Aasim; Gizem Deveci; Zemran Mustafa (Plant Cell, Tissue and Organ Culture (PCTOC), 2024)

Spatial distribution of potentially toxic elements pollution and ecotoxicological risk of sediments in the high-altitude lakes ecosystem - Said Muhammad; Aasim Zeb; Mohammed Rafi Shaik; Mohamed E. Assal (Physics and Chemistry of the Earth, Parts A/B/C, 2024)

Frontiers in bacterial-based green synthesized nanoparticles (NPs): A sustainable strategy for combating infectious plant pathogens - Amjad Ali; Muhammad Aasim; Kübra Çelik; Muhammad Azhar Nadeem; Faheem Shehzad Baloch (Biocatalysis and Agricultural Biotechnology, 2024)

Zeugodacus fruit flies (Diptera: Tephritidae) host preference analysis by machine learning-based approaches - Naila Nazir; Sehar Fatima; Muhammad Aasim; Faheem Yaqoob; Khalid Mahmood; Seyid Amjad Ali; Shahid Iqbal Awan; Ihsan ul Haq (Computers and Electronics in Agriculture, 2024)

Synergizing LED Technology and Hydropriming for Intelligent Modeling and Mathematical Expressions to Optimize Chickpea Germination and Growth Indices - Muhammad Aasim; Fatma Akin; Seyid Amjad Ali (Journal of Plant Growth Regulation, 2024)
The influence of hydropriming and Light Emitting Diodes (LED) on germination and growth indices, followed by optimizing and validation via artificial intelligence-based models was carried out in this research. White LEDs (W-LEDs) were more effective by yielding the most effective growth indices, such as mean germination time (MGT) (1.11 day), coefficient of variation of germination time (CV t ) (20.72%), mean germination rate (MR) (0.81 day −1 ), uncertainty ( U ) (0.40 bit), and synchronization ( Z values) (0.79); the optimum MGT (1.09 day), CV t (15.97%), MR (0.77 day −1 ), U (0.32 bit), and Z (0.55) values were found after 2 h of hydropriming, which was responsible for all efficient growth indicators. W-LEDs with 1 h hydropriming proved to be the ideal LED and hydropriming combination. Results on growth indices for in vitro seedlings were completely different from those on germination indices, and the most desirable germination indices were linked to red LEDs (R-LEDs). Whereas 4 h hydropriming was most effective for the post-germination process. Pareto charts, normal plots, contour plots, and surface plots were created to optimize the input variables. Finally, the data were predicted using Arificial Neural Network (ANN) inspired multilayer perceptron (MLP) and machine learning-based random forest (RF) algorithms. For both models, plant height was correlated with maximum R 2 values. Whereas, all output variables had relatively low mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) scores, indicating that both models performed well. The results of this investigation disclosed a link between certain LEDs and hydropriming treatment for in vitro germination indices and plant growth. Graphical Abstract Graphical presentation of actual and predicted values for germination indices in chickpea

A unified framework of response surface methodology and coalescing of Firefly with random forest algorithm for enhancing nano-phytoremediation efficiency of chromium via in vitro regenerated aquatic macrophyte coontail (Ceratophyllum demersum L.) - Seyid Amjad Ali; Numan Emre Gümüş; Muhammad Aasim (Environmental Science and Pollution Research, 2024)
Nano-phytoremediation is a novel green technique to remove toxic pollutants from the environment. In vitro regenerated Ceratophyllum demersum (L.) plants were exposed to different concentrations of chromium (Cr) and exposure times in the presence of titania nanoparticles (TiO 2 NPs). Response surface methodology was used for multiple statistical analyses like regression analysis and optimizing plots. The supplementation of NPs significantly impacted Cr in water and Cr removal (%), whereas NP × exposure time (T) statistically regulated all output parameters. The Firefly metaheuristic algorithm and the random forest (Firefly-RF) machine learning algorithms were coalesced to optimize hyperparameters, aiming to achieve the highest level of accuracy in predicted models. The R 2 scores were recorded as 0.956 for Cr in water, 0.987 for Cr in the plant, 0.992 for bioconcentration factor (BCF), and 0.957 for Cr removal through the Firefly-RF model. The findings illustrated superior prediction performance from the random forest models when compared to the response surface methodology. The conclusion is drawn that metal-based nanoparticles (NPs) can effectively be utilized for nano-phytoremediation of heavy metals. This study has uncovered a promising outlook for the utilization of nanoparticles in nano-phytoremediation. This study is expected to pave the way for future research on the topic, facilitating further exploration of various nanoparticles and a thorough evaluation of their potential in aquatic ecosystems. Graphical Abstract

Spatial distribution of drinking, irrigation water quality, and health risk indices of high-altitude lakes - Said Muhammad; Aasim Zeb; Rizwan Ullah; Sehrish Amin; Ashfaq Ahmad; Cem Tokatli (Physics and Chemistry of the Earth, Parts A/B/C, 2024)

Response surface methodology and artificial intelligence modeling for in vitro regeneration of Brazilian micro sword (Lilaeopsis brasiliensis) - Seyid Amjad Ali; Muhammad Aasim (Plant Cell, Tissue and Organ Culture (PCTOC), 2024)
In this study, response surface methodology (RSM) was used to optimize in vitro regeneration of the Brazilian micro sword ( Lilaeopsis brasiliensis) aquatic plant, followed by data prediction and validation using machine learning algorithms. The basal salt, sucrose and Benzyaminopurine (BAP) concentrations were derived from Box-Behnken design of RSM. The response surface regression analysis revealed that 1.0 g/L MS + 0.1 mg/L BAP + 25 g/L sucrose was optimized for maximum regeneration (100%), shoot counts (63.2), and fresh weight (1.382 g). The RSM-based predicted scores were fairly similar to the actual scores, which were 100% regeneration, 63.39 shoot counts, and 1.44 g fresh weight. Pareto charts analysis illustrated the significance of MS for regeneration and fresh weight but remained insignificant. Conversely, MS × BAP was found to be the most crucial factor for the shoot counts, with MS coming in second and having a major influence. The analysis of the normal plot ascertained the negative impact of elevated MS concentration on shoot counts and enhanced shoot counts from the combination of MS × BAP. Results were further optimized by constructing contour and surface plots. The response optimizer tool demonstrated that maximum shoot counts of 63.26 and 1.454 g fresh weight can be taken from the combination of 1.0 g/L MS + 0.114 mg/L BAP + 23.94 g/L. Using three distinct performance criterias, the results of machine learning models showed that the multilayer perceptron (MLP) model performed better than the random forest (RF) model. Our findings suggest that the results may be utilized to optimize various input variables using RSM and verified via ML models. Key message Optimization of in vitro whole plant regeneration of Brazilian sword wood using response surface methodology Data analysis through ANOVA, response surface regression anlaysis and machine learning Graphical presentation of data via Pareto charts, normal plots, contour plots and surface plots for optimization Better performance of ANN-based MLP model compared to decision tree based RF model Graphical abstract

Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.) - Muhammad Aasim; Buşra Yıldırım; Ahmet Say; Seyid Amjad Ali; Selim Aytaç; Muhammad Azhar Nadeem (Plant Molecular Biology, 2024)

Mechanism and synergistic effect of sulfadiazine (SDZ) and cadmium toxicity in spinach (Spinacia oleracea L.) and its alleviation through zinc fortification - Muhammad Nafees; Adiba Khan Sehrish; Sarah Owdah Alomrani; Linlin Qiu; Aasim Saeed; Shoaib Ahmad; Shafaqat Ali; Hongyan Guo (Journal of Hazardous Materials, 2024)

Artificial neural network and decision tree–based models for prediction and validation of in vitro organogenesis of two hydrophytes—Hemianthus callitrichoides and Riccia fluitans - Esra Özcan; Hasan Hüseyin Atar; Seyid Amjad Ali; Muhammad Aasim (In Vitro Cellular & Developmental Biology - Plant, 2023)

Heterologous expression of bacterial dehydrin gene in Arabidopsis thaliana promotes abiotic stress tolerance - Nadir Zaman Khan; Akhtar Ali; Waqar Ali; Muhammad Aasim; Tariq Khan; Zaryab Khan; Iqbal Munir (Physiology and Molecular Biology of Plants, 2023)

iPBS-retrotransposons variations: DNA fingerprinting and the evaluation of genetic diversity and population structure in international cowpea germplasm - Faheem Shehzad Baloch; Muhammad Tanveer Altaf; Mehmet Bedir; Muhammad Azhar Nadeem; Muhammed Tatar; Tolga Karaköy; Muhammad Aasim (Genetic Resources and Crop Evolution, 2023)

Artificial neural network and decision tree facilitated prediction and validation of cytokinin-auxin induced in vitro organogenesis of sorghum (Sorghum bicolor L.) - Muhammad Aasim; Seyid Amjad Ali; Muhammad Tanveer Altaf; Amjad Ali; Muhammad Azhar Nadeem; Faheem Shahzad Baloch (Plant Cell, Tissue and Organ Culture (PCTOC), 2023)

Machine Learning and Artificial Neural Networks-Based Approach to Model and Optimize Ethyl Methanesulfonate and Sodium Azide Induced In Vitro Regeneration and Morphogenic Traits of Water Hyssops (Bacopa monnieri L.) - Kubra Mirza; Muhammad Aasim; Ramzan Katırcı; Mehmet Karataş; Seyid Amjad Ali (Journal of Plant Growth Regulation, 2023)

Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L) - Muhammad Aasim; Fatma Akin; Seyid Amjad Ali; Mehmet Burak Taskin; Muslume Sevba Colak; Khalid Mahmood Khawar (Physiology and Molecular Biology of Plants, 2023)

Artificial intelligence–based approaches to evaluate and optimize phytoremediation potential of in vitro regenerated aquatic macrophyte Ceratophyllum demersum L. - Muhammad Aasim; Seyid Amjad Ali; Senar Aydin; Allah Bakhsh; Canan Sogukpinar; Mehmet Karatas; Khalid Mahmood Khawar; Mehmet Emin Aydin (Environmental Science and Pollution Research, 2023)

Light-emitting diodes induced in vitro regeneration of Alternanthera reineckii mini and validation via machine learning algorithms - Muhammad Aasim; Seyid Amjad Ali; Pınar Bekiş; Muhammad Azhar Nadeem (In Vitro Cellular & Developmental Biology - Plant, 2022)

Application of artificial neural network and machine learning algorithms for modeling the in vitro regeneration of chickpea (Cicer arietinum L.) - Arife Kirtis; Muhammad Aasim; Ramazan Katırcı (Plant Cell, Tissue and Organ Culture (PCTOC), 2022)

Deciphering the iPBS retrotransposons based genetic diversity of nanoparticles induced in Vitro seedlings of industrial hemp (Cannabis sativa L.) - Ozlem Akgur; Muhammad Aasim (Molecular Biology Reports, 2022)

Iron-doped zinc oxide nanoparticles-triggered elicitation of important phenolic compounds in cell cultures of Fagonia indica - Atta Ullah Khan; Tariq Khan; Mubarak Ali Khan; Akhtar Nadhman; Muhammad Aasim; Nadir Zaman Khan; Waqar Ali; Nausheen Nazir; Muhammad Zahoor (Plant Cell, Tissue and Organ Culture (PCTOC), 2021)

Towards better insect management strategy: restriction of insecticidal gene expression to biting sites in transgenic cotton - Emine Anayol; Allah Bakhsh; Ömer Cem Karakoç; Selma Onarıcı; Deniz Köm; Muhammad Aasim; Sancar Fatih Özcan; Surendra Barpete; Saber D. Khabbazi; Burak Önol; Cengiz Sancak; Khalid M. Khawar; et al. (Plant Biotechnology Reports, 2016)

An insight into cotton genetic engineering (Gossypium hirsutum L.): current endeavors and prospects - Allah Bakhsh; Emine Anayol; Sancar Fatih Özcan; Tahira Hussain; Muhammad Aasim; Khalid Mahmood Khawar; Sebahattin Özcan (Acta Physiologiae Plantarum, 2015)