Crop Management Research Articles

Assessment of cover crop adoption and impact on weed management in Wisconsin corn‐soybean cropping systems

AbstractThe evolution and widespread occurrence of herbicide‐resistant weeds pose a major challenge for farmers and crop consultants across North America, warranting integrated management strategies. The adoption of cover crops offer weed suppression and soil health benefits but adds management complexity. A survey, targeting farmers and crop consultants, was conducted during the spring of 2023 to evaluate current cover crop management practices and perceptions in Wisconsin cropping systems. The survey included 26 questions across five sections: respondent profile, cover crop adoption and experience, cover crop management ahead of soybean [Glycine max (L.) Merr.], cover crop management ahead of corn (Zea mays L.), and general benefits and challenges of cover crop adoption. Farmers and crop consultants represented most respondents, influencing a total of 29,500 and 557,000 ha, respectively. Waterhemp (Amaranthus tuberculatus [Moq.] J.D. Sauer) and giant ragweed (Ambrosia trifida L.) were identified as the most troublesome weeds. Over 90% of respondents use herbicides to terminate their cover crops, and 68% of respondents agree that cover crops improved overall weed control in their farms/clients’ farms to some extent [cereal rye (Secale cereale L.) as main cover crop species]. Additionally, results demonstrate a strong consensus (85%) on cover crops positively influencing water retention, with 98% agreement on their efficacy in reducing soil erosion. Results from this survey can help guide farmers, agronomists, researchers, and policymakers with cover crop adoption, management, policies, incentives, and future research and education needs in Wisconsin and beyond to support the development of more sustainable and efficient weed and crop management strategies.

The Brazilian case as a beacon to increase crop production in sub-Saharan Africa

Abstract Maize is one of the major agricultural commodities in the world, and a source of food in Africa, representing more than 40 million ha currently harvested on the continent. Despite sub-Saharan Africa's dependence on grain, the maize actual yield (Ya) of the crop is low when compared to its potential yield. In Brazil, the yield-gap between Ya and water-limited yield (Yw) is approximately 50% of Yw. The objective of this study was to carry out a case study, using upland maize as a reference to identify a set of agricultural areas with similar soil and climate in Brazil and sub-Saharan Africa (SSA). The climatic similarity between Brazil and SSA countries was verified, seeking homogeneous climatic zones that occur in both regions. The Ya was determined including the data of at least the last three years of cultivation and were taken from the database of the national institutes of agricultural statistics. The climatic data showed that the SSA had well-distributed rainfall throughout the crop season, being higher than in Brazil, as well as the average air temperature. The average Yw was 11.3 and 7.4 Mg/ha for Brazil and SSA, respectively. Maize Ya in SSA was 1.4 Mg/ha, while in Brazil Ya was 5.2 Mg/ha. Ya represented approximately 9% of Yw in the SSA. The low Ya shows the large yield-gap found in SSA. With this, it is evident that the technologies used and the crop management are largely responsible for the yield differences between Brazil and SSA.

The LTAR Cropland Common Experiment at Lower Chesapeake Bay.

The Lower Chesapeake Bay (LCB) Long-Term Agroecosystem Research (LTAR) Common Experiment (CE) located in Beltsville, MD, focuses on research of concern to producers of the major regional crops, which are corn (Zea mays L.), soybean [Glycine max (L.) Merr.], wheat (Triticum aestivum L.), and various forage species. Livestock production in the region includes broiler and laying chickens (Gallus gallus domesticus L.) and dairy and beef cattle (Bos taurus L.). The LCB region is among the most heavily populated in the United States. Urban development pressure is high for both farms and natural areas. The need to restore Chesapeake Bay water quality is a major influence on regional agricultural practices. Conservation practices such as cover cropping, no-till agriculture, and nutrient management planning are more common in the region compared to nationally. However, farmers still face management challenges implementing practices that address water quality and the rise of herbicide-resistant weeds. Researchers at the LCB site recognize the need to protect the Chesapeake and Delaware Bays and maintain farmer profitability. The LCB CE compares a 3-year crop rotation system featuring alternative crop management (cover crop intensification, crop rotation diversification, and integrated weed management [IWM]) with a prevailing 2-year system (no cover crops and no IWM), both under continuous no-tillage, to identify the optimal balance to promote the sustainability of regional cropping systems. The LTAR LCB site provides data-driven tools and solutions to support farmers in the mid-Atlantic region.

Perceptual Pigeon Galvanized Optimization of Multi-objective CNN on the Identification and Classification of Mango Leaves Disease

Aims The aim of this study is to develop a strong Multi-objective Convolutional Neural Network (MOCNN) optimized using Perceptual Pigeon Galvanized Optimization (PPGO) for accurate identification and classification of mango leaf diseases. This approach aims to increase classification accuracy, computational efficiency, and generalization ability. The ultimate goal is to improve disease management in mango crops through advanced image-based diagnostic techniques. Background Demands for the consumption of mango (Mangifera indica) fruit and its leaves were growing exponentially in all parts of the world due to its large health benefits for various organs in the human body. However, these plants were largely exposed to various kinds of microbial diseases during cultivation despite the application of pesticides. Hence, it is becoming a significant threat to the farmers and to the food industry. Objective The objectives of this study are to develop reliable methods for the early identification of mango leaf diseases, enabling prompt intervention and reducing crop damage. Additionally, the study aims to provide effective disease management applications that will help farmers minimize crop losses and maintain their economic stability. Methods PPGO is an advanced optimization algorithm inspired by the natural foraging behavior of pigeons. It integrates perceptual hashing and galvanic responses to adaptively adjust the search process, allowing for efficient exploration and exploitation of the solution space. The multi-objective convolutional neural network is trained to minimize a composite loss function that considers classification accuracy, computational efficiency, and generalization error. Results The Perceptual Pigeon Galvanized Optimization (PPGO) with a Multi-objective Convolutional Neural Network (MOCNN) demonstrates superior performance compared to traditional CNN optimization techniques. The results show an accuracy of 96%, recall of 94%, precision of 92%, and an F1 score of 92%. These metrics surpass those of existing methods such as Efficient Supervised Learning based on Deep Neural Network (ESDNN), Hierarchical Deep Learning Support Vector Machine (HDLSVM), Ordinal Regression Neural Network (ORNN), Deep Convolutional Generative Adversarial Network (DCGAN), Convolutional Neural Network (CNN), Local Contrast Normalization Convolutional Neural Network (LCNN), and Visual Geometry Group Network (VGGNET 19). Conclusion The integration of Perceptual Pigeon Galvanized Optimization with a Multi-objective Convolutional Neural Network offers a powerful approach for identifying and classifying mango leaf diseases. The proposed method effectively balances multiple performance metrics, leading to a robust and efficient model suitable for real-world agricultural applications.

Accuracy and robustness of a plant-level cabbage yield prediction system generated by assimilating UAV-based remote sensing data into a crop simulation model

AbstractIn-season crop growth and yield prediction at high spatial resolution are essential for informing decision-making for precise crop management, logistics and market planning in horticultural crop production. This research aimed to establish a plant-level cabbage yield prediction system by assimilating the leaf area index (LAI) estimated from UAV imagery and a segmentation model into a crop simulation model, the WOrld FOod STudies (WOFOST). The data assimilation approach was applied for one cultivar in five fields and for another cultivar in three fields to assess the yield prediction accuracy and robustness. The results showed that the root mean square error (RMSE) in the prediction of cabbage yield ranged from 1,314 to 2,532 kg ha–1 (15.8–30.9% of the relative RMSE). Parameter optimisation via data assimilation revealed that the reduction factor in the gross assimilation rate was consistently attributed to a primary yield-limiting factor. This research further explored the effect of reducing the number of LAI observations on the data assimilation performance. The RMSE of yield was only 107 kg ha–1 higher in the four LAI observations obtained from the early to mid-growing season than for the nine LAI observations over the entire growing season for cultivar ‘TCA 422’. These results highlighted the great possibility of assimilating UAV-derived LAI data into crop simulation models for plant-level cabbage yield prediction even with LAI observations only in the early and mid-growing seasons.

Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations

Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.

Life Cycle Assessment of an Avocado: Grown in South Africa-Enjoyed in Europe.

Food production is known to have significant environmental impacts, with the main contributors residing in the farming and transportation life cycle phases. Of the various food products transported around the world, avocados have increasingly gained attention as a high-commodity superfood. Avocados require specific climatic and agricultural conditions for farming, with the most fertile land and conditions located outside Europe. Consequently, most avocados consumed in Europe are imported over vast geographical distances, with little information available to quantify the environmental impacts of this imported superfood. This paper aims to present the most detailed life cycle assessment results of an avocado cultivated, grown and harvested in the Limpopo Province of South Africa and exported to the European market for sale and consumption. A life cycle assessment was developed for the farming, harvesting, handling, packaging, ripening, transportation, and carbon sequestration potential of the avocado, and it was used to conduct a holistic life cycle assessment. Input data was obtained through an 18-month data collection campaign across the relevant stakeholders. A baseline 'business-as-usual' scenario is focused on throughout this study, and scope for optimisation is identified for each life cycle phase where applicable, accompanied by uncertainty analyses. Results show a total carbon input of 904.85 kg CO2e/tonne. Mitigating this, 521.88 kg CO2e/tonne is offset, resulting in a net carbon footprint of 382.97 kg CO2e/tonne with uncertainty ranges of -23.22 to +58.69 kg CO2e/tonne, normalised to 57.45 g CO2e/avocado grown in South Africa and sold in Europe. The environmental impacts of the avocado industry under consideration are largely mitigated by the "nature first" philosophy of the farming and logistics enterprises, which have made significant investments in reducing emissions. Sensitivity analyses indicate that implementing large-scale renewable energy, using alternative packaging instead of cardboard, and selling avocados unripened could further enable the farming enterprise to achieve Net Zero objectives. These measures could reduce baseline emissions from 382.97 kg CO2e/tonne to a theoretical -68.54 kg CO2e/tonne, representing a 117.9% decrease. Although this study does not quantify climate change impacts, qualitative analyses suggest that climate change will have a net negative effect on the avocado industry in South Africa. These regions, typically located in micro-climates, are projected to become wetter and warmer, adversely affecting crop phenology, pest control, road conditions, management complexity, farmer livelihoods, and food security. The study recommends large-scale implementation of the optimisation strategies identified to achieve Net Zero objectives and the development of proactive climate change mitigation strategies to enhance the resilience of avocado supply chains to future stressors. These insights are crucial for policymakers, industry stakeholders, and consumers aiming to promote sustainability in the avocado market.

Enhancing crop growth and management of bacterial wilt disease in ginger by potential Bacillus strains through induced systemic resistance

ABSTRACT Bacterial wilt disease, caused by Ralstonia solanacearum poses a severe threat to ginger plants. The present study aimed to determine the effect of Bacillus strains to combat R. solanacearum-induced bacterial wilt in ginger by inducing defense enzymes viz., peroxidase(POX), Polyphenol oxidase (PPO), Phenylalanine ammonia-lyase (PAL) and total phenol content, while also promoting growth and yield. The Bacillus strains were isolated from soil samples collected from the Western Ghats, one of India's biological diversity hot spots through random sampling. Two strains exhibiting better antagonistic activity were identified as Bacillus spp through 16S rRNA sequencing. Pot studies were conducted to evaluate the biocontrol efficacy and plant growth promoting ability of these bacterial strains against R. solanacearum. Results showed that ginger plants treated with NIIST NB120 significantly increased shoot length 70.80 cm, leaf length 30.03 cm, leaf breadth 3.42 cm, number of leaves 22, and yield 355.81% compared to plants treated with R. solanacearum, which had lower values. Furthermore, plants treated with NIIST NB120 had the lowest bacterial wilt incidence at 25%, in contrast to 66% in plants treated with pathogen. Application of Bacillus strains also enhanced the activities of defense-related enzymes, including, PAL 0.030 units/ g tissue, PPO 0.0016 units/ g tissue, POD 68.28 units/ g tissue, and total phenolic content 219.5 units/ g tissue, with the lowest enzyme activity observed in pathogen treated plants. These findings suggest that the Bacillus strains NIIST B16 B. siamensis and NIIST NB120 B. amyloliquefaciens could be effective biocontrol agents of ginger bacterial wilt disease.

AI in Agriculture : Revolutionizing Precision Farming and Sustainable Crop Management

This article explores the transformative impact of Artificial Intelligence (AI) on agriculture, focusing on precision farming and sustainable crop management. It highlights how AI-driven technologies are revolutionizing agricultural practices by optimizing resource utilization, enhancing productivity, and promoting sustainability. The article discusses key applications of AI in agriculture, including crop health monitoring, predictive analytics, resource optimization, smart irrigation systems, variable rate application of chemicals, and pest and disease detection. It presents case studies and statistical data demonstrating the significant improvements in crop yields, resource efficiency, and environmental sustainability achieved through AI adoption. The article also addresses the challenges facing widespread AI implementation in agriculture, such as cost barriers, lack of technical expertise, data quality issues, and infrastructure limitations, while exploring future directions including the integration of AI with robotics and blockchain technology.

Into thin air: prehistoric intensive crop management in high altitude western Tibet

Into thin air: prehistoric intensive crop management in high altitude western Tibet

Beneficial soil fungi enhance tomato crop productivity and resistance to the leaf-mining pest Tuta absoluta in agronomic conditions

Research has shown that soil-borne beneficial microorganisms can enhance plant growth, productivity, and resistance against pests and pathogens and could thus serve as a sustainable alternative to agrochemicals. To date, however, the effect of soil-beneficial microbes under commercial crop production has been little assessed. We here investigated the effect of root inoculation with nine well-characterized bacterial and fungal strains and two consortia on tomato performance under intensive tomato crop management practices. We measured the impact of these root inoculations on plant growth, fruit quality, yield, and pest and pathogen incidence. While most microbial strains showed weak effects, we found that the fungal strains Trichoderma afroharzianum T22 and Funneliformis mosseae significantly increased marketable tomato yield. Moreover, we found that inoculation with most of the fungal strains led to a significant reduction in the incidence of the devastating leaf-mining pest Tuta absoluta, while this effect was not observed for bacterial inoculants. In addition, we found that microbial inoculations did not impact the incidence of introduced natural pest enemies, supporting their compatibility with well-established integrated pest management strategies in horticulture. In summary, the observed general positive effects of soil microbes on tomato yield and resistance reinforce the move toward broader adoption of microbial inoculants in future crop production, ultimately improving agricultural sustainability.

A Study on the Allelopathic Effects of Wild Barley (Hordeum spontaneum) Residue Incorporated with Soil on Growth of Some Plant species

This study examines the allelopathic effects of wild barley (Hordeum spontaneum) on the growth and yield of bread wheat (Triticum aestivum), barley (Hordeum vulgare), and wild barley (Hordeum spontaneum). The experiment was conducted in a greenhouse at Shamamar-Hawler/ Kurdistan Region of Iraq using a complete randomized factorial design (CRD) within 3 replications. The roots, shoots, and seeds of wild barley were dried, powdered, and added to the soil at different concentrations (0%, 10%, 20%, 30%, and 40%) in 500 g pots. Each pot was planted with 5 seeds, which were later reduced to 3 seedlings. Growth parameters such as shoot and root lengths, dry weights, number of tillers, weight of spikes, seeds number per plant, and biological yield was measured. The results indicated significant effects of wild barley residues on the studied crops. Wheat showed the highest roots, shoots and total lengths were (29.56 cm, 17.05 cm and 46.58 cm, respectively) and the highest shoots and roots dry weights were (0.24 g and 0.30 g). Barley showed the highest number of stems (1.96) but the lowest shoots and dry weights of roots were (0.11 g and 0.23 g). Wild barley had the highest biological weight and harvest index (HI). Among the plant parts, seed extracts significantly enhanced growth parameters, while roots and shoots extract had the greatest inhibitory effects as wild barley residue concentrations increased. Nevertheless, the HI showed a decline as the concentrations increased, suggesting a detrimental effect on growth efficiency. The study shows the potential of wild barley as a bio-herbicide, emphasizing its allelopathic effects. This suggests that agricultural practices should be carefully considered to maximize the efficiency of crops. Incorporating these results into precision agriculture can greatly improve crop management and increase yield.

Implementation of smart irrigation system

Current global technology significantly impacts agriculture, with automation playing a crucial role by executing procedures without assistance from any individual. This work aims to demonstrate how an individual can set up an affordable automatic irrigation system in a few hours by connecting various electronic components and materials. A sensor-based automated irrigation system has been designed and implemented, providing widespread use and significant advantages. This system aids daily activities by saving time and reducing labor. It makes use of a sensor technology in conjunction with a microcontroller, relay, DC motor, and battery. By measuring the moisture content of the soil and turning on irrigation as necessary, the device functions as an intelligent switch. The motor automatically turns on or off based on soil dryness. Sensor readings are transmitted to a computer for graph generation and analysis, allowing easy monitoring and control via a computer. Green roofs, nurseries, greenhouses, and gardens of all sizes can use this watering system.It conserves time, energy, reduces water wastage, and simplifies irrigation planning for farmers, enhancing their management of crops.

Time series forecasting of rain-induced attenuation using coupled ARIMA-SARIMA models for radio propagation applications over subtropical locations

This present research aimed to identify the optimum model for predicting the rain-induced attenuation along the terrestrial and satellite communication networks and employed the time series forecasting models of Autoregressive integrated moving average (ARIMA) and Seasonal Autoregressive integrated moving average (SARIMA) to examine and contrast several techniques for predicting rain-induced attenuation across a sub-tropical area of South Africa. In this research, the ARIMA was developed using the Box and Jenkins technique to predict long-term rain-induced attenuation in the following South African provinces: Kwazulu-Natal, Eastern Cape, Gauteng, and Northern Cape. The datasets used for the research were obtained from the South African Weather Station from 1994–2023 were used to build and check the model after generating rain-induced attenuation using synthetic storm techniques (SST). The forecasting performance of the seasonal autoregressive integrated moving average (SARIMA) model and that of autoregressive integrated moving average (ARIMA) were compared with four forecast performance measures: - Mean Squared Error (MSE), Mean Absolute Error (MAE), R- squared or the coefficient of determination (R2) and Root Mean Squared Error (RMSE). The results of the study showed that due to its lower forecast performance error indicators, SARIMA performed better than ARIMA in forecasting rain-induced attenuation in South Africa. There is no statistically significant difference between the two projected values, according to the results of a Ljung-box test for significant difference. The authors draw the inference that the two approaches can effectively be employed in their proper locations. Rain-attenuation prediction data indicates that this long-term projection can help decision makers to improve the performance of microwave networks in the face of random fluctuations and avoiding unexpected signal loss with efficient installation of communications infrastructure. It also has applications in prediction of floods, urban planning, and crop management.

Unlocking Innovation in Crop Resilience and Productivity: Breakthroughs in Biotechnology and Sustainable Farming

Recent advancements in agricultural biotechnology and sustainable practices are revolutionizing crop production and resilience, addressing global food security challenges. This review highlights cutting-edge innovations in plant sciences, including genetic engineering, genome editing technologies, and biotechnological interventions that enhance crop resistance to biotic and abiotic stresses. It discusses the integration of precision agriculture and digital farming tools that optimize resource use and boost productivity, providing data-driven insights for better crop management. Additionally, sustainable agricultural practices such as agroecology, organic farming, and integrated pest management are examined for their potential to reduce environmental impact while maintaining high yield. The review investigates the key practices like conservation tillage, crop rotation, and biological pest control, emphasizing their roles in enhancing soil health, biodiversity, and agricultural resilience. Significant contributions of biotechnological interventions, including genetic engineering and genome editing, are explored for developing stress-resistant and high-yield crop varieties. Moreover, the integration of precision agriculture and digital farming technologies is discussed for their potential to optimize resource utilization and improve crop management through advanced data analytics.

Redefining the accumulated temperature index for accurate prediction of rice flowering time in diverse environments.

Accurate prediction of flowering time across diverse environments is crucial for effective crop management and breeding. While the accumulated temperature index (ATI) is widely used as an indicator for estimating flowering time, its traditional definition lacks systematic evaluation and genetic basis understanding. Here, using data from 422 rice hybrids across 47 locations, we identified the optimal ATI calculation window as 1 day after sowing to 26 days before flowering. Based on this redefined ATI, we developed a single-parameter model that outperforms the state-of-the-art reaction norm index model in both accuracy and stability, especially with limited training data. We identified 10 loci significantly associated with ATI variation, including two near known flowering time genes and four linked to ecotype differentiation. To enhance practical utility, we developed an efficient flowering time prediction kit using 28 functionally relevant markers, complemented by a user-friendly online tool (http://xielab.hzau.edu.cn/ATI). Our approach can be easily applied to other crops, as ATI is commonly used across various agricultural systems.

Begomovirus disease of pumpkin crop in India and its management strategies possibility: a review

Pumpkin is an important commercial crop grown worldwide in tropical and subtropical regions. The whitefly-transmitted Pumpkin yellow vein mosaic disease seriously threatens pumpkin cultivation worldwide. The advent of transgenic technology in the 1980s revolutionized the possibilities for introducing virus resistance into agriculturally important plant species. It offered a powerful tool to enhance crop protection and provided a pathway to potentially unlimited sources of resistance against viral diseases. The ongoing research and development in this field continue to explore and refine conventional and non-conventional approaches for effective virus management in agriculture crops. This review focuses on developing transgenic resistance against begomoviruses and discusses possible management strategies to address these challenges.

Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network

Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual maneuver. The CNN model was trained using actual canopy volumes measured using a cylindrical case and small expanded polystyrene (EPS) balls to account for internal plant spaces. Estimated canopy volumes using the CNN with flight altitude compensation closely matched the canopy volumes measured with EPS balls (nearly 1:1 relationship). The model achieved a slope, coefficient of determination (R2), and root mean squared error (RMSE) of 0.98, 0.98, and 74.3 cm3, respectively, corresponding to an 84% improvement over the conventional paraboloid shape approximation. In the application tests, the canopy volume map of the entire strawberry field was generated, highlighting the spatial variability of the plant’s canopy volumes, which is crucial for implementing site-specific management of strawberry crops.

Unraveling the Role of Contaminants Reshaping the Microflora in Zea mays Seeds from Heavy Metal–Contaminated and Pristine Environment

Heavy metal (HM) contaminants are the emerging driving force for reshaping the microflora of plants by eradicating the non-tolerance and non-resistant microbes via their lethal effects. Seeds served as a prime source of ancestral microbial diversity hereditary transfer from generation to generation. However, the problem arises when they got exposed to metal contamination, does metal pollutant disrupt the delicate balance of microbial communities within seeds and lead to shifts in their microflora across generations. In this study, the endophytic community within Zea mays seeds was compared across three distinct regions in Yunnan province, China: a HM-contaminated site Ayika (AK), less-contaminated site Sanduoduo (SD), and a non-contaminated Site Dali (DL). High-throughput sequencing techniques were employed to analyze the microbial communities. A total of 492,177 high-quality reads for bacterial communities and 1,001,229 optimized sequences for fungal communities were obtained. These sequences were assigned to 502 and 239 operational taxonomic units (OTUs) for bacteria and fungi, respectively. A higher diversity was recorded in AK samples than in SD and DL. Microbial community structure analysis showed higher diversity and significant fluctuation in specific taxa abundance in the metal-polluted samples exhibiting higher response of microbial flora to HM. In AK samples, bacterial genera such as Gordonia and Burkholderia-Caballeronia-Paraburkholderia were dominant, while in SD Pseudomonas and Streptomyces were dominant. Among the fungal taxa, Fusarium, Saccharomycopsis, and Lecanicillium were prevalent in HM-contaminated sites. Our finding revealed the influential effect of HM contaminants on reshaping the seed microbiome of the Zea mays, showing both the resilience of certain important microbial taxa as well the shifts in the diversity in the contaminated and pristine conditions. The knowledge will benefit to develop effective soil remediation, reclamation, and crop management techniques, and eventually assisting in the extenuation of metal pollution's adverse effects on plant health and agricultural productivity.

Al-based Techniques for Comprehensive Crop Nutrition Assessment: A Review

Artificial Intelligence (AI) methodologies, such as machine learning, deep learning, and data fusion, are transforming agricultural practices by offering advanced tools for data-driven decision-making, predictive modeling, and automated processes. This paper explores the latest AI techniques utilized in agriculture to enhance productivity, optimize resource management, and address the challenges posed by climate change. By harnessing AI-driven solutions for crop management, soil analysis, and pest control, these methods significantly contribute to sustainable agriculture, benefiting both farmers and the environment through more efficient and eco-friendly practices. However, while AI holds immense promise, challenges remain in terms of accessibility, data quality, and the adaptation of these techniques to diverse agricultural conditions. This review aims to provide a balanced overview of the current state of AI applications in agriculture, offering insights into the opportunities and limitations faced by this growing field.