Maize Leaf Research Articles

Evaluating Bacterial Nanocellulose Interfaces for Recording Surface Biopotentials from Plants.

The study of plant electrophysiology offers promising techniques to track plant health and stress in vivo for both agricultural and environmental monitoring applications. Use of superficial electrodes on the plant body to record surface potentials may provide new phenotyping insights. Bacterial nanocellulose (BNC) is a flexible, optically translucent, and water-vapor-permeable material with low manufacturing costs, making it an ideal substrate for non-invasive and non-destructive plant electrodes. This work presents BNC electrodes with screen-printed carbon (graphite) ink-based conductive traces and pads. It investigates the potential of these electrodes for plant surface electrophysiology measurements in comparison to commercially available standard wet gel and needle electrodes. The electrochemically active surface area and impedance of the BNC electrodes varied based on the annealing temperature and time over the ranges of 50 °C to 90 °C and 5 to 60 min, respectively. The water vapor transfer rate and optical transmittance of the BNC substrate were measured to estimate the level of occlusion caused by these surface electrodes on the plant tissue. The total reduction in chlorophyll content under the electrodes was measured after the electrodes were placed on maize leaves for up to 300 h, showing that the BNC caused only a 16% reduction. Maize leaf transpiration was reduced by only 20% under the BNC electrodes after 72 h compared to a 60% reduction under wet gel electrodes in 48 h. On three different model plants, BNC-carbon ink surface electrodes and standard invasive needle electrodes were shown to have a comparable signal quality, with a correlation coefficient of >0.9, when measuring surface biopotentials induced by acute environmental stressors. These are strong indications of the superior performance of the BNC substrate with screen-printed graphite ink as an electrode material for plant surface biopotential recordings.

Integrating UAV hyperspectral data and radiative transfer model simulation to quantitatively estimate maize leaf and canopy nitrogen content

Crop nitrogen (N) content reflects crop nutrient status and plays an important role in precision nutrient management. Accurate crop N content estimation from remote sensing has been well documented. However, the robustness (i.e., the ability of a model to perform consistently across various conditions) of these methods under varied soil conditions or different growth stages has rarely been considered. We proposed a hybrid method that integrates in-situ measurements and the data simulated by a mechanistic model to improve the estimation of maize N content. In-situ data included hyperspectral images collected by Unmanned Aerial Vehicle (UAV), and leaf and canopy N content (LNC and CNC). A mechanistic radiative transfer model (PROSAIL-PRO) was used to generate simulated data, i.e., canopy reflectance paired with target crop traits (i.e., LNC, CNC). We compared the performance from the hybrid method with a machine learning method (Gaussian Process Regression) and six different vegetation indices (VIs) on four in-situ datasets collected at three study sites from 2021 to 2022. Results show that the hybrid method consistently performed the best for LNC estimation across four testing datasets (RRMSE ranging from 10.08% to 10.84%). For CNC estimation, the hybrid method had the best estimation results on two out of the four testing datasets and performed comparably to the best method on the other two datasets (RRMSE ranging from 13.89% to 25.21%). Next, we assessed the estimation robustness of the hybrid method, the machine learning, and the best-VI by comparing the mean (µ) and standard deviation (σ) of RRMSE across diverse water and N treatments (condition #1) and different growth stages (condition #2). Among 16 total cases (two crop traits by four study sites by two conditions), the hybrid method had 11 cases of smallest µ and seven cases of smallest σ, outperforming the machine learning (0/16 for µ, 4/16 for σ) and the best-VI (5/16 for µ, 5/16 for σ). These results underscore the greater robustness of the hybrid method. This study highlights the potential of integrating in-situ measurements and simulated data to improve estimation accuracy and robustness for maize LNC and CNC. The promising performance of the hybrid method suggests its applicability to a broader range of crops and various crop traits.

Deep transfer learning for fine-grained maize leaf disease classification

Machine learning (ML) can enhance agricultural yields by combating plant diseases and climate change. However, traditional image processing techniques for disease detection have limitations in robustness and generalizability. In this study, we investigate deep transfer learning for fine-grained disease classification in maize plants, which is a challenging task due to the subtle and nuanced disease patterns. We use four tailored deep learning frameworks: VGGNET, Inception V3, ResNet50, and InceptionResNetV2. ResNet50 achieves the highest validation accuracy of 87.51%, precision of 90.33%, and recall of 99.80%, demonstrating the efficacy and superiority of our approach. Our study offers an innovative solution for accurate disease classification in maize plants.

Screening of leaf extraction and storage conditions for eco-metabolomics studies.

Mass spectrometry-based plant metabolomics is frequently used to identify novel natural products or study the effect of specific treatments on a plant's metabolism. Reliable sample handling is required to avoid artifacts, which is why most protocols mandate shock freezing of plant tissue in liquid nitrogen and an uninterrupted cooling chain. However, the logistical challenges of this approach make it infeasible for many ecological studies. Especially for research in the tropics, permanent cooling poses a challenge, which is why many of those studies use dried leaf tissue instead. We screened a total of 10 extraction and storage approaches for plant metabolites extracted from maize leaf tissue across two cropping seasons to develop a methodology for agroecological studies in logistically challenging tropical locations. All methods were evaluated based on changes in the metabolite profile across a 2-month storage period at different temperatures with the goal of reproducing the metabolite profile of the living plant as closely as possible. We show that our newly developed on-site liquid-liquid extraction protocol provides a good compromise between sample replicability, extraction efficiency, material logistics, and metabolite profile stability. We further discuss alternative methods which showed promising results and feasibility of on-site sample handling for field studies.

Screening of tropical maize inbred lines for disease resistance in Bangladesh

The cultivation of maize in Bangladesh has been boosted over the last decade. Concurrently, diseases are also becoming a major threat to its higher production because of lacking disease-resistant cultivars. The study screened 29 maize inbred lines against southern corn leaf blight, northern corn leaf blight, downy mildew, rust, stalk rot, anthracnose, and maize leaf streak in field conditions. Among the inbred lines, Ag-0296, Ag-0305, Ag-0309, Ag-0312, Ag-0314, Ag-0318 and Ag-0321 showed resistance to various diseases. Twenty-one inbred lines belonged to cluster I demonstrated highest plant height (154.75 cm), ear height (61.19 cm) and yield (4.41 t/h), intermediate days to pollen shedding (108.43), days to silking (109.90) and days to maturity (157.14). Finally, cluster analysis revealed Ag-0296, Ag-0298, Ag-0300, Ag-0301, Ag-0303, Ag-0304, Ag-0305, Ag-0306, Ag-0307, Ag-0309, Ag-0310, Ag-0311, Ag-0313, Ag-0315, Ag-0318, Ag-0319, Ag-0321 and Ag-0324 belongs to Cluster I as they showed both disease resistance ability with good agronomic attributes.

An ideal model of plant-vector-phytopathogen interaction and the management of the vector

Maize (Zea mays L.) is a vital cereal crop worldwide, crucial for global food security and various industrial applications. Its cultivation faces significant challenges from a diverse array of insect pests and pathogens, notably including the maize leaf aphid (Rhopalosiphum maidis) and the maize dwarf mosaic virus (MDMV). This paper explores the intricate interactions among maize, its insect vectors, and MDMV, emphasizing the urgent need for a deeper understanding to develop sustainable management strategies. Maize exhibits vast genetic diversity and is cultivated across diverse environments, making it susceptible to a range of pests and diseases. The transmission of MDMV by aphids, particularly R. maidis, poses a significant threat to maize production globally. The complex tripartite interaction between maize, aphids, and MDMV serves as an ideal model system for studying plant-insect-phytopathogen interactions. Understanding the components of this interaction is critical for developing effective management strategies. Despite significant research efforts, there remains a knowledge gap in the molecular mechanisms underlying vector-borne diseases. Further research on the molecular level is essential for identifying specific targets for genetic pest control and disrupting pathogen transmission by insect vectors. Developing countries, in particular, require intensified research efforts to address the growing challenges to food security and agricultural sustainability. Thus, unraveling the complexities of plant-insect-phytopathogen interactions is essential for devising effective strategies to combat vector-borne diseases and sustain global food systems. Enhanced scientific research, especially in developing regions, is crucial for addressing these challenges and ensuring food security for future generations.

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images

Maize is a globally important cereal crop, however, maize leaf disease is one of the most common and devastating diseases that afflict it. Artificial intelligence methods face challenges in identifying and classifying maize leaf diseases due to variations in image quality, similarity among diseases, disease severity, limited dataset availability, and limited interpretability. To address these challenges, we propose a residual-based multi-scale network (MResNet) for classifying multi-type maize leaf diseases from maize images. MResNet consists of two residual subnets with different scales, enabling the model to detect diseases in maize leaf images at different scales. We further utilize a hybrid feature weight optimization method to optimize and fuse the feature mapping weights of two subnets. We validate MResNet on a maize leaf diseases dataset. MResNet achieves 97.45% accuracy. The performance of MResNet surpasses other state-of-the-art methods. Various experiments and two additional datasets confirm the generalization performance of our model. Furthermore, thermodynamic diagram analysis increases the interpretability of the model. This study provides technical support for the disease classification of agricultural plants.

Maize emergence rate and leaf emergence speed estimation via image detection under field rail-based phenotyping platform

Accurate and efficient acquisition of maize emergence rate and leaf emergence speed in the field is essential for detecting seed quality, evaluating crop field management plans, and yield assessment. This study constructs a system solution to obtain the maize seedling emergence rate and leaf emergence speed based on the field rail-based phenotyping platform and convolutional neural network. Firstly, we use the field rail-based phenotyping platform to collect a high-temporal sequence visible light images of maize plant during the seedling stage. In the first stage, an improved Faster R-CNN is used to detect maize seedlings in the plot images, and the plant ROI area is cropped as the input for the second stage network. In the second stage, the best performing ResNeSt network out of four backbone networks is chosen, using the Mask R-CNN model to segment the leaves of the input plant image, which is then used to calculate the number of leaves. We propose a quantification index for leaf emergence speed based on a weighted average combination of leaf numbers. Using the method described in this paper, we analyzed the plant images from 52 inbred lines plots of over seven consecutive days. The experimental results show that when the Intersection Over Union (IOU) is 0.50, the bbox_mAP of the maize seedling detection model is 0.969, with an accuracy rate of 99.53%. Compared with manual counting, the calculated R2 is 0.997 and RMSE is 43.382. The segm_mAP of the plant leaf segmentation model is 0.942. The differences in emergence rate and leaf emergence speed across 52 inbred lines were compared, providing new phenotyping reference indices for further exploring the genotypic differences affecting seed emergence and leafing.

CASF-MNet: multi-scale network with cross attention mechanism and spatial dimension feature fusion for maize leaf disease detection

Maize is an important food crop, and accurate identification of maize leaf diseases is significant for disease control. Aiming at the complexity of the color and texture features of diseases with inter-class similarity and intra-class variability, a two-channel multi-scale network model with a cross-attention mechanism and spatial dimension feature fusion (CASF-MNet) was designed. Firstly, aiming at the complexity of color features, the HSV color Sub-network (CSNet) was designed to extract the color features of disease spots. Then, aiming at the complexity of texture features, Texture Sub-Network (TSNet) and Cross Attention mechanism (CAM) were proposed to enhance the texture features of disease spots. Finally, to better fuse color and texture features, a cross-spatial feature fusion mechanism (FFM) was proposed. In the five-fold cross-validation of the self-built dataset, the average F1-Score and average Accuracy of CASF-MNet reach 97.17% and 97.02%. In the test experiments of two public datasets, the F1-Score of CASF-MNet reaches 98.13% and 98.71%, and the Accuracy reaches 97.44% and 98.67%, which are better than the seven most advanced leaf disease identification methods. It is proved that CASF-MNet is advanced and stable, and can assist disease detection personnel in maize disease detection in field environments.

Use of unmanned aerial vehicle-derived multi-spectral data for the early detection of multi-temporal maize leaf equivalent water thickness and fuel moisture content for the improved resilience of smallholder maize farming

Use of unmanned aerial vehicle-derived multi-spectral data for the early detection of multi-temporal maize leaf equivalent water thickness and fuel moisture content for the improved resilience of smallholder maize farming

WGCNA analysis of the effect of exogenous BR on leaf angle of maize mutant lpa1

Leaf angle, as one of the important agronomic traits of maize, can directly affect the planting density of maize, thereby affecting its yield. Here we used the ZmLPA1 gene mutant lpa1 to study maize leaf angle and found that the lpa1 leaf angle changed significantly under exogenous brassinosteroid (BR) treatment compared with WT (inbred line B73). Transcriptome sequencing of WT and lpa1 treated with different concentrations of exogenous BR showed that the differentially expressed genes were upregulated with auxin, cytokinin and brassinosteroid; Genes associated with abscisic acid are down-regulated. The differentially expressed genes in WT and lpa1 by weighted gene co-expression network analysis (WGCNA) yielded two gene modules associated with maize leaf angle change under exogenous BR treatment. The results provide a new theory for the regulation of maize leaf angle by lpa1 and exogenous BR.

Efficient plant leaf detection through machine learning approach based on corn leaf image classification

Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the improved k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.

Analysis of unintended open reading frame ORF-130 expression in maize event MZIR098 by LC-MS as part of the allergenicity risk assessment of genetically modified crops

Rigorous safety assessments are required for genetically modified (GM) crops to support their use as food or feed. These assessments include bioinformatics analysis of the amino acid sequences of all potential open reading frames (ORFs) encoded in the exogenous DNA introduced into the plant through genetic modification. Sequence similarities of potential DNA products to known or potential toxins and allergens trigger subsequent assessments of potential hazards for the consumer or the environment. The bioinformatic analysis of Syngenta maize event MZIR098 insert DNA revealed a total of 415 ORFs. One putative ORF (ORF-130) triggered further risk assessment based on its sequence similarities with known allergens, including peanut allergen Ara h 1, wheat glutenin, and others. We examined the potential expression of ORF-130 at a protein level to exclude the possibility of exposure during consumption of MZIR098 maize. Specifically, we developed and validated a liquid chromatography approach coupled to a tandem mass spectrometry (LC-MS/MS) method, called parallel reaction monitoring (PRM). This measure of expression was quantitative and targeted against the peptides unique to ORF-130. The linear range of the method was 25-5000 amol ORF-130/µl. Both, limit of detection (LOD) and lower limit of quantitation (LLOQ) of the method, were 12 μg ORF-130 per gram of dry MZIR098 maize leaf. The method was accurate and specific to ORF-130. PRM analysis of MZIR098 extracts did not detect putative ORF-130 above the limits of detection/quantitation. Furthermore, the broad proteomic data dependent acquisition (DDA) analysis of the MZIR098 extracts demonstrated no evidence of ORF-130 corroborating the PRM analysis. Our results confirm that ORF-130 is not characterized as a functional gene in Syngenta maize event MZIR098 insert DNA and does not express gene products at detectable levels.

The complex stress of waterlogging and high temperature accelerated maize leaf senescence and decreased photosynthetic performance at different growth stages

AbstractThe greenhouse effect caused by global warming was becoming more and more obvious, resulting in increased frequency of high temperature and high humidity, which significantly affected maize productivity. However, it was poorly understood how the interactions of high temperature and high humidity affected leaf senescence, photosynthetic performance and yield of summer maize. Three stress treatments including (a) high temperature stress (T), (b) waterlogging stress (W) and (c) complex stress (T‐W) were set at the third leaf stage (V3), the sixth leaf stage (V6) and the tasselling stage (VT) in 2019–2020 to explore the influence mechanism of complex stress. Each stress treatment period lasted 6 days. Non‐stressed plants served as control. Yield, antioxidant enzyme activity, photosynthetic characteristics, and dry matter accumulation were determined. Our study found that the activity of antioxidant enzymes was significantly decreased, while malonyldialdehyde (MDA) accumulation was increased under each stress treatment. As a result, the photosynthetic characteristics were impaired, manifested in a significant decrease in net photosynthetic rate (Pn), enzyme activities of phosphoenolpyruvate carboxylase (PEPCase) and ribulose diphosphate carboxylase (RUBPCase). The decrease in photosynthetic intensity affected by each stress treatment led to a significant decrease in total dry matter accumulation and grain yield. The most significant effects of waterlogging and combined stresses on yield occurred at the V3 stage, followed by the V6 and VT stages. However, the most significant effects of high temperature occurred at the VT stage, followed by the V6 and V3 stages. Moreover, the compound stress exacerbated damage to leaf senescence and photosynthetic properties of summer maize compared to the single stress of high temperature or waterlogging.

CO2 elevation and N fertilizer supply modulate leaf physiology, crop growth and water use efficiency of maize in response to progressive soil drought

AbstractElevated atmospheric CO2 concentration (e[CO2]) and varied nitrogen (N) fertilization levels may mediate the different responses of C4 crops to progressive soil drought. In this study, the effects of reduced N (N1, 0.8 g pot−1) and adequate N (N2, 1.6 g pot−1) supply on leaf physiology, plant growth and water use efficiency (WUE) of maize (C4 crop) exposed to progressive soil drought grown at ambient CO2 (a[CO2], 400 ppm) and elevated CO2 (e[CO2], 800 ppm) concentration were investigated. The results indicated that compared with a[CO2], net photosynthetic rate (An) and leaf water potential (Ψl) at e[CO2] were maintained in maize leaves, while stomatal conductance (gs), transpiration rate and leaf hydraulic conductance were decreased, leading to enhanced WUE from stomatal to leaf scale. Despite An and Ψl of e[CO2] plants were more sensitive to progressive soil drought under both N fertilization levels, e[CO2] would increase leaf ABA concentration ([ABA]leaf) but decline the gs response to [ABA]leaf under N1 supply. e[CO2] coupled with N1 fertilization was conducive to enlarging leaf area, promoting specific leaf area, root and total dry mass, whereas reduced stomatal aperture and plant water use under progressive drought stress, contributing to an improvement in plant WUE, implying a better modulation of maize leaf stomata and water status under reduced N supply combined with e[CO2] responding to progressive soil drought. These findings in the current study would provide valuable advice for N management on maize (C4) crop efficient water use in a drier and CO2‐enriched environment.

Enabling High-throughput Transgene Expression Studies Using Automated Liquid Handling for Etiolated Maize Leaf Protoplasts.

The field of plant biotechnology has witnessed remarkable advancements in recent years, revolutionizing the ability to manipulate and engineer plants for various purposes. However, as research in this field increases in diversity and becomes increasingly sophisticated, the need for early, efficient, dependable, and high-throughput transient screening solutions to narrow down strategies proceeding to stable transformation is more apparent. One method that has re-emerged in recent years is the utilization of plant protoplast, for which methods of isolation and transfection are available in numerous species, tissues, and developmental stages. This work describes a simple automated protocol for the randomized preparation of plasmid within a 96-well plate, a method for the isolation of etiolated maize leaf protoplast, and an automated transfection procedure. The adoption of automated solutions in plant biotechnology, exemplified by these novel liquid handling protocols for plant protoplast transfection, represents a significant advancement over manual methods. By leveraging automation, researchers can easily overcome the limitations of traditional methods, enhance efficiency, and accelerate scientific progress.

The serine-threonine protein kinase Snf1 orchestrates the expression of plant cell wall-degrading enzymes and is required for full virulence of the maize pathogen Colletotrichum graminicola

Colletotrichum graminicola, the causal agent of maize leaf anthracnose and stalk rot, differentiates a pressurized infection cell called an appressorium in order to invade the epidermal cell, and subsequently forms biotrophic and necrotrophic hyphae to colonize the host tissue. While the role of force in appressorial penetration is established (Bechinger et al., 1999), the involvement of cell wall-degrading enzymes (CWDEs) in this process and in tissue colonization is poorly understood, due to the enormous number and functional redundancy of these enzymes. The serine/threonine protein kinase gene SNF1 identified in Sucrose Non-Fermenting yeast mutants mediates de-repression of catabolite-repressed genes, including many genes encoding CWDEs. In this study, we identified and functionally characterized the SNF1 homolog of C. graminicola. Δsnf1 mutants showed reduced vegetative growth and asexual sporulation rates on media containing polymeric carbon sources. Microscopy revealed reduced efficacies in appressorial penetration of cuticle and epidermal cell wall, and formation of unusual medusa-like biotrophic hyphae by Δsnf1 mutants. Severe and moderate virulence reductions were observed on intact and wounded leaves, respectively. Employing RNA-sequencing we show for the first time that more than 2,500 genes are directly or indirectly controlled by Snf1 in necrotrophic hyphae of a plant pathogenic fungus, many of which encode xylan- and cellulose-degrading enzymes. The data presented show that Snf1 is a global regulator of gene expression and is required for full virulence.

Statistical analysis and comparison of deep convolutional neural network models for the identification and classification of maize leaf diseases

Statistical analysis and comparison of deep convolutional neural network models for the identification and classification of maize leaf diseases

Utilizing Two Populations Derived from Tropical Maize for Genome-Wide Association Analysis of Banded Leaf and Sheath Blight Resistance

Banded leaf and sheath blight (BLSB) in maize is a soil-borne fungal disease caused by Rhizoctonia solani Kühn, resulting in significant yield losses. Investigating the genes responsible for regulating resistance to BLSB is crucial for yield enhancement. In this study, a multiparent maize population was developed, comprising two recombinant inbred line (RIL) populations totaling 442 F8RILs. The populations were generated by crossing two tropical inbred lines, CML444 and NK40-1, known for their BLSB resistance, as female parents, with the high-yielding but BLSB-susceptible inbred line Ye107 serving as the common male parent. Subsequently, we utilized 562,212 high-quality single nucleotide polymorphisms (SNPs) generated through genotyping-by-sequencing (GBS) for a comprehensive genome-wide association study (GWAS) aimed at identifying genes responsible for BLSB resistance. The objectives of this study were to (1) identify SNPs associated with BLSB resistance through genome-wide association analyses, (2) explore candidate genes regulating BLSB resistance in maize, and (3) investigate pathways involved in BLSB resistance and discover key candidate genes through Gene Ontology (GO) analysis. The GWAS analysis revealed nineteen SNPs significantly associated with BLSB that were consistently identified across four environments in the GWAS, with phenotypic variation explained (PVE) ranging from 2.48% to 11.71%. Screening a 40 kb region upstream and downstream of the significant SNPs revealed several potential candidate genes. By integrating information from maize GDB and the NCBI, we identified five novel candidate genes, namely, Zm00001d009723, Zm00001d009975, Zm00001d009566, Zm00001d009567, located on chromosome 8, and Zm00001d026376, on chromosome 10, related to BLSB resistance. These candidate genes exhibit association with various aspects, including maize cell membrane proteins and cell immune proteins, as well as connections to cell metabolism, transport, transcriptional regulation, and structural proteins. These proteins and biochemical processes play crucial roles in maize defense against BLSB. When Rhizoctonia solani invades maize plants, it induces the expression of genes encoding specific proteins and regulates corresponding metabolic pathways to thwart the invasion of this fungus. The present study significantly contributes to our understanding of the genetic basis of BLSB resistance in maize, offering valuable insights into novel candidate genes that could be instrumental in future breeding efforts to develop maize varieties with enhanced BLSB resistance.

Effects of Tillage Practices on the Growth and Yield of Maize, in the Guinea Savannah Ecological Zone of Ghana

Abstract: A field study was conducted to investigate the influence of conventional tillage (CVT) and conservation tillage (CST) on various aspects of maize yield parameters and grain yield, using the Wagdaata maize variety. The study was conducted at the experimental farm of Tamale Technical University, located at Northern region, Tamale, Sagnarigu municipality in the Guinea savannah ecological zone of Ghana in 2023 cropping season. A randomised complete block design was used with three replicates. The treatments consisted of two tillage practices: conventional tillage (CVT) and conservation tillage (CST) and a control. The CVT involved plowing the soil to a depth of 15cm using a tractor plough with disk to plow. The CST involved use of hoe to make ridges and the control was direct seeding without loosen the soil. Plant height was evaluated every two weeks by measuring the height of maize to the base of the apex leaf. Other data collected were: maize leaf area. Cob characteristics including ear height, cob weight with husk, fresh cob weight, and dry cob weight, number of cobs per hectare and number of kernels per cob, 1000 seed weight and grain yields. The present study revealed that plant height, maize leaf area and shoot dry weight increased in the ridges than the other tillage practices. Cob characteristics such as ear height, cob weight with husk, fresh cob weight, and dry cob weight, number of cobs per hectare and number of kernels per cob, 1000 seed weight and grain yields was also optimum with the ridges as a against the other tillage practices. The study showed increased grain yield by 66% in the ridge tillage (2,389 kg/ha) over the plough tillage (1,436 kg/ha). The study provides comprehensive insights into the impact of tillage practices on various aspects of maize growth and yield, with ridge tillage emerging as a favorable practice for optimizing maize plant development and overall productivity in the Guinea savannah ecological zone of Ghana.