Fungal Species Causing Maize Leaf Blight in Different Agro-Ecologies in India

Foliar diseases of maize cause severe economic losses in India and around the world. The increasing severity of maize leaf blight (MLB) over the past ten years necessitates rigorous identification and characterization of MLB-causing pathogens from different maize production zones to ensure the success of resistance breeding programs and the selection of appropriate disease management strategies. Although Bipolaris maydis is the primary pathogen causing MLB in India, other related genera such as Curvularia, Drechslera, and Exserohilum, and a taxonomically distant genus, Alternaria, are known to infect maize in other countries. To investigate the diversity of pathogens associated with MLB in India, 350 symptomatic leaf samples were collected between 2016 and 2018, from 20 MLB hotspots in nine states representing six ecological zones where maize is grown in India. Twenty representative fungal isolates causing MLB symptoms were characterized based on cultural, pathogenic, and molecular variability. Internal Transcribed Spacer (ITS) and glyceraldehyde-3-phosphate dehydrogenase (GADPH) gene sequence-based phylogenies showed that the majority of isolates (13/20) were Bipolaris maydis. There were also two Curvularia papendorfii isolates, and one isolate each of Bipolaris zeicola, Curvularia siddiquii, Curvularia sporobolicola, an unknown Curvularia sp. isolate phylogenetically close to C. graminicola, and an Alternaria sp. isolate. The B. zeicola, the aforesaid four Curvularia species, and the Alternaria sp. are the first reports of these fungi causing MLB in India. Pathogenicity tests on maize plants showed that isolates identified as Curvularia spp. and Alternaria sp. generally caused more severe MLB symptoms than those identified as Bipolaris spp. The diversity of fungi causing MLB, types of lesions, and variation in disease severity by different isolates described in this study provide baseline information for further investigations on MLB disease distribution, diagnosis, and management in India.

Transcriptional Analysis of Maize Leaf Tissue Treated With Seaweed Extract Under Drought Stress

Kappaphycus alvarezii seaweed extract (KSWE) has been known for its plant biostimulant and stress alleviation activities on various crops. However, very few reports are available depicting its impact at the molecular level, which is crucial in identifying the mechanism of action of KSWE on plants. Here, maize leaf tissue of control and KSWE-treated plants were analyzed for their transcriptional changes under drought stress. KSWE was applied foliarly at the V5 stage of maize crop under drought, and leaf transcriptome analysis was performed. It was found that a total of 380 and 631 genes were up- and downregulated, respectively, due to the application of KSWE. Genes involved in nitrate transportation, signal transmission, photosynthesis, transmembrane transport of various ions, glycogen, and starch biosynthetic processes were found upregulated in KSWE-treated plants, while genes involved in the catabolism of polysaccharide molecules such as starch as well as cell wall macromolecules like chitin and protein degradation were found downregulated. An overview of differentially expressed genes involved in metabolic as well as regulatory processes in KSWE-treated plants was also analyzed via Mapman tool. Phytohormone signaling genes such as cytokinin-independent 1 (involved in cytokine signal transduction), Ent-kaurene synthase and GA20 oxidase (involved in gibberellin synthesis), and gene of 2-oxoglutarate-dependent dioxygenase enzyme activity (involved in ethylene synthesis) were found upregulated while 9-cis-epoxycarotenoid dioxygenase (a gene involved in abscisic acid synthesis) was found downregulated due to the application of KSWE. Modulation of gene expression in maize leaf tissue in response to KSWE treatment elucidates mechanisms to ward off drought stress, which can be extended to understand similar phenomenon in other crops as well. This molecular knowledge can be utilized to make the use of KSWE more efficient and sustainable.

Analysis of the relationship between leaf color spectrum and soil plant analysis development

Assessment nutrient content of maize leaf is particularly important in achieving higher grain yield. Characterization of leaf chlorophyll involves routine Soil Plant Analyzer Development (SPAD) reading particularly at critical stage of growth development. The objective of the study was to assess the color spectrum of maize leaf in relation to the chlorophyll content by using Random-forest modeling. genotypes of corn plants based on the characters of the ear and kernel using a logistic regression model. The research was conducted at IP2TP Bajeng in 2021 by planting maize varieties at various fertilizer level. RGB data of maize leaf was recorded by using Hamamatsu sensor (Hamamatsu, Japan), and converted to HIS, HSV and LAB color spectrum. The results indicated that Random-forest model with 20-fold validation indicated the highest accuracy as compared to the other fold-range. Among the tested model, integration of Random-forest model to LAB (Light, red/green coordinate, and the yellow/blue coordinate) color spectrum provided the best model performances with RMSE (4.77), MSE (22.76), MAE (3.80) and R2 (0.853). This value indicates that the use of Hamamatsu color sensor and converted into LAB color spectrum provided the best SPAD (Soil Plant Analyzer Development) reading with high accuracy and consistency of results. Thus, digital based model can be integrated with manual selection for fast and precise nutrient monitoring.

Integration of GWAS and TWAS to elucidate the genetic architecture of natural variation for leaf cuticular conductance in maize

The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed. Dissecting the genetic architecture of natural variation for maize leaf cuticular conductance (gc) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we performed an integrated genome- and transcriptome-wide association study (GWAS/TWAS) to identify candidate genes putatively regulating variation in leaf gc. Of the 22 plausible candidate genes identified, five were predicted to be involved in cuticle precursor biosynthesis and export, two in cell wall modification, nine in intracellular membrane trafficking, and seven in the regulation of cuticle development. A gene encoding an INCREASED SALT TOLERANCE1-LIKE1 (ISTL1) protein putatively involved in intracellular protein and membrane trafficking was identified in GWAS and TWAS as the strongest candidate causal gene. A set of maize nested near-isogenic lines that harbor the ISTL1 genomic region from eight donor parents were evaluated for gc, confirming the association between gc and ISTL1 in a haplotype-based association analysis. The findings of this study provide novel insights into the role of regulatory variants in the development of the maize leaf cuticle, and will ultimately assist breeders to develop drought-tolerant maize for target environments.

High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module

Maize leaf disease detection is an essential project in the maize planting stage. This paper proposes the convolutional neural network optimized by a Multi-Activation Function (MAF) module to detect maize leaf disease, aiming to increase the accuracy of traditional artificial intelligence methods. Since the disease dataset was insufficient, this paper adopts image pre-processing methods to extend and augment the disease samples. This paper uses transfer learning and warm-up method to accelerate the training. As a result, three kinds of maize diseases, including maculopathy, rust, and blight, could be detected efficiently and accurately. The accuracy of the proposed method in the validation set reached 97.41%. This paper carried out a baseline test to verify the effectiveness of the proposed method. First, three groups of CNNs with the best performance were selected. Then, ablation experiments were conducted on five CNNs. The results indicated that the performances of CNNs have been improved by adding the MAF module. In addition, the combination of Sigmoid, ReLU, and Mish showed the best performance on ResNet50. The accuracy can be improved by 2.33%, proving that the model proposed in this paper can be well applied to agricultural production.

A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data

Determining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms.