Search Results (29)

Determining the effectiveness of fungicide programs based on cultivar resistance to pathogens, especially late leaf spot (caused by Nothopassalora personata (Berk. & M.A. Curtis) [U. Braun, C. Nakash., Videira & Crous]) is important in establishing recommendations to peanut (Arachis hypogaea L.) farmers. Research was conducted in North Carolina during 2021 and 2022 at three locations to compare the incidence of late leaf spot (e.g., visual estimates of percent of peanut leaflets with lesions), percentage of the peanut canopy defoliated caused by this disease, and yield of the peanut cultivars Bailey II, Emery, and Sullivan when exposed to five fungicide regimens including a non-treated control. Peanut yield was not affected by the interaction of cultivar × fungicide regimens. While differences in leaf spot incidence and canopy defoliation were noted for cultivars, these differences did not translate into differences in peanut yield. All fungicides regimens protected peanut yield from leaf spot disease regardless of the number of sprays during the cropping cycle (e.g., three, four, or five sprays). Peanut yield in the absence of fungicides was 4410 kg/ha compared with a range of 5000 to 5390 kg/ha when fungicides were applied. Peanut yield was greater when fungicides were applied four or five times compared with only three sprays or non-treated peanut. The regimen with five consecutive sprays of chlorothalonil alone for the first and final spray in the regimen and when this fungicide was applied with tebuconazole for the second, third, and fourth sprays was as effective as fungicide regimens including combinations of pydiflumetofen plus azoxystrobin plus benzovindiflupyr, mefentrifluconazole plus pyraclostrobin plus fluxapyroxad, bixafen plus flutriafol, and prothioconazole plus tebuconazole. Full article

Colletotrichum fructicola is a member of the gloeosporioides complex and can act as a pathogen, causing anthracnose in various plants and as an endophyte residing in healthy plants. As a plant pathogen, C. fructicola has been frequently reported to cause anthracnose in chili fruit and tea plants, bitter rot in apples and pears, crown rot in strawberries, and Glomerella leaf spot in apples, which are the most common diseases associated with this pathogen. Over the years, C. fructicola has been reported to infect a wide range of plants in tropical, subtropical, and temperate regions, including various types of fruit crops, ornamental and medicinal plants, tree nuts, peanuts, and weeds. Several reports have also been made regarding endophytic C. fructicola recovered from different plant parts. Endophytic C. fructicola has the ability to switch to a pathogenic state, which may contribute to the infection of host and other susceptible plants. Due to the economic importance of C. fructicola infections, the present review highlighted C. fructicola as a plant pathogen and endophyte, providing a summary of its infections in various plants and endophytic ability to inhabit plant tissues. Several control measures for managing C. fructicola infections have also been provided. Full article

Peanut rust, caused by Puccinia arachidis Speg., is one of the most significant leaf diseases globally, and has a severe impact on peanut yield and quality. The development of disease-resistant varieties is recognized as an effective strategy to mitigate the damage caused by peanut rust. However, the research foundation for understanding peanut rust remains relatively limited. In this study, we identified significant single nucleotide polymorphisms (SNPs) associated with peanut rust resistance using a natural population consisting of 353 peanut germplasm accessions. These accessions were analyzed based on resequencing data and rust disease phenotypes across one laboratory test and three field trials. A total of 18 significant SNPs were identified on chromosomes A05 (5 SNPs), A08 (7 SNPs), and A12 (6 SNPs). Notably, three SNPs—Arahy.05_93085395, Arahy.05_93114354, and Arahy.12_4097252—were consistently detected across multiple environments. Within their confidence intervals, 48 genes were annotated, including 9 NLR domain-containing genes functionally related to plant disease resistance, which may serve as candidate genes for peanut rust resistance. This study provides insights into the regulatory mechanisms underlying peanut rust resistance. Full article

Leaf spot disease significantly impacts peanut growth. Timely, effective, and accurate monitoring of leaf spot severity is crucial for high-yield and high-quality peanut production. Hyperspectral technology from unmanned aerial vehicles (UAVs) is widely employed for disease detection in agricultural fields, but the low spatial resolution of imagery affects accuracy. In this study, peanuts with varying levels of leaf spot disease were detected using hyperspectral images from UAVs. Spectral features of crops and backgrounds were extracted using simple linear iterative clustering (SLIC), the homogeneity index, and k-means clustering. Abundance estimation was conducted using fully constrained least squares based on a distance strategy (D-FCLS), and crop regions were extracted through threshold segmentation. Disease severity was determined based on the average spectral reflectance of crop regions, utilizing classifiers such as XGBoost, the MLP, and the GA-SVM. Results indicate that crop spectra extracted using the superpixel-based unmixing method effectively captured spectral variability, leading to more accurate disease detection. By optimizing threshold values, a better balance between completeness and the internal variability of crop regions was achieved, allowing for the precise extraction of crop regions. Compared to other unmixing methods and manual visual interpretation techniques, the proposed method achieved excellent results, with an overall accuracy of 89.08% and a Kappa coefficient of 85.42% for the GA-SVM classifier. This method provides an objective, efficient, and accurate solution for detecting peanut leaf spot disease, offering technical support for field management with promising practical applications. Full article

Peanut (Arachis hypogea L.) crops in the southeastern U.S. suffer significant yield losses from diseases like leaf spot, southern blight, and stem rot. Traditionally, growers use conventional boom sprayers, which often leads to overuse and wastage of agrochemicals. However, advances in computer technologies have enabled the development of precision or variable-rate sprayers, both ground-based and drone-based, that apply agrochemicals more accurately. Historically, crop disease scouting has been labor-intensive and costly. Recent innovations in computer vision, artificial intelligence (AI), and remote sensing have transformed disease identification and scouting, making the process more efficient and economical. Over the past decade, numerous studies have focused on developing technologies for peanut disease scouting and sprayer technology. The current research trend shows significant advancements in precision spraying technologies, facilitating smart spraying capabilities. These advancements include the use of various platforms, such as ground-based and unmanned aerial vehicle (UAV)-based systems, equipped with sensors like RGB (red–blue–green), multispectral, thermal, hyperspectral, light detection and ranging (LiDAR), and other innovative detection technologies, as highlighted in this review. However, despite the availability of some commercial precision sprayers, their effectiveness is limited in managing certain peanut diseases, such as white mold, because the disease affects the roots, and the chemicals often remain in the canopy, failing to reach the soil where treatment is needed. The review concludes that further advances are necessary to develop more precise sprayers that can meet the needs of large-scale farmers and significantly enhance production outcomes. Overall, this review paper aims to provide a review of smart spraying techniques, estimating the required agrochemicals and applying them precisely in peanut fields. Full article

Peanut early leaf spot (ELS), caused by Cercospora arachidicola, is a major global threat to peanut production, leading to substantial economic losses. The development of ELS is closely linked to favorable climatic conditions. This study aimed to develop a predictive model, optimized using the Biomod tuning function, to assess the future risk and spatial distribution of ELS under various climate change scenarios. Our results suggest a northward expansion of suitable habitats for C. arachidicola driven by global warming, particularly under the SSP585-2050s and SSP585-2090s scenarios. Regions such as Shandong, Henan, and Shaanxi in northern China are predicted to become increasingly suitable for the pathogen, extending beyond traditional warm and humid zones. Climate-induced shifts in ecological niches were quantified, revealing significant changes in the pathogen’s distribution, with a reduction in niche overlap under future climatic conditions. Principal component analysis identified the bioclimatic variables bio5, bio6, and bio8 as key drivers of the pathogen’s niche shift. The first two principal components explained 71.82–75.02% of the variance in environmental factors. These findings provide crucial insights for proactive disease management and underscore the profound impact of climate change on ELS distribution, highlighting the necessity of adaptive strategies to mitigate its effects on agricultural systems. This model can also directly provide migration predictions for pathogenic bacteria for farmers and government departments, and make a great contribution to reducing disease losses. Full article

The intelligent transformation of crop leaf disease detection has driven the use of deep neural network algorithms to develop more accurate disease detection models. In resource-constrained environments, the deployment of crop leaf disease detection models on the cloud introduces challenges such as communication latency and privacy concerns. Edge AI devices offer lower communication latency and enhanced scalability. To achieve the efficient deployment of crop leaf disease detection models on edge AI devices, a dataset of 700 images depicting peanut leaf spot, scorch spot, and rust diseases was collected. The YOLOX-Tiny network was utilized to conduct deployment experiments with the peanut leaf disease detection model on the Jetson Nano B01. The experiments initially focused on three aspects of efficient deployment optimization: the fusion of rectified linear unit (ReLU) and convolution operations, the integration of Efficient Non-Maximum Suppression for TensorRT (EfficientNMS_TRT) to accelerate post-processing within the TensorRT model, and the conversion of model formats from number of samples, channels, height, width (NCHW) to number of samples, height, width, and channels (NHWC) in the TensorFlow Lite model. Additionally, experiments were conducted to compare the memory usage, power consumption, and inference latency between the two inference frameworks, as well as to evaluate the real-time video detection performance using DeepStream. The results demonstrate that the fusion of ReLU activation functions with convolution operations reduced the inference latency by 55.5% compared to the use of the Sigmoid linear unit (SiLU) activation alone. In the TensorRT model, the integration of the EfficientNMS_TRT module accelerated post-processing, leading to a reduction in the inference latency of 19.6% and an increase in the frames per second (FPS) of 20.4%. In the TensorFlow Lite model, conversion to the NHWC format decreased the model conversion time by 88.7% and reduced the inference latency by 32.3%. These three efficient deployment optimization methods effectively decreased the inference latency and enhanced the inference efficiency. Moreover, a comparison between the two frameworks revealed that TensorFlow Lite exhibited memory usage reductions of 15% to 20% and power consumption decreases of 15% to 25% compared to TensorRT. Additionally, TensorRT achieved inference latency reductions of 53.2% to 55.2% relative to TensorFlow Lite. Consequently, TensorRT is deemed suitable for tasks requiring strong real-time performance and low latency, whereas TensorFlow Lite is more appropriate for scenarios with constrained memory and power resources. Additionally, the integration of DeepStream and EfficientNMS_TRT was found to optimize memory and power utilization, thereby enhancing the speed of real-time video detection. A detection rate of 28.7 FPS was achieved at a resolution of 1280 × 720. These experiments validate the feasibility and advantages of deploying crop leaf disease detection models on edge AI devices. Full article

Subtilases (SBTs), known as serine proteases or phytoproteases in plants, are crucial enzymes involved in plant development, growth, and signaling pathways. Despite their recognized importance in other plant species, information regarding their functional roles in cultivated peanut (Arachis hypogea L.) remains sparse. We identified 122 AhSBT genes in the STQ peanut genome, classifying them into six subgroups based on phylogenetic analysis. Detailed structural and motif analyses revealed the presence of conserved domains, highlighting the evolutionary conservation of AhSBTs. The collinearity results indicate that the A. hypogea SBT gene family has 17, 5, and 1 homologous gene pairs with Glycine max, Arabidopsis thaliana, and Zea mays, respectively. Furthermore, the prediction of cis-elements in promoters indicates that they are mainly associated with hormones and abiotic stress. GO and KEGG analyses showed that many AhSBTs are important in stress response. Based on transcriptome datasets, some genes, such as AhSBT2, AhSBT18, AhSBT19, AhSBT60, AhSBT102, AhSBT5, AhSBT111, and AhSBT113, showed remarkably higher expression in diverse tissues/organs, i.e., embryo, root, and leaf, potentially implicating them in seed development. Likewise, only a few genes, including AhSBT1, AhSBT39, AhSBT53, AhSBT92, and AhSBT115, were upregulated under abiotic stress (drought and cold) and phytohormone (ethylene, abscisic acid, paclobutrazol, brassinolide, and salicylic acid) treatments. Upon inoculation with Ralstonia solanacearum, the expression levels of AhSBT39, AhSBT50, AhSBT92, and AhSBT115 were upregulated in disease-resistant and downregulated in disease-susceptible varieties. qRT-PCR-based expression profiling presented the parallel expression trends as generated from transcriptome datasets. The comprehensive dataset generated in the study provides valuable insights into understanding the functional roles of AhSBTs, paving the way for potential applications in crop improvement. These findings deepen our understanding of peanut molecular biology and offer new strategies for enhancing stress tolerance and other agronomically important traits. Full article

Peanut web blotch is an important leaf disease caused by Phoma arachidicola, which seriously affects the quality and yield of peanuts. However, the molecular mechanisms of peanut resistance to peanut web blotch are not well understood. In this study, a transcriptome analysis of the interaction between peanut (Arachis hypogaea) and P. arachidicola revealed that total 2989 (779 up- and 2210 down-regulated) genes were all differentially expressed in peanut leaves infected by P. arachidicola at 7, 14, 21 days post inoculation. The pathways that were strongly differentially expressed were the flavone or isoflavone biosynthesis pathways. In addition, two 2-hydroxy isoflavanone synthase genes, IFS1 and IFS2, were strongly induced by P. arachidicola infection. Overexpression of the two genes enhanced resistance to Phytophthora parasitica in Nicotiana benthamiana. Knockout of AhIFS genes in peanut reduced disease resistance to P. arachidicola. These findings demonstrated that AhIFS genes play key roles in peanut resistance to P. arachidicola infection. Promoter analysis of the two AhIFS genes showed several defense-related cis-elements distributed in the promoter region. This study improves our understanding of the molecular mechanisms behind resistance of peanut infection by P. arachidicola, and provides important information that could be used to undertake greater detailed characterization of web blotch resistance genes in peanut. Full article

The economic return for peanut (Arachis hypogaea L.) in Ghana is often low due to limitations in the availability of inputs or their adoption, which are needed to optimize yield. Six experiments were conducted in Ghana in 2020 and 2021 to determine the impact of planting date, cultivar, fertilization, pest management practices, and harvest date on peanut yield, financial return, and pest reaction. A wide range of interactions among these treatment factors were often observed for infestations of aphids (Aphis gossypii Glover); groundnut rosette disease (Umbravirus: Tombusviridaee); millipedes (Peridontopyge spp.); white grubs (Schyzonicha spp.); wireworms (Conoderus spp.); termites (Microtermes and Odontotermes spp.); canopy defoliation as a result of early leaf spot disease caused by Passalora arachidicola (Hori) and late leaf spot caused by Nothopassalora personata (Berk. and M. A. Curtis); and the scarification and boring of pods caused by arthropod feeding. Pod yield and economic return increased for the cultivar Chitaochi and Sarinut 2 when fertilizer was applied and when fertilizer was applied at early, mid-, and late planting dates. Pod yield and economic return increased when a combination of locally derived potassium soaps was used for aphid suppression and one additional hand weeding was used in the improved pest management practice compared with the traditional practice without these inputs. Pearson correlations for yield and economic return were negatively correlated for all pests and damage caused by pests. The results from these experiments can be used by farmers and their advisors to develop production packages for peanut production in Ghana. Full article

Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R²: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively. Full article

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper’s improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately. Full article

Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction method based on an improved long short-term memory (LSTM) model and multi-year meteorological data combined with disease survey records. Our method employed a combination of convolutional neural networks (CNNs) and LSTMs to capture spatial–temporal patterns from the data and improve the model’s ability to recognize dynamic features of the disease. In addition, we introduced a Squeeze-and-Excitation (SE) Network attention mechanism module to enhance model performance by focusing on key features. Through several hyper-parameter optimization adjustments, we identified a peanut leaf spot disease condition index prediction model with a learning rate of 0.001, a number of cycles (Epoch) of 800, and an optimizer of Adma. The results showed that the integrated model demonstrated excellent prediction ability, obtaining an RMSE of 0.063 and an $R^2$ of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the $R^2$ by 0.155 and 0.122, respectively, compared to the single CNN and LSTM. Predicting the occurrence and severity of peanut leaf spot disease based on the meteorological conditions and neural networks is feasible and valuable to help growers make accurate management decisions and reduce disease impacts through optimal fungicide application timing. Full article

Here, we review the research undertaken since the 1950s in Australia’s grain cropping regions on seed-borne virus diseases of cool-season pulses caused by alfalfa mosaic virus (AMV) and cucumber mosaic virus (CMV). We present brief background information about the continent’s pulse industry, virus epidemiology, management principles and future threats to virus disease management. We then take a historical approach towards all past investigations with these two seed-borne pulse viruses in the principal cool-season pulse crops grown: chickpea, faba bean, field pea, lentil, narrow-leafed lupin and white lupin. With each pathosystem, the main focus is on its biology, epidemiology and management, placing particular emphasis on describing field and glasshouse experimentation that enabled the development of effective phytosanitary, cultural and host resistance control strategies. Past Australian cool-season pulse investigations with AMV and CMV in the less commonly grown species (vetches, narbon bean, fenugreek, yellow and pearl lupin, grass pea and other Lathyrus species) and those with the five less important seed-borne pulse viruses also found (broad bean stain virus, broad bean true mosaic virus, broad bean wilt virus, cowpea mild mottle virus and peanut mottle virus) are also summarized. The need for future research is emphasized, and recommendations are made regarding what is required. Full article

Peanut southern blight has a severe impact on peanut production and is one of the most devastating soil-borne fungal diseases. We conducted a hyperspectral analysis of the spectral responses of plants to peanut southern blight to provide theoretical support for detecting the severity of the disease via remote sensing. In this study, we collected leaf-level spectral data during the winter of 2021 and the spring of 2022 in a greenhouse laboratory. We explored the spectral response mechanisms of diseased peanut leaves and developed a method for assessing the severity of peanut southern blight disease by comparing the continuous wavelet transform (CWT) with traditional spectral indices and incorporating machine learning techniques. The results showed that the SVM model performed best and was able to effectively detect the severity of peanut southern blight when using CWT (WF_770~780, 5) as an input feature. The overall accuracy (OA) of the modeling dataset was 91.8% and the kappa coefficient was 0.88. For the validation dataset, the OA was 90.5% and the kappa coefficient was 0.87. These findings highlight the potential of this CWT-based method for accurately assessing the severity of peanut southern blight. Full article