Search Results (129)

The phasing out of most chemicals has created a demand for alternative methods to preserve grain quality and market value. Hermetic storage offers a chemical-free solution for pest control by creating an airtight environment that naturally leads to insect death. Adding oxygen scavengers can further enhance hermetic storage by accelerating oxygen depletion. However, no study has examined scaling hand warmers in hermetic storage bags used by large grain handlers and farmers. We evaluated the effects of 1, 2, or 3 hand warmers in 25-kg PICS bags and 2, 4, or 6 hand warmers in 50-kg PICS bags on oxygen consumption and grain quality. We hypothesized that doubling the number of hand warmers used in 25-kg to 50-kg PICS bags would maintain the same rate of oxygen reduction. Oxygen levels decreased as the number of hand warmers increased. Additionally, oxygen concentrations in 25-kg PICS bags with 1, 2, or 3 hand warmers closely mirrored those of 2, 4, or 6 hand warmers in 50-kg PICS bags, respectively. Using 2 or 3 hand warmers in 25-kg PICS bags and 4 or 6 hand warmers in 50-kg PICS bags reduced oxygen concentrations below the 5% threshold for pest suppression within 12 h and maintained it for at least 8 days. While a slight rise in relative humidity was observed with more hand warmers, this did not negatively affect seed moisture content or germination rates. Doubling hand warmers along with the bag size from 25 to 50 kg produced similar oxygen depletion rates. These findings are helpful for large grain handlers and farmers who use 50-kg hermetic bags to store seeds or specialty crops to maintain quality. Hermetic bags combined with hand warmers promote sustainability by reducing chemical usage and minimizing food and nutrient losses. Full article

Maize (Zea mays L.) is one of the most important agricultural crops in the world; however, its production is often threatened by several harmful insects, one of which is the European corn borer, Ostrinia nubilalis (Hübner). This study aimed to examine the efficacy of several biological control methods against this pest. A randomized block design was used in the study, which included three treatments: parasitic wasps (Trichogramma brassicae), common green lacewings (Chrysoperla carnea), a combination of both agents and a control. The results showed that the treatment with T. brassicae wasps, as well as their combination with C. carnea predators, significantly improved the maize grain yield over the control treatment. The same combined treatment significantly improved the yield over treatments with only Trichogramma and only C. carnea predators, suggesting a higher efficacy of the combination of both agents in improving grain yield. Fewer tunnels and larvae in stalks, compared to other treatments, including the control, were also observed in this treatment. The reduced number of tunnels and larvae in stalks directly enabled plants to allocate more resources into grain development, which contributed to improved grain yield. In light of the growing focus on minimizing insecticide applications to mitigate environmental impacts, a combination of parasitic wasp and lacewings predator could be a suitable biological alternative to the use of chemical insecticides. Full article

Aphids are significant pests of winter wheat, causing damage by feeding on plant sap and reducing crop yield and quality. This study evaluates the potential of hyperspectral remote sensing (350–2500 nm) and machine learning (ML) models for classifying healthy and aphid-infested wheat canopies. Field-based hyperspectral measurements were conducted at three growth stages—T1 (stem elongation–heading), T2 (flowering), and T3 (milky grain development)—with infestation levels categorized according to established economic thresholds (ET) for each growth stage. Spectral data were analyzed using Uniform Manifold Approximation and Projection (UMAP); vegetation indices; and ML classification models, including Logistic Regression (LR), k-Nearest Neighbors (KNNs), Support vector machines (SVMs), Random Forest (RF), and Light Gradient Boosting Machine (LGBM). The classification models achieved high performance, with F1-scores ranging from 0.88 to 0.99, and SVM and RF consistently outperforming other models across all input datasets. The best classification results were obtained at T2 with an F1-score of 0.98, while models trained on the full spectrum dataset showed the highest overall accuracy. Among vegetation indices, the Modified Triangular Vegetation Index, MTVI (r_pb = −0.77 to −0.82), and Triangular Vegetation Index, TVI (r_pb = −0.66 to −0.75), demonstrated the strongest correlations with canopy condition. These findings underscore the utility of canopy spectra and vegetation indices for detecting aphid infestations above ET levels, allowing for a clear classification of wheat fields into “treatment required” and “no treatment required” categories. This approach provides a precise and timely decision making tool for insecticide application, contributing to sustainable pest management by enabling targeted interventions, reducing unnecessary pesticide use, and supporting effective crop protection practices. Full article

The cowpea is a vital crop for low-resource farmers in the Sahel, but post-harvest losses due to insect pests remain a major challenge. Callosobruchus maculatus (Fabricius, 1775), is the primary pest responsible for most of the damage to stored cowpeas. Recently, Trogoderma granarium (Everts, 1898) was found infesting cowpeas in large warehouses in Niger. This study evaluated hermetic storage bags to manage both common and emerging insect pests. Treatments included (i) the Purdue Improved Crop Storage (PICS) hermetic bag; (ii) a woven polypropylene (PP) bag with a polyethylene (PE) liner and Phostoxin; and (iii) a woven PP bag without Phostoxin (control). Naturally infested cowpea grains were obtained from the Office des Produits Vivriers du Niger (OPVN) warehouse in Maradi, Niger. Infestation levels were assessed using 12 samples of 500 g each, randomly collected from each treatment at the start and end of the trial. Major pests identified were C. maculatus, T. granarium, and Tribolium sp., with initial populations of 0.83, 0.44, and 0.83 adults per 500 g of cowpea, respectively. After six months of storage, pest densities in the control increased significantly: 232-fold for C. maculatus, 7.4-fold for T. granarium, and 2.7-fold for Tribolium sp.; resulting in a 38.5% weight loss. In contrast, both the Phostoxin and the PICS hermetic bags effectively suppressed pest populations, preventing weight loss. This study confirms the efficacy of hermetic storage, such as the PICS bag, in protecting cowpeas from both common and emerging pests. Full article

Wheat plants encounter both biotic and abiotic pressure in their surroundings. Among the biotic stress factors, the Russian wheat aphid (RWA: Diuraphis noxia Kurdjumov) decreases grain yield and quality. The current RWA control strategies, including resistance breeding and the application of aphicides, are outpaced and potentially environmentally harmful. Alternatively, priming can stimulate defence responses to RWA infestation. This study investigated the priming potential of two priming agents, avirulent Puccinia triticina (Pt) isolates and salicylic acid (SA), against RWA infestation. The priming effect of Pt isolates and SA in reducing RWA-induced leaf damage and increased antioxidant activities is an indication of defence responses. Selected South African wheat cultivars and Lesotho landraces, grown under greenhouse conditions, were inoculated with Pt isolates (UVPt13: avirulent, UVPt26: virulent) and treated with SA at the seedling or booting stages. The leaf damage rating score was used for phenotyping. The antioxidant-mediated defence responses were evaluated in three selected cultivars for further priming investigation. Our results revealed that the priming agents significantly reduced the leaf damage in most cultivars at both growth stages, and UVPt13 and SA priming significantly (p ≤ 0.05) increased superoxide dismutase, peroxidase, and ascorbate peroxidase activities. However, catalase activity exhibited a more pronounced decline in plants treated with the UVPt13 isolate. The Pt isolate priming was more efficient than the SA application. However, it is crucial to investigate the potential of effectors from the avirulent Pt isolate to prime wheat plants for resistance against RWA infestation. This could contribute to developing strategies to enhance crop protection and relieve pest pressure in wheat production. Full article

In the face of global climate change, crop pests and diseases have emerged on a large scale, with diverse species lasting for long periods and exerting wide-ranging impacts. Identifying crop pests and diseases efficiently and accurately is crucial in enhancing crop yields. Nonetheless, the complexity and variety of scenarios render this a challenging task. In this paper, we propose a fine-grained crop disease classification network integrating the efficient triple attention (ETA) module and the AttentionMix data enhancement strategy. The ETA module is capable of capturing channel attention and spatial attention information more effectively, which contributes to enhancing the representational capacity of deep CNNs. Additionally, AttentionMix can effectively address the label misassignment issue in CutMix, a commonly used method for obtaining high-quality data samples. The ETA module and AttentionMix can work together on deep CNNs for greater performance gains. We conducted experiments on our self-constructed crop disease dataset and on the widely used IP102 plant pest and disease classification dataset. The results showed that the network, which combined the ETA module and AttentionMix, could reach an accuracy as high as 98.2% on our crop disease dataset. When it came to the IP102 dataset, this network achieved an accuracy of 78.7% and a recall of 70.2%. In comparison with advanced attention models such as ECANet and Triplet Attention, our proposed model exhibited an average performance improvement of 5.3% and 4.4%, respectively. All of this implies that the proposed method is both practical and applicable for classifying diseases in the majority of crop types. Based on classification results from the proposed network, an install-free WeChat mini program that enables real-time automated crop disease recognition by taking photos with a smartphone camera was developed. This study can provide an accurate and timely diagnosis of crop pests and diseases, thereby providing a solution reference for smart agriculture. Full article

This study investigates the innovative use of natural porous clays from the Bejaad Region in Morocco as a support matrix for the encapsulation and controlled release of lemon essential oil (Citrus limonum, EOCL), a natural compound with well-documented insecticidal properties. The research aims to address the inherent challenges of essential oils, particularly their high volatility and rapid degradation, by improving their stability and insecticidal efficiency against the grain pest Sitophilus granarius. By anchoring EOCL onto clay matrices, this study seeks to achieve a sustained and controlled release of the active components, thereby enhancing their practical application as biopesticides. The clays were comprehensively characterized using advanced analytical techniques, including X-ray diffraction (XRD), X-ray fluorescence (XRF), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy with energy-dispersive X-ray analysis (SEM-EDX), and thermogravimetric analysis (TGA). These techniques revealed the mineralogical composition, thermal properties, and morphology of the clays, demonstrating their suitability for effectively adsorbing and retaining EOCL. The insecticidal performance of the clay/EOCL composites was thoroughly tested under controlled conditions, revealing a marked improvement in efficacy, with significantly lower lethal doses required to achieve high mortality rates in Sitophilus granarius. The diffusion of EOCL through the clay matrix was modeled using Fick’s law of diffusion, and the results were further refined through statistical optimization to identify key parameters that influence the release and effectiveness of the active compounds. Complementing the experimental approach, a bioinformatics analysis was conducted to explore the molecular interactions between limonene, the primary active component of EOCL, and target proteins in insects. This theoretical investigation provided insights into the potential mechanisms of action, reinforcing the empirical findings. This study concludes that encapsulating EOCL within porous clay matrices not only enhances the stability and controlled release of the oil but also significantly boosts its insecticidal effectiveness. This approach presents a promising, environmentally sustainable strategy for crop protection, integrating material science, theoretical modeling, and bioinformatics to develop more efficient and durable biopesticides. Full article

Herbicides are synthetic chemicals that are extensively employed in agricultural practices with the objective of enhancing crop yield and quality. Despite their selectivity for plant systems and being generally regarded as non-toxic to animals, there is a paucity of understanding surrounding the sublethal effects on non-target organisms, including animals. This gap underscores the necessity for ecotoxicological research that prioritizes the identification of suitable models and develops reliable biomarkers for the early assessment of environmental impact. In this context, hemocytes—circulating immune cells found in invertebrates—have been identified as a crucial system for assessing sublethal toxicological effects, given their role in immune defense and overall organism health. Tenebrio molitor, a beetle pest of stored grain, was used as a model for the assessment of the effects of a metribuzin-based herbicide (MTB, Feinzin DF 70, 70% metribuzin, 0.25 kg ha⁻¹). Following a 96 h exposure to MTB, the males (7–10 days post-eclosion) were examined for multiple biomarkers in their hemocytes, including cell density, phagocytic activity, lysosomal membrane stability, and cytological changes. Although no mortality was observed, exposure to MTB resulted in a reduction in the phagocytic index and an increase in blast-like cells, indicating the potential for immunotoxicity. Lysosomal membrane stability was reduced, though no significant changes in hemocyte density or nuclear morphology were observed. These responses indicate potential immune system impairment, which could affect the beetle’s fitness and reproductive potential. This study highlights the potential of hemocytes for assessing sublethal herbicide effects, raising concerns about the ecological impact of herbicides in agroecosystems and their potential risks to both wildlife and human health. Full article

Cowpea (Vigna unguiculata) is a vital crop in sub-Saharan Africa, but the legume pod borer (LPB), Maruca vitrata, can cause over 80% yield losses. Natural resistance to this lepidopteran pest is absent in cowpea germplasm, and insecticides are ineffective due to the pest’s cryptic behavior. To address this, a genetically modified (GM) cowpea expressing the cry1Ab protein from Bacillus thuringiensis (Bt) was developed, providing complete LPB resistance. This Bt cowpea, commercialized as Sampea 20-T in Nigeria, was recently approved in Ghana as Songotra T. To evaluate its performance and the financial returns of its cultivation, field trials were conducted across multiple locations in northern Ghana to compare it to the non-transgenic Songotra control and two commercial cultivars, Kirkhouse-Benga and Wang-Kae. Songotra T exhibited protection against LPB infestations and damage, achieving a grain yield of 2534 kg/ha compared to 1414–1757 kg/ha for the other entries. As expected, non-LPB pest infestations and damage were similar across all entries. Economic analysis revealed that Songotra T had the highest return on investment (464%), outperforming the other tested cultivars (214%). These results demonstrate the potential of GM crops to enhance yields and profitability for resource-poor farmers, underscoring the value of biotechnology for addressing critical agricultural challenges. Full article

In Uganda, the common bean (Phaseolus vulgaris) is often infested by a complex of insect pests, but bean flies, aphids, bean leaf beetles, and flower thrips are the most important. Whereas yield losses due to these pests have been established, there is limited information on their population dynamics at different stages of crop growth and their effect on yield and yield components. In order to describe the population dynamics of selected common bean pests at various phases of bean crop growth, and their impact on yield and yield components, a study was carried out in Uganda during the 2016 second rains and the 2017 first rains in three agro-ecological zones. Bean flies, bean aphids, bean leaf beetles, whitefly, striped bean weevil, leafhoppers, and caterpillars were the main insects observed. Pesticide spray schedules were imposed to generate different populations of insect pests whose effects on yield and its components were determined. The findings indicate that spray regimes significantly influenced the abundance of bean flies and leafhoppers, but not the other insect pests. Additionally, except for caterpillars, insect pests were significantly influenced by crop growth stages, but only leafhoppers exhibited a significant negative relationship with grain yield. Furthermore, yield and yield components varied significantly between spray regimes, and there was a significant positive relationship between grain yield and yield components. Our study is important for informing growers on the stage of crop growth at which management tactics such as use of insecticides can be applied for different insect pests. Full article

As a crucial grain crop, wheat is vulnerable to pest attacks throughout its growth cycle, leading to reductions in both yield and quality. Therefore, promptly detecting and identifying wheat pests is essential for effective pest management and to guarantee better wheat production and quality. Wheat pests exhibit considerable diversity and are often found in complex environmental contexts. Intraspecies variation among wheat pests can be substantial, while differences between species may be minimal, making accurate pest detection a difficult task. We provide an enhanced algorithm, YOLO-Wheat, based on YOLOv8, to solve the aforementioned issues. The proposed YOLO-Wheat, an extension of YOLOv8, integrates SimAM into the C2f module to enhance feature extraction capabilities. Additionally, a novel feature fusion technique, CGconcat, is introduced, which enhances fusion efficiency by applying channel weighting to emphasize critical feature information. Moreover, the EMA attention mechanism is implemented before the detection head to preserve feature information through multipath processing, thereby addressing detection challenges posed by pests of varying sizes. Experiments revealed that YOLO-Wheat achieved an mAP@0.5 of 89.6%, reflecting a 2.8% increase compared to its prior performance. Additionally, mAP@0.5:0.95 reached 46.5%, marking a 1.7% improvement. YOLO-Wheat also performs better than other popular object detection algorithms (YOLOv5, YOLOv10, RT-DETR), and the model is successfully deployed for simple real-time detection. These results demonstrate that YOLO-Wheat can achieve real-time high-precision detection for wheat pests. Full article

Background: Corn is the main grain crop grown in China, and the ear shape index of corn is an important parameter for breeding new varieties, including ear length, diameter, row number of ears, row number of grains per ear, and so on. Objective: In order to solve the problem of limited field of view associated with computer detection of the corn ear shape index against a complex background, this paper proposes a panoramic splicing method for corn ears against a complex background, which can splice 10 corn ear panoramic images at the same time, to improve information collection efficiency, display comprehensive information, and support data analysis, so as to realize automatic corn seed examination. Methods: A summary of corn ear panoramic stitching methods under complex backgrounds is presented as follows: 1. a perceptual hash algorithm and histogram equalization were used to extract video frames; 2. the U-Net image segmentation model based on transfer learning was used to predict corn labels; 3. a mask preprocessing algorithm was designed; 4. a corn ear splicing positioning algorithm was designed; 5. an algorithm for irregular surface expansion was designed; 6. an image stitching method based on template matching was adopted to assemble the video frames. Results: The experimental results showed that the proposed corn ear panoramic stitching method could effectively solve the problems of virtual stitching, obvious stitching seams, and too-high similarity between multiple images. The success rate of stitching was as high as 100%, and the speed of single-corn-ear panoramic stitching was about 9.4 s, indicating important reference value for corn breeding and disease and insect detection. Discussions: Although the experimental results demonstrated the significant advantages of the panoramic splicing method for corn ear images proposed in this paper in terms of improving information collection efficiency and automating corn assessment, the method still faces certain challenges. Future research will focus on the following points: 1. addressing the issue of environmental interference caused by diseases, pests, and plant nutritional status on the measurement of corn ear parameters in order to enhance the stability and accuracy of the algorithm; 2. expanding the dataset for the U-Net model to include a wider range of corn ears with complex backgrounds, different growth stages, and various environmental conditions to improve the model’s segmentation recognition rate and precision. Recently, our panoramic splicing algorithm has been deployed in practical applications with satisfactory results. We plan to continue optimizing the algorithm and more broadly promote its use in fields such as corn breeding and pest and disease detection in an effort to advance the development of agricultural automation technology. Full article

Carbon dioxide (CO₂) is the most abundant greenhouse gas (GHG) in the atmosphere and the substrate for the photosynthetic fixation of carbohydrates in plants. Increasing GHGs from anthropogenic emissions is warming the Earth’s atmospheric system at an alarming rate and changing its climate, which can affect photosynthesis and other biochemical reactions in crop plants favorably or unfavorably, depending on plant species. For the substrate role in plant carbon reduction reactions, CO₂ concentration ([CO₂]) in air potentially enhances photosynthesis. However, N uptake and availability for protein synthesis can be a potential limiting factor in enhanced biomass synthesis under enriched [CO₂] conditions across species. Legumes are C₃ plants and symbiotic N fixers and are expected to benefit from enhanced [CO₂] in the air. However, the concurrent increase in air temperatures with enhanced [CO₂] demands more detailed investigations on the effects of [CO₂] enhancement on grain legume growth and yield. In this article, we critically reviewed and presented the online literature on growth, phenology, photosynthetic rate, stomatal conductance, productivity, soil health, and insect behavior under elevated [CO₂] and temperature conditions. The review revealed that specific leaf weight, pod weight, and nodule number and weight increased significantly under elevated [CO₂] of up to 750 ppm. Under elevated [CO₂], two mechanisms that were affected were the photosynthesis rate (increased) and stomatal conductivity (decreased), which helped enhance water use efficiency in the C₃ legume plants to achieve higher yields. Exposure of legumes to elevated levels of [CO₂] when water stressed resulted in an increase of 58% in [CO₂] uptake, 73% in transpiration efficiency, and 41% in rubisco carboxylation and decreased stomatal conductance by 15–30%. The elevated [CO₂] enhanced the yields of soybean by 10–101%, peanut by 28–39%, mung bean by 20–28%, chickpea by 26–31%, and pigeon pea by 31–38% over ambient [CO₂]. However, seed nutritional qualities like protein, Zn, and Ca were significantly decreased. Increased soil temperatures stimulate microbial activity, spiking organic matter decomposition rates and nutrient release into the soil system. Elevated temperatures impact insect behavior through higher plant feeding rates, posing an enhanced risk of invasive pest attacks in legumes. However, further investigations on the potential interaction effects of elevated [CO₂] and temperatures and extreme climate events on growth, seed yields and nutritional qualities, soil health, and insect behavior are required to develop climate-resilient management practices through the development of novel genotypes, irrigation technologies, and fertilizer management for sustainable legume production systems. Full article

Oulema melanopus L., 1758 (Coleoptera: Chrysomelidae) is one of the significant pests affecting cereal crops in Europe. Its damage is evident in the destruction of leaves during the spring growing season, leading to substantial impacts on both the quantity and quality of the harvested yields. The study aimed to evaluate the extent of leaf surface damage, changes in chlorophyll content caused by this pest, and the subsequent effects on yield quality. To achieve this, two experimental parcels were established, each subjected to different pesticide treatments during the spring vegetation cycle, but notably, with the difference that one parcel did not receive insecticide applications. The phytosanitary status, yield quantity, and quality parameters of thes parcels were compared. Chlorophyll content in damaged and undamaged plants was monitored in vivo using SPAD measurements, while the extent of leaf surface damage was assessed through image analysis using GIMP software 2.10.32. Harvested grain underwent milling and baking analysis, with milling and baking-quality indicators measured using a NIR grain analyzer. The results revealed that omitting springtime insecticide treatments during the emergence of O. melanopus led to significant reductions in leaf area and yield quality. In untreated parcels, leaf decession followed linear progression, reaching 35–40% within 20 days. This damage correlated with the decline in SPAD index values, indicating a 40–50% reduction in chlorophyll content dependent photosynthetic activity. Consequently, there were substantial decreases in milling and baking qualities, including nearly 30% reductional protein-content indicators and 10% in the Hagberg falling number. In summary, our large-scale field experiments demonstrated that persistent O. melanopus damage in wheat fields significantly reduced both the quantity and quality of yields, particularly protein content. These facts underscore the economic importance of timely pest-control measures to mitigate damage and preserve crop value. Full article

This article describes the results of the studies related to the occurrence of the European corn borer (Ostrinia nubilalis Hbn) pest carried out at the Technical University of Bydgoszcz. The studies concerned the real occurrence of the pest in corn stubble (cultivated variety SY Collosseum). The research issue undertaken is in line with an IPM (integrated pest management) system and is important since the feeding of the European corn borer can lead to large, direct yield losses estimated nationally at an average of up to about 20%, which does not differ significantly from that of world crops. Corn, for the purposes of this study, was harvested for green fodder in September, and for grain in November 2022, using a John Deere X9 1110 harvester. The average stubble height when cut for green fodder was 280 mm, while for grain it was 265 mm. The experimental studies conducted clearly showed that European corn borer larvae colonized as much as 12% of corn samples harvested for grain at an average stubble height of 155 mm, and 19% of corn samples harvested for grain at an average height of 75 mm. The conducted studies also showed that the average diameter of the stems at the height of the residence of the corn borer larvae when harvested for green fodder is 19.80 mm, and 21.80 mm for grain. The studies conducted by the authors showed the randomness of the locations of the pest larvae, which clearly indicates that the generally known and used mechanical methods of its control are not fully effective. Therefore, the authors presented their own design of a machine construction (filed with the patent office of the Republic of Poland) for destroying the European corn borer in a mechanical way without the use of crop protection chemicals. The presented research problem in this paper is of national and global importance considering the fact that corn is grown on an area of nearly 162 million hectares, where we have to deal with the European corn borer pest. In the case of implementing the design of the construction of a machine for destroying the pest, there will be no need to use chemical pesticides, which will significantly contribute to environmental protection. Full article