Soybean gall midge, Resseliella maxima Gagné, was first described in 2019 following widespread outbreaks in soybean across the midwestern United States. Larval feeding inside stems causes lesions, lodging, and yield loss. Third instars overwinter in the soil within silken cocoons, but their cold tolerance, an important factor for survival in the temperate midwestern United States, is unknown. In 2022 and 2023, late-season soybean stems with R. maxima were collected from southwestern Minnesota. Third instars were retrieved and transferred to vials of sand, where they successfully burrowed and formed cocoons. Vials with cocoons were stored at 13 and 3°C, and larval supercooling points and lower lethal temperatures (LTs) were measured after 1 and 2 months. All individuals remained larvae by the time of measurements, suggesting they were in diapause. Supercooling points and lower LTs were primarily between -20 and -25°C. Alignment between these measurements suggests that R. maxima is freeze-intolerant. There were no consistent effects of storage time or temperature, indicating a lack of further acclimation. The temperature causing 50% mortality (LT50) was -19.8°C in both 2022 (95% CI: -26.4 to -13.2°C) and 2023 (95% CI: -26.6 to -13.0°C). Historical soil temperature data reveal the coldest temperature recorded from 2015 to 2024 near the northern portion of R. maxima's known range (-17.7°C) would cause <50% mortality to R. maxima populations, while average coldest temperatures were substantially warmer (-3.1 to -8.5°C). Short-term exposures to winter soil temperatures may not cause significant mortality to overwintering R. maxima in the midwestern United States.

Single-compound floral signaling stabilizes cooperation in an obligate brood-site pollination mutualism.

Pollination: One chemical cue underpins an obligate brood-site mutualism.

Gall-inducing insects modify plant metabolism to convert host tissue cells into new forms of galls. The black locust gall midge Oblongoides robiniae (Haldeman) forms marginal rolling gall on black locust leaves (Robinia pseudoacacia L.). We investigated the hormonal changes in host plants after infection and gall growth to senescence. A wide range of phytohormones was analysed for the first time in gall tissue. Hierarchical cluster analysis demonstrated that young gall (YGall) composition corresponds with non-galled leaflets control (NGLC) and non-galled leaflets of galled leaves (NGLG). A second cluster was formed by mature gall (MGall) and senescent gall (SGall). Through all stages of gall formation the level of hormones increased ABA, GA3, auxins (IAA and PAA), different types of brassinosteroids, 7-oxalactone (BL, EBL, and HBL), 6-oxo type (CS, ECS, TY, and CT), and 6-deoxo type (6dTY) as well as different types of cytokinins, such as free bases (cZ, tZ, DHZ, and iP), N-glucosides (tZ7G, tZ9G, DHZ7G, DHZ9G, and iP7G), O-glucosides (cZOG, cZROG, tZOG, tZROG, DHZOG, and DHZOGR), and ribosides (cZR, tZR, iPR, and DHZR), and salicylic acid (SA). There was no reduction in the examined hormones. The senescence galls present the highest fluctuation and we also noticed changes in NGLC and NGLG. We noted the highest levels of HBL and DHZOG and the lowest contents of ABA, auxins, brassinosteroids, cytokinins, GA3, and SA. MGall and SGall presented changes in opposite directions. The level of cytokinins in gall tissue was higher than NGLC and NGLG, indicating that infection modulates the levels of these phytohormones in whole compound leaves. DHZOG, tZROG level increased in NGLG variant suggests that they are crucial after infection. Brassinosteroids (HBL) level decreased during YGall formation according to NGLC. Results of wide hormone profiling suggest that hormonal changes occur in sequence.

Ten granular and foliar insecticide modules were tested against the major insect pests of rice and their impacts on natural enemies were also noted during 2021 and 2022. Three commonly used granular formulations, namely chlorantraniliprole 0.4 % G, cartap hydrochloride 4 % G and fipronil 0.3 % G, were tested along with three commonly used foliar insecticides: flubendiamide 480 % SC, chlorantraniliprole 18.5 % SC and spinetoram 11.7 % SC. The first application with granular formulations was made 20 days after transplantation (DAT) and the second application with foliar insecticides was made at 50 DAT. Observations on stem borer, leaf folder, gall midge and whorl maggot (WM) incidence were recorded at 30, 45 and 60 DAT. Observations on predatory insects were recorded from the same plots used for evaluating insecticide efficacies. Application of chlorantraniliprole 0.4 % G at 20 DAT and spraying of either flubendiamide 480 % SC or chlorantraniliprole 18.5 % SC at 50 DAT proved highly effective in controlling the stem borer incidence at the vegetative stage as well as at the reproductive stage. The application of chlorantraniliprole, either as a granular or spray, proved highly effective in controlling the leaf folder in rice. Among the three granular formulations tested, fipronil 0.3 % G granules recorded the lowest silver shoots. Chlorantraniliprole, available in both granular and spray formulations, has proven highly effective in controlling stem borers and leaf folders in rice. Fipronil 0.3 % G granules recorded the lowest silver shoot incidence caused by gall midge.

Soybean gall midge, Resseliella maxima Gagné, is a pest of soybean that causes severe yield loss, with no specific management tactics currently being widely implemented. Due to R. maxima forming cocoons and pupating in soil, characterizing its cocooning behavior and studying the effects of artificial burial on adult emergence may reveal possible cultural control tactics like tillage. R. maxima larvae were released and allowed to pupate in vials filled with sand, which were then dissected into cross-sections to identify the depth at which cocoons were formed. R. maxima tended to form cocoons within the first 1 cm of sand when 10 larvae were released, and within the first 1.5 cm of sand when 50 larvae were released. In a second experiment, cocoons were buried at depths up to 3 cm in 0.5 cm increments. When cocoons were buried, adult emergence decreased and was delayed as depth increased, with no adult emergence when cocoons were buried deeper than 1.5 cm. In a third experiment, larvae were buried at depths up to 12 cm in 1 cm increments. When larvae were buried, adult emergence decreased and was delayed as depth increased; however, there was emergence from the deepest tested depth. Upward movement of larvae plateaued and decreased as burial depth increased, with greater burial depths also associated with lower cocooning rates. These findings suggest that burial of cocooned R. maxima can effectively reduce adult emergence, and that tillage should be explored as a potential management tactic to control this pest.

A critical challenge in agricultural automation is the precise detection of mango leaf diseases that compromise crop quality and yield. To address the limitation of existing heavy models in resource-constrained agricultural environments, this study proposes MangoLeafNet-XAI, a novel lightweight deep learning architecture. The model synergistically integrates Efficient Channel Attention (ECA) modules with a DenseNet-121 backbone to adaptively refine features and capture subtle pathological patterns with high precision. The proposed framework was rigorously evaluated using a 5-fold cross-validation and soft-voting ensemble strategy across three public datasets (MLDID, Mango Leaf Disease, and Harumanis). These datasets encompass diverse environmental conditions and distinct disease classes, including Anthracnose, Bacterial Canker, Die Back, Gall Midge, Powdery Mildew, Sooty Mould, and Cutting Weevil. MangoLeafNet-XAI achieved state-of-the-art accuracies of 98.83% on MLDID, 98.09% on the Mango Leaf Disease Dataset, and 98.76% on the Harumanis dataset. A primary contribution of this work is the optimal balance between performance and computational efficiency, utilizing only 6.9 million parameters, making it highly suitable for deployment on edge devices. Moreover, the interpretability of AI methods, such as Grad-CAM and LIME, that are used to explain the rationale behind predictions to offer pathological explanations, also validate the focus on clinically important aspects of the model. The results discuss the key limitations of existing methods, such as computational complexity, inability to interpret the findings, and dataset-dependent overfitting, and demonstrate a high level of resilience and generalizability on diverse datasets. MangoLeafNet-XAI will be a new benchmark of reliable, deployable, as well as accurate disease diagnosis systems, in smart agriculture.

Soybean gall midge, Resseliella maxima Gagné (Diptera: Cecidomyiidae), is a soybean pest in the midwestern United States. Pterostichus melanarius (Illiger) (Coleoptera: Carabidae), an abundant predator in R. maxima-infested fields, feeds on and prefers R. maxima in the laboratory. However, it remains unknown if P. melanarius feeds on R. maxima in the field. We used experiments to assess whether P. melanarius feeds on R. maxima in the field and determine how quickly R. maxima DNA degrades in P. melanarius guts (ie the half-life). Molecular gut content analysis of P. melanarius adults collected from soybean fields in 2021 and 2022 detected R. maxima DNA in P. melanarius guts in both years (3.5% of all P. melanarius in 2021, and 10% in2022). Across pitfall traps deployed on a single sampling date, up to 20% of P. melanarius had recently fed on R. maxima. Pterostichus melanarius predation on R. maxima was density dependent, and the half-life for detection of DNA from a single R. maxima larva in P. melanarius guts was 6.59 h. Overall, results confirm that (i) P. melanarius is a predator of R. maxima in the field, (ii) predation of R. maxima is dependent on R. maxima density, and (iii) field predation estimates are likely an underestimate due to the short detection half-life. These findings indicate P. melanarius may contribute to natural control of R. maxima in the field.

First report of the gall midge genus Macrolabis (Diptera: Cecidomyiidae) on Celastraceae, with description of a new species developing in the flower buds of Euonymus oxyphyllus

Swede midge, Contarinia nasturtii Kieffer (Diptera: Cecidomyiidae), is an invasive pest of canola (Brassica napus Linnaeus, Brassica juncea Linnaeus, Brassica rapa Linnaeus) and other Brassicaceae crops that causes significant damage in eastern North America. Contarinia nasturtii has the potential to invade the Canadian Prairies, which represents North America’s largest canola growing region. This study examined host plant range, female oviposition preference, and larval development of C. nasturtii on selected weed, cultivated, and model Brassicaceae species. We also examined potential host plant resistance using a diverse panel of B. napus lines and developed a novel measure of larval performance using the proportion of third instar larvae as a proxy for larval development. All tested weed species, except Descurainia sophia Linnaeus, supported C. nasturtii development and 5 new host plants (Lepidium densiflorum Schrad., Neslia paniculata (Linnaeus) Desv., Diplotaxis muralis (Linnaeus) DC., Camelina sativa (Linnaeus) Crantz, and Erysimum cheiranthoides Linnaeus) were identified. Notably, we provide the first evidence that Arabidopsis thaliana Linnaeus can be a host for C. nasturtii, establishing a novel model system for gall midge–plant interaction studies. Evaluation of B. napus lines found slight variation in oviposition but no strong resistance, suggesting the need to investigate resistance sources outside of B. napus. These findings expand our knowledge on the host range of C. nasturtii, introduce A. thaliana as a tractable experimental model system, and underscore the need for investigation of host plant resistance and the development of integrated pest management strategies for C. nasturtii to mitigate threats to North American canola production.

Mango (Mangiferaindica) being an economically and nutritionally significant tropical fruit, yet its cultivation is threatened by several foliar diseases such as anthracnose, powdery mildew, bacterial canker and so on. However, this is an automatic identification for mango plant disease and classification has vied an important role within agriculture exploitation digital image process techniques. Basically, traditional detection methods rely on manual inspection, which is labor-intensive, subjective, and often delayed. Advances in deep learning (DL) provide opportunities for automated, accurate, and scalable solutions. This study presents a comparative analysis of deep learning models for mango leaf disease classification using a dataset of 4,000 images across seven classes: healthy, anthracnose, powdery mildew, bacterial canker, gall midge, dieback, cutting weevil, and sooty mold. Four models namely: Custom CNN, LeafNet, AlexNet, and VGG19, were trained and evaluated using accuracy, precision, recall, and F1-score. Results show that Custom CNN and LeafNet achieved the highest performance (99.5% across all metrics), followed by VGG19 (99.0%) and AlexNet (88.0%). The study also introduces a vein-pattern-based segmentation approach that enhances feature localization. The findings highlight the potential of AI-driven frameworks for early mango disease detection, with implications for improving crop management, reducing yield losses, and supporting sustainable agricultural practices.

Mango leaf diseases pose a significant threat to farmers’ productivity in Indonesia due to the difficulty and inaccuracy of manual diagnosis. A mango leaf disease detection system was developed by optimizing the decision threshold for classification using a ResNet50 Convolutional Neural Network (CNN). The Kaggle dataset consisted of 3,979 mango leaf images across eight classes: healthy, anthracnose, bacterial canker, gall midge, cutting weevil, dieback, sooty mold, and powdery mildew. The raw dataset was processed in Roboflow with an 80:10:10 train-validation-test split, and threefold data augmentation on the training set produced a total of 9,600 images. Decision threshold optimization using the precision-recall curve analysis identified 0.85 as the optimal threshold. At this threshold, precision reached 97.03%, while recall was 94.36%. These results provide a critical reference for agricultural applications in Indonesia, particularly considering local characteristics. The model achieved an F1-score of 95.49% after validation on the augmented dataset specifically tailored for tropical conditions.

Unexpected diversity and a wide distribution of paedogenetic gall midges of the genus Heteropeza (Diptera: Cecidomyiidae) in Japan

Mango cultivation is a cornerstone of Thailand's agriculture and economy, but diseases such as anthracnose, algal leaf spot, and gall midge present significant challenges as they can reduce crop yield and quality. In this study, we developed a machine learning-based system to diagnose mango leaf diseases using a dataset of 1,900 images collected from mango orchards in Phitsanulok province. The data underwent preprocessing and augmentation to optimize model training. Five deep learning models—Convolutional Neural Network (CNN), VGG16, DenseNet121, ResNet50, and InceptionV3—were trained and evaluated. Among these, ResNet50 demonstrated the best performance, with an accuracy of 99.8%, a precision of 0.998, a recall of 0.998, and an F1-score of 0.998. Leveraging its superior performance, the ResNet50 model was integrated into a mobile application designed for real-time disease diagnosis. This user-friendly application enables mango farmers to upload images of affected leaves and receive instant disease identification and treatment recommendations. The findings highlight the potential of deep learning models in agricultural applications, offering a reliable and efficient tool for early disease detection and management. By enabling timely intervention, this innovation enhances crop health, reduces losses, and boosts productivity, contributing significantly to sustainable farming practices and improving farmers' livelihoods.

Detection and mapping of gm13, a QTL governing recessive resistance to rice gall midge.

A new genus and species of gall midge (Diptera: Cecidomyiidae) from Populus spp. (Salicaceae) in western North America

ABSTRACT During the study of pollination mutualisms between cecidomyiid flies and Aspidistra flowers in Guangxi, southern China, we discovered a new species of gall midge, the only known pollinator for Aspidistra saxicola. It is described as Resseliella saxicolae sp. nov., based on morphological comparison with known congeners and DNA barcoding sequencing data. The new species is distinguished by dense body setae (except the thorax), uniform flagellomeres in both sexes, longer marginal wing setae, an obvious transverse arcuate scutum (located in the posterior thorax) and curved claws longer than empodia. Its wings lack special spots or stripes, while the dense setae covering and prominent scutum ridge are unique among congeners. Mitochondrial cytochrome oxidase subunit I (COI) gene sequencing indicated that adults and larvae from A. saxicola flowers belong to the same species. Bayesian analysis showed that Resseliella saxicolae and the morphologically similar species R. kadsurae Yukawa, Sato and Xu (2011) were placed in one clade. The gall midge is diurnal and mates at nearby perch sites. In the life cycle of R. saxicolae, females oviposit in flower buds or flowers, with larvae feeding on pollen before pupating in soil. http://www.zoobank.org/urn:lsid:zoobank.org:pub:53EF132A-6929-40EF-BF0C-60E52B3D4697

The gall midge, Gephyraulus lycantha Jiao & Kolesik (Diptera: Cecidomyiidae), causes abnormal enlargement of goji berry, Lycium barbarum L, buds during its larval stage, forming galls and resulting in a significantly decrease in yield in China. Identifying the distribution of the midge in China under current and future climate change scenarios will provide guidance for the scientific prevention and control of this pest. The MaxEnt model was used to predict the current and future potential suitable habitats for the midge in China based on the filtered 56 distribution points and 11 environmental factors, and the ArcGIS software was used to analyze the changes in its suitable region. The results showed that when the parameters were feature combination (FC) = HP and regularization multiplier (RM) = 1, the MaxEnt model was optimal, and the AUC and TSS values were greater than 0.90. The mean temperature of driest quarter (suitable range was -9.36-4.43°C) was the most critical factor influencing the distribution of the midge. Under current climate conditions, the area of suitable habitat for the midge was 112.73 × 104 km2, primarily distributed in Xinjiang (29.03 × 104 km2), Inner Mongolia (26.44 × 104 km2), Gansu (18.36 × 104 km2), Qinghai (10.46 × 104 km2), and Ningxia (3.90 × 104 km2) Provinces. Under the 2050s and 2070s climate scenarios, the area of suitable habitats was larger than current ones (except for SSP126), reaching its maximum under the SSP585 (119.06 × 104 km2) and SSP245 scenarios (135.25 × 104 km2), respectively. In addition, climate warming would cause the suitable habitat of the midge to expand northeastward. Therefore, it is necessary to strengthen monitoring, early warning, and control measures for the pest to ensure the production of goji berry.

Brood pollination mutualisms are obligate interactions in which the specialized insect generally evolves parasitism of its pollinated flower, but whether pollen parasitism could also evolve in nursery pollination systems remains little known. Aspects of pollination, particularly floral phenology and anthesis, as well as feeding habits and life cycles of pollinators, were examined in seven species of Aspidistra (Asparagaceae), in which flowers of most species are cryptic, usually covered by forest litter. The 9-yr field study found that at least six species were pollinated by fungus gnats (Mycetophilidae, Sciaridae) or gall midges (Cecidomyiidae), and their larvae were pollen parasites of the pollinated flowers. As illustrated here, Aspidistra saxicola was exclusively pollinated by a female midge, whose adults fed on pollen and oviposited in flowers, and whose larvae developed in 3-4 d on a diet of the pollen within the corolla. The timing of the midges' life cycle matched the flowering phase of pollen provision for 4-7 d. Unlike previously reported obligate brood pollination mutualisms, in which larvae are seed predators, the sole pollinators, gall midges or fungus gnats, are completely dependent on pollen in multiple species of Aspidistra, illustrating a new fly-pollinated pollen-parasite mutualism in angiosperms.

Exogenous melatonin reduces gall midge infestation in mango leaves by increasing chlorophyll content and phenolics