Crop Insect Research Articles

Insect resistance in Rice (Oryza sativa L.): overview on current breeding interventions

Breeding for insect resistant varieties has been central to the integrated pest management as it offers an ecologically viable approach agianst biotic constraints. Considerable progress has been made in the past to incorporate resistance to insect pests of rice using conventional breeding approaches. However the diversity in insect pest population, continuous selection of virulent biotypes, lack of resistance sources in cultivated rice (Oryza sativa and O. glaberrima) gene pool, want of efficient insect rearing and varietal screening protocols and inherently complex genetics of resistance further necessitates supplementation of conventional breeding techniques with advanced molecular approaches. Hence, alternative approaches like wide hybridization to introgress resistance from other species of Oryza, transgenic approach to deploy Bt cry, snowdrop lectin and other plant derived genes are being actively pursued. The increasingly identified, mapped, cloned and characterized quantitative trait loci and genes related to insect resistance traits in rice have provided a solid foundation for direct selection and varietal improvement through molecular breeding. Utilization of DNA-based markers provided additional impetus to efficiency and precision of conventional plant breeding via marker-assisted selection to successfully introgress several genes for resistance to insect pests of rice. RNA interference technology has offered another reliable tool in meeting the challenges imposed by crop insects by targeting the enzymes/proteins integral to various biological processes of crop insects. This review briefly discusses the current progress and future prospects in molecular breeding for enhanced varietal tolerance to insect pests of rice.

Crop pest classification based on deep convolutional neural network and transfer learning

The growth of most important field crops such as rice, wheat, maize, soybean, and sugarcane are affected due to attack of various pests and the crop production is reduced due to different types of insects. The classification and identification of all types of crop insects correctly is a difficult task for the farmers due to the similar appearance in the earlier stage of crop growth. To address this issue, Convolutional neural network (CNN) with deep architectures is being applied as it performs automatic feature extraction and learns complex high-level features in image classification applications. This study proposed an efficient deep CNN model to classify insect species on three publicly available insect datasets. The National Bureau of Agricultural Insect Resources (NBAIR) dataset used as first insect dataset that consists of 40 classes of field crop insect images, while the second and third dataset (Xie1, Xie2) contains 24 and 40 classes of insects respectively. The proposed model was evaluated and compared with pre-trained deep learning architectures such as AlexNet, ResNet, GoogLeNet and VGGNet for insect classification. Transfer learning was applied to fine-tune the pre-trained models. The data augmentation techniques such as reflection, scaling, rotation, and translation are also applied to prevent the network from overfitting. The effectiveness of hyperparameters was analysed in the proposed model to improve accuracy. The highest classification accuracy of 96.75, 97.47, and 95.97% was achieved in proposed CNN model for NBAIR insect dataset (40 classes), Xie1 (24 classes) insect dataset and Xie2 (40 classes) insect dataset respectively. The results demonstrated that the proposed CNN model is effective in classifying various types of insects in field crops than pre-trained models and can be implemented in the agriculture sector for crop protection.

Occurrence of Entomopathogenic Nematodes (Rhabditida: Steienernematidae, Heterorhabditidae) from Agricultural Ecosystems in Forest (Polissya) and Forest-Steppe Natural Zones of Ukraine

Abstract A survey for entomopathogenic nematodes (EPNs) of the Steienernematidae and Heterorhabditidae in soils of different crop types of agricultural lands (household plots, commercial fields) in the forest (Polissya) and forest-steppe (Lisostep) zones of Ukraine was carried out in spring, summer and autumn months from 2016 to 2018. In total, 205 soil samples and 92 live-traps were processed. In addition, 98 samples of soil-living larvae and adults of insect crop pests, including cockchafer beetles, click beetle larvae, darkling beetle larvae, caterpillars of the scoops were collected. It is shown that the EPNs distribution and frequency of occurrence depends on the natural geographical features (regional characteristics) and habitat types. EPNs were found in 46 (15.4 %) out 297 samples. The incidence (% of samples) of the entomopathogenic nematodes, and their diversity varied depending on the location-based sampling, the type of agrocenosis, and the predominant agricultural plant species or typical species-edificators. The proportion of entomopathogenic nematodes (Steinernema spp., Heterorhabditis spp.) recovery from regions of Lisostep zone was 16.9 %, and 13.6 % from regions of Polissya zone. The highest number among all positive samples was recorded from the apple trees, Malus domestica Borkh., 1803 (10 samples). The least number of positive samples (one sample) was obtained from sweet cherry trees, Prúnus cérasus L., 1753, junipers, Juniperus communis L., 1753, alfalfa, Medicago sativa L., 1753, common beans, Phaseolus vulgaris L., 1753, and common pumpkin, Cucurbita pepo L., 1753. The steinernematid nematodes were noticeably dominant over heterorhabditid nematodes: 60.8 % vs 39.2 % respectively. Steinernema spp. is widespread in different regions and plots, whereas Heterorhabditis spp. are common in fruit orchards and coniferous decorative perennial plantings.

Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence

Specialty crop growers face challenges from numerous diseases and pests. For example, the Asian citrus psyllid (ACP) is a key pest of citrus due to its role as vector of huanglongbing (HLB) (greening disease). There is no known cure for HLB, but vector management is critical, both for slowing spread and attenuating symptoms in infected trees. Therefore, monitoring ACP population, as well as other pest populations, is an essential management component for timing and assessment of control actions. Manual crop scouting is often labor intensive and time consuming. In this work, an automated system was developed and evaluated utilizing machine vision and artificial intelligence to monitor ACP in groves. This system comprised a tapping mechanism to collect insects from the tree’s branches and a board with a grid of cameras for image acquisition. A software was developed using two convolutional neural-networks to accurately detect and distinguish psyllids from other insects and debris fallen from the tree. A GPS was utilized to automatically record individual tree position to facilitate data assessment on large groves. A precision and recall of 80% and 95%, respectively, was obtained on detecting ACPs on a sample of 90 young citrus trees. The system proved a great potential to automate scouting procedures in citrus and to be extended to other crop insects.

Dynamics of predatory and herbivorous insects at the farm scale: the role of cropped and noncropped habitats

AbstractNatural enemy conservation is known to be affected by ecological processes that range from local to landscape scales. At the farm scale, there are cropped and noncropped areas that differ in their management and plant diversity; these differences affect the spatiotemporal dynamics of natural enemies. We investigated how different habitat types can affect the conservation and spatial dynamics of predatory and herbivore insects in organic vegetable crops in Brazil. Insects were simultaneously sampled in two cropped (focal and neighbourhood crops) and two noncropped habitats (fallow and native forests) during five consecutive focal crop cycles. We found a higher species richness of predators and herbivores in noncropped habitats. All of the habitats shared species from both functional groups throughout the year, indicating that species could disperse among habitats. Fallow areas can serve as a source and sink for species migrating to/from cropped habitats where predators and herbivores can numerically increase their populations during the crop cycles. The spatiotemporal dynamics of herbivores and predators depend on the management and maintenance of natural, seminatural and cropped habitats within the farm.

Deciphering host-parasitoid interactions and parasitism rates of crop pests using DNA metabarcoding

An accurate estimation of parasitism rates and diversity of parasitoids of crop insect pests is a prerequisite for exploring processes leading to efficient natural biocontrol. Traditional methods such as rearing have been often limited by taxonomic identification, insect mortality and intensive work, but the advent of high-throughput sequencing (HTS) techniques, such as DNA metabarcoding, is increasingly seen as a reliable and powerful alternative approach. Little has been done to explore the benefits of such an approach for estimating parasitism rates and parasitoid diversity in an agricultural context. In this study, we compared the composition of parasitoid species and parasitism rates between rearing and DNA metabarcoding of host eggs and larvae of the millet head miner, Heliocheilus albipunctella De Joannis (Lepidoptera, Noctuidae), collected from millet fields in Senegal. We first assessed the detection threshold for the main ten endoparasitoids, by sequencing PCR products obtained from artificial dilution gradients of the parasitoid DNAs in the host moth. We then assessed the potential of DNA metabarcoding for diagnosing parasitism rates in samples collected from the field. Under controlled conditions, our results showed that relatively small quantities of parasitoid DNA (0.07 ng) were successfully detected within an eight-fold larger quantity of host DNA. Parasitoid diversity and parasitism rate estimates were always higher for DNA metabarcoding than for host rearing. Furthermore, metabarcoding detected multi-parasitism, cryptic parasitoid species and differences in parasitism rates between two different sampling sites. Metabarcoding shows promise for gaining a clearer understanding of the importance and complexity of host-parasitoid interactions in agro-ecosystems, with a view to improving pest biocontrol strategies.

Natural habitat fragments obscured the distance effect on maintaining the diversity of insect pollinators and crop productivity in tropical agricultural landscapes

In tropical regions, habitat change and fragmentation partly occur due to urbanization. This change of land-use can affect many ecosystem services and their providers, such as pollination and pollinators. Within agricultural systems, monoculture systems and pesticide application are the most detrimental to pollinators and insect communities. In this study, we investigated the effect of distance from natural habitat on the diversity of insect pollinators and cucumber productivity. As the independent variable, distance from natural habitats was classified into two different groups i.e. agricultural area near to (less than 200m) and far from (more than 1km) the natural habitat. We found that the abundance of insect pollinators was significantly lower in agricultural areas near to natural habitats compared to those located far from natural habitats. Cucumber farms located near to natural habitats had 54% similar species composition of insect pollinators with cucumber farms located far from natural habitats. The productivity of cucumbers did not differ between cucumber farms near to and far from natural habitats. An expected result was the positive correlation between pollinator abundance (i.e. Xylocopa spp.) and the productivity of cucumber. Our findings suggest that the diversity of pollinators in tropical agricultural landscape is influenced more by a landscape composition of high natural habitat fragments than spatial distance between cropland and natural habitats.

A new segmentation algorithm based on evolutionary algorithm and bag of words model

Crop disease and insect pest detection and recognition using machine vision can provide precise diagnosis and preventive suggestions. However, the complexity of agricultural pest and disease identification based on traditional bag of words (BOW) models is high and the effect is general. This paper presents a histogram quadric segmentation algorithm based on an evolutionary algorithm to observe the features (colour, texture) of disease spots and to learn from the guided filtering algorithm. This process aims to obtain the precise positions of disease spots in images. Dense-SIFT, which can extract features and spatial pyramid, which can map image features to high-spatial-resolution space, are simultaneously applied in the recognition of crop diseases and insect pests in the BOW model. The experimental results show that the new segmentation algorithm can effectively locate the positions of disease spots in corn images and the improved BOW model substantially increases the recognition accuracy of crop diseases and insect pests.

Wheat Stem Maggot (Diptera: Chloropidae): An Emerging Pest of Cover Crop to Corn Transition Systems

The wheat stem maggot (Meromyza americana Fitch) (WSM) is a minor pest of wheat, rye, and other grasses. In 2017, growers in Nebraska reported dead center whorls and excessive tillering in early-season cornfields that followed wheat or rye terminated after planting corn. A survey was conducted to evaluate the risk factors for this insect in cover crop to corn transition systems. In each field, management practices and the percentage of injured plants were recorded. Symptomatic corn plants were collected from each field and dissected to determine larval and plant characteristics. In a few cases, small patches of a field were planted to a cover crop to manage soil erosion, and injured plants were only found where the cover crop was present. From these observations, the hypothesis is that terminating a cover crop after planting corn allowed the WSM larva to move from the dying cover crop to corn to complete its development. Cornfields infested with WSM had a frequency of injured corn plants from 0 to 60% with yield losses estimated at 30 bushels/acre. This paper provides the first detailed documentation of WSM injury in corn and addresses important management practices that may have influenced this uncommon situation.

Establishing Defoliation Thresholds for Insect Pest of Peanut in Mississippi

ABSTRACT Defoliation of peanut by foliage-feeding insects reduces photosynthetic capacity, and in turn, may reduce pod yield, particularly when canopy loss occurs at critical growth stages, i.e., 40 or 80 d after full plant emergence (DAE). The objective of this research was to determine the impact of peanut defoliation levels of 0, 20, 40, 60, 80, and 100%, at 40 or 80 DAE on canopy height and width, plant biomass, pod grade and yield, and economic injury level. Research was conducted in Stoneville and Starkville MS in 2015 and 2016. The experimental design was a six (defoliation level) by two (defoliation timing) factorial arranged in a randomized complete block. Up to four wk after defoliation, canopy height, canopy width, and plant biomass were negatively correlated with defoliation level regardless of defoliation timing (40 and 80 DAE). Neither defoliation level nor timing had an effect on peanut grade or maturity. Similarly, defoliation at 40 DAE did not affect pod yield but when damage occurred 80 DAE, pod yield was reduced 18.6 kg/ha for every 1% increase in defoliation. Considering average crop value and insect control costs, the economic injury for peanut defoliation at 80 DAE is 5% defoliation. These data indicate that control of canopy-feeding insects is only economically viable when defoliation exceeds 5% defoliation at 80 DAE.

Exploration on Strengthening Treatment Method of Integral Stressed Bridge under Special Load

Based on the characteristics and current situation of crop diseases and insect pests, this paper analyzes the problems of farmers’ lack of awareness of prevention and control, blind introduction of quarantine pests that lead to the spread of quarantine pests, and lack of advanced pest control technology in the prevention and control of crop diseases and pests. , For reference and reference.

PESTICIDES IMPORT, USE, CONSUMPTION AND RESIDUE STATUS AMONG FOOD CROPS IN NEPAL: A REVIEW

Insubstantial options for chemical pest management compelled farmers to use synthetic pesticides against crop insect pests that created vicious circle of increasing high-dose-pesticides applications in agriculture sector. The study was carried out to understand prevailing situation of pesticides uses and their state regulation status. Secondary data were obtained from Plant Protection Division, Plant Management and Registration Division, and FAOSTAT for drawing inferences and trend analysis. Chemical pest management commenced from using Paris green, Gamaxone, Nicotine sulphate and Chlorinated hydrocarbons in 1950s to 117 chemically defined pesticides including bio-pesticides and bio-rational compounds of 2186 formulations from 1960s to 2018 has made obvious changes in pest management scenario in the country. Per hectare pesticide consumption in Nepal is 396.0 gram (a.i.) while in commercial vegetable sector its consumption is as high as 1,600 gram a.i. per hectare. Option dearth but farmers’ reliability only in chemical applications in pest management in crop fields has resulted in repercussion of vicious circle of compelled higher dose pesticide applications in crops. Obvious result is scores of multitudes of pesticides crowded in market in the country. Above all, problems of pesticide residues in food grains and green vegetables are emerging.

Development and Survival of Lygus hesperus (Hemiptera: Miridae) Nymphs Under Constant and Variable Temperatures.

Thermal environments of the arid western United States are often harsh compared with the ranges of temperatures favorable for development and survival of crop insect pests. In cotton [Gossypium spp. (Malvales: Malvaceae)], new irrigation practices such as deficit irrigation may impact populations of pest and beneficial arthropods by temporarily altering temperature profiles within the plant canopy. Most information regarding the temperature-dependent development and survival of an important cotton pest, the western tarnished plant bug (Lygus hesperus Knight), is derived from constant temperature studies. We examined the development and survival of L. hesperus nymphs under constant (±0.2°C) and variable (±8°C) temperature regimes at daily mean temperatures of 15, 22, and 29°C. Under the low temperature (15°C), stadium lengths and duration of the nymphal stage were shorter when temperatures were variable compared with a constant temperature. No differences in development times were observed between regimes at the medium temperature (22°C). Except for the first stadium, development times under the high variable temperature regime were longer compared with the high constant regime (29°C). Nymph survival was unaffected by temperature regime except at the lowest temperature, where daily thermal fluctuations substantially improved survival compared with the constant conditions. These results suggest that temporarily increased crop canopy temperatures caused by altered irrigation schemes are unlikely to substantially reduce the growth of L. hesperus populations. However, enhanced nymphal development and survival under low variable temperatures likely contribute to the survival of overwintering L. hesperus in the absence of acute, low-temperature mortality.

Efficacy of Insecticides Against Spodoptera frugiperda (Smith, 1797)

The Brazil’s economy is supported by agribusiness, however, the continuous cycle of food production and favorable climate contributes to the incidence of pest insects all year round. The fall armyworm Spodoptera frugiperda (Smith, 1797) (Lepidoptera: Noctuidae) is considered the main insect in the corn crop, due to voracity of the caterpillars and occurrence throughout the crop cycle. Therefore, the chemical control has been demanded considerably, causing emergence of populations resistant to the different products, as well as implications in the environment. Thus, studies are needed to evaluate the efficiency of chemical insecticide control according to the susceptibility and the stages of biological development of the pest. The objective of this work was to evaluate the performance of isolated and combined insecticides for the control of S. frugiperda caterpillars under laboratory conditions. In the residual and direct contact bioassays, 8 treatments with 5 replications were used in a completely randomized design, performed with 2nd and 5th instar. The results showed that by residual contact after 72 hours, clofenapir + zeta-cypermethrin treatment had 100% efficacy in the mortality of both instars. When applied via direct contact 72 hours later, the combined treatments showed an efficiency above 80%. However, for an integrated pest management program, where it was recommended the association of different control tactics, the management of insecticides with clofenapir + zeta-cypermethrin was effective with 100% control efficiency in both instars.

Insect Detection and Classification Based on an Improved Convolutional Neural Network.

Regarding the growth of crops, one of the important factors affecting crop yield is insect disasters. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual classification, but this is an extremely time-consuming and expensive process. This work proposes a convolutional neural network model to solve the problem of multi-classification of crop insects. The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features. During the regional proposal stage, the Region Proposal Network is adopted rather than a traditional selective search technique to generate a smaller number of proposal windows, which is especially important for improving prediction accuracy and accelerating computations. Experimental results show that the proposed method achieves a heightened accuracy and is superior to the state-of-the-art traditional insect classification algorithms.

Protease inhibitors: recent advancement in its usage as a potential biocontrol agent for insect pest management.

Plant-derived protease inhibitors (PIs) are a promising defensin for crop improvement and insect pest management. Although agronomist made significant efforts in utilizing PIs for managing insect pests, the potentials of PIs are still obscured. Insect ability to compensate nutrient starvation induced by dietary PI feeding using different strategies, that is, overexpression of PI-sensitive protease, expression of PI-insensitive proteases, degradation of PI, has made this innumerable collection of PIs worthless. A practical challenge for agronomist is to identify potent PI candidates, to limit insect compensatory responses and to elucidate insect compensatory and resistance mechanisms activated upon herbivory. This knowledge could be further efficiently utilized to identify potential targets for RNAi-mediated pest control. These vital genes of insects could be functionally annotated using the advanced gene-editing technique, CRISPR/Cas9. Contemporary research is exploiting different in silico and modern molecular biology techniques to utilize PIs in controlling insect pests efficiently. This review is structured to update recent advancements in this field, along with its chronological background.

Determination of ultra-trace amounts of neonicotinoid insecticide imidacloprid by cyclic and square wave voltammetric methods using pretreated glassy carbon electrode

<p>Pesticide production and application have progressively increased worldwide during recent decades. Among these compounds, imidacloprid (IMD) is a widely used insecticide in agriculture, and more particularly to control sucking insects in crops. The aim of this work is to develop rapid, simple and sufficiently sensitive cyclic voltammetry (CV) and square wave voltammetry (SWV) for the detection and quantification of this insecticide in water and agriculture samples. Pretreated glassy carbon electrode (PGC) was employed as working electrode. Aqueous solutions were prepared with Confidor 200-SL commercial formulation of IMD. Each voltammogram was characterized by a well-defined single irreversible cathodic peak at approximately -1.38 V/SCE. The electrode reaction is mainly a diffusion controlled process. The optimization of experimental parameters such as IMD initial concentration, potential scan rate, pH and temperature was carried out by CV, and then a comparative study was conducted by SWV. For the detection and quantification of IMD, the results obtained by SWV showed higher sensibility and accuracy than that by CV. The developed methods were successfully used to determine IMD in real samples, namely plum and peach juice.</p>

Predator Insect Families Associated With Melon Crop in a Semiarid Region in Brazil

Knowledge of diversity of natural enemies in agroecosystems is vital for the integrated pest management. However, surveys of beneficial insects (predators and parasitoids) in the melon crop (Cucumis melo L.) in the semiarid region in Brazil are scarce. Thus, the objective of this study was to know the families of predator insects associated with melon crop in a semiarid region in the state of Rio Grande do Norte (RN), one of the main melon producers in Brazil. The survey study was carried out in a commercial melon area, in the municipality of Baraúna (RN). The samples were collected weekly during the cycle of melon, using passive (Pitfall and Moericke traps) and active (sweep net) collection methods. A total of 13 families of predator insects were collected. The families with the highest relative abundances in the passive collection method were Labiduridae (89.53%) and Formicidae (6.91%), while in the active collection method were Chrysopidae (59.81%) and Staphylinidae (20.56%). These families contain important species of predator which can promote crop pest suppression in melon agricultural systems.

Phytophagous insects in tamarind crop (Tamarindus indica L.), with emphasis on the greatest damage to the fruit, in five farms from the nearby Western of Antioquia

El tamarindo es un fruto importante para los pequeños productores del occidente cercano antioqueño, en cuanto a que se ofrece en diversas presentaciones a los turistas que visitan esta región. Sin embargo, algunos problemas de calidad, relacionados con la presencia de insectos en este fruto generan dificultades para su comercialización. El objetivo de este trabajo fue determinar los insectos fitófagos de los árboles de tamarindo, con énfasis en los que causan mayor daño al fruto. En cinco fincas de Santa Fe de Antioquia y Sopetrán, se recolectaron los insectos asociados a cada órgano, de seis árboles por finca, se describió su daño y se identificaron hasta el nivel más detallado posible. Tres insectos causantes del mayor daño en el fruto tuvieron prioridad, determinándose su porcentaje de infestación (PI). Para ello, se diseñó una escala de daño y se evaluaron 30 frutos por árbol. Se encontraron once insectos fitófagos asociados al tamarindo, de los cuales cinco registros son nuevos para el tamarindo en Colombia: H. obscurus, Toxoptera aurantii, Trigona sp., Ectomyelois ceratoniae y Acromyrmex octospinosus. Cinco insectos atacan al fruto: Caryedon serratus, dos polillas Phycitinae, Sitophilus linearis e Hypothenemus obscurus. El PI promedio para C. serratus, las dos polillas Phycitinae y S. linearis fue de 19,5%, 8% y 2,5%, respectivamente. Los tres primeros afectan la pulpa y S. linearis ataca a la semilla. El daño más frecuente (43% - 52%) fue de grado 1 y los grados 4 y 5 se presentaron en menores porcentajes (0% - 4%).

Sexual dimorphism in Drosophila melanogaster survival of Beauveria bassiana infection depends on core immune signaling

In many animal species, females and males differ in physiology, lifespan, and immune function. The magnitude and direction of the sexual dimorphism in immune function varies greatly and the genetic and mechanistic bases for this dimorphism are often unknown. Here we show that Drosophila melanogaster females are more likely than males to die from infection with several strains of the fungal entomopathogen Beauveria bassiana. The sexual dimorphism is not exclusively due to barrier defenses and persists when flies are inoculated by injection as well as by surface exposure. Loss of function mutations of Toll pathway genes remove the dimorphism in survivorship. Surprisingly, loss of function mutation of relish, a gene in the Imd pathway, also removes the dimorphism, but the dimorphism persists in flies carrying other Imd pathway mutations. The robust sexual dimorphism in D. melanogaster survival to B. bassiana presents opportunities to further dissect its mechanistic details, with applications for biological control of insect vectors of human disease and insect crop pests.