This field study examined the incidence and population dynamics of cabbage aphid (Brevicoryne brassicae) on six cauliflower cultivars (PSB-1, Pusa Hybrid-1, PBSK, PBSK-25, PusaHimjoyti, and Snowball-16) under four nitrogen fertilization levels (25, 50, 75, and 100 kg/acre). Results showed substantial cultivar-specific responses to nitrogen treatments, with PSB-1 consistently showing the highest aphid populations (up to 55 aphids/plant in 2021 at 25 kg N), while PBSK and Snowball-16 remained relatively resilient across nitrogen levels. Increasing nitrogen from 25 to 100 kg/acre generally suppressed peak aphid densities. Correlation analysis revealed that relative humidity exhibited the strongest positive association with aphid populations (r ranging from 0.453 to 0.681**), with significantly stronger correlations observed in 2021. Rainfall consistently demonstrated strong negative correlations across all cultivar-nitrogen combinations (r = -0.161 to -0.533), indicating a pronounced effect on aphid population growth independent of nitrogen application rates. Temperature relationships were cultivar-specific and variable, with weak correlations ranging from -0.503 to 0.461. These findings indicate that nitrogen fertilization interacts with cultivar phenotype and environmental factors to determine B. brassicae population dynamics, offering practical insights for developing IPM strategies in cauliflower.

Accurate identification of insect pests is crucial for effective agricultural management and prevention of crop losses. Among these pests, tephritid fruit flies significantly impact fruit and vegetable production, leading to economic losses and reduced market quality. Existing insect identification methods largely rely on manual inspection by taxonomists, which is time-consuming, error-prone, and not feasible for real-time applications in field conditions. Moreover, many existing machine learning-based approaches suffer from limited generalizability and dependence on controlled environments, restricting their practical deployment. To address these challenges, this study proposes a Modified Convolutional Neural Network (MCNN)-based approach for automated identification and classification of tephritid fruit fly species. The proposed method integrates image segmentation, feature extraction, and data augmentation techniques to enhance classification performance under varying conditions. A real-world dataset was collected using pheromone traps from multiple agricultural locations in Punjab, India, comprising four major species: Bactrocera dorsalis, Bactrocera zonata, Zeugodacus cucurbitae, and Zeugodacus tau. The MCNN model is trained using optimized hyperparameters, including learning rate, batch size, and optimizer selection, to improve robustness and accuracy. Experimental results demonstrate that the proposed model achieves an accuracy of 90%, along with improved classification capability compared to traditional approaches. The integration of real-field data and enhanced preprocessing techniques makes the proposed system suitable for practical deployment in precision agriculture. This study contributes to the development of an efficient, scalable, and automated insect identification framework that can assist farmers and agricultural experts in timely pest management.

Fruit fly populations were monitored using pheromone traps in cucumber and bitter gourd fields. A total of 3,321 fruit flies were recorded, with Zeugodacus cucurbitae (35.74%) being the most dominant species, followed by Bactrocera zonata (30.68%), Bactrocera dorsalis (26.76%), and Bactrocera correcta (6.80%). Population trends showed emergence at the 10th standard meteorological week (SMW), peaking at the 19th SMW with 42.75 flies per four traps in 2023 and 41.25 in 2024. Diversity indices remained stable, with the Shannon index (H′) at 1.266 in 2023 and 1.265 in 2024. Environmental factors significantly influenced fruit fly populations, with maximum temperature showing a positive correlation, while wind speed had a negative impact. Regression models indicated that weather parameters accounted for 62.50 - 80.40% of the variation in fruit fly populations.

Pollination, a keystone ecological process sustaining most flowering plant communities, is indispensable to human survival, with over 500 cultivated plant species relying on insect pollinators. Solitary bees (Hymenoptera: Apoidea) are critical contributors to this service, requiring specialized foraging, nesting, and habitat resources. Plant diversity strongly correlates with pollinator community composition, underscoring the ecological interdependence of these groups. Within solitary bees, the family Halictidae (~4500 species) plays a disproportionately significant role in global pollination networks. Halictids exhibit remarkable diversity in social organization—ranging from solitary to communal, semi-social, and primitively eusocial behaviors—shaped by floral resource availability, geographic distribution, and climatic factors. The subfamily Halictinae represents the group's greatest diversity, with the tribe Halictini comprising 53.3% of described species. Key pollinator genera such as Lasioglossum (e.g., Lasioglossum marginatum , Lasioglossum leucozonium ) dominate temperate ecosystems. However, population declines in solitary bees have severely disrupted pollination services across wild and cultivated plant systems, exacerbating global concerns over insect biodiversity loss and biomass reduction. These declines threaten foundational ecosystem services, necessitating urgent research to refine species diversity estimates, identify habitat conservation priorities, and implement evidence-based protective policies. This review highlights the need for standardized methodologies to accurately assess global bee diversity and proposes targeted strategies to mitigate conservation challenges for Halictidae and other solitary bee taxa.

A study was conducted to evaluate the effect of nitrogen fertilizer levels on the expression of Cry1Ac and Cry2Ab proteins in Bt cotton cultivars and their translocation into sucking pests and honeydew. Six cultivars (Ankur 3028, Bioseed 6588, NCS 855, RCH 650, RCH 773, and RCH 776) were tested under four nitrogen levels (0, 65, 100, and 130 kg/ ha) at 60 and 120 days after sowing (DAS). ELISA analysis revealed that Cry protein expression significantly increased with higher nitrogen application and declined as the plants aged. At 60 DAS, Cry1Ac content ranged from 2.15 to 4.37 µg/ g fresh weight, with the highest expression in RCH 776 at 130 kg N/ ha. Similarly, Cry2Ab content ranged from 18.78 to 23.33 µg/ g, with RCH 773 and RCH 776 recording the highest values. At 120 DAS, Cry1Ac and Cry2Ab levels declined, ranging from 1.37 to 2.18 µg/ g and 17.37 to 20.32 µg/ g, respectively. Among the cultivars, RCH 776 consistently showed higher Cry protein expression at both stages and nitrogen levels. Trace amounts of Cry1Ac (0.033–0.094 µg/ g) and Cry2Ab (0.931–1.084 µg/ g) were detected in jassid nymphs and whitefly adults, while honeydew samples from all treatments showed negligible levels (<0.009 µg/ g). These findings confirm that nitrogen significantly enhances Bt toxin expression in cotton leaves, particularly at early growth stages, and suggest minimal translocation of the toxin into phloem-feeding insect pests and their excreta.

Studies were conducted to investigate the effect of different levels of nitrogen fertilizer on the incidence of whitefly in six Bt cotton cultivars, namely, Ankur 3028, NCS 855, RCH 776, RCH 650, RCH 773, Bioseed 6588 and one each of American cotton LH2108 and desi cotton cultivar FDK 124 at the Entomological Research Farm, Department of Entomology, PAU, Ludhiana during 2014 and 2015. The study revealed that incidences of whitefly were higher during 2015 than 2014 during the course of study. During both years, significantly higher incidence of whitefly was recorded at higher dose of nitrogen fertilizer (325 kg/ acre) as compared to lower doses (165 and 250 kg/ acre). Among different cultivars, whitefly was significantly higher on Bt cotton cultivars as compared to non-Bt and desi cotton cultivar. Whitefly population was significantly higher on Bt cotton cultivar, Bioseed 6588 (6.10 and 8.84/ 3 leaves) during 2014 and 2015, respectively. However, lower population of whitefly was recorded on the desi cotton cultivar, FDK 124 (3.54 and 2.95/ 3 leaves) during 2014 and 2015, respectively. The correlation coefficient studies with weather parameter revealed that adults showed significant positive correlation with maximum and minimum temperature, evaporation and sunshine hours.

Background: The diverse insect fauna harboring in every corner of the agroecosystem plays a crucial role in ecological balance. The insect pest feeding on various crops which then it is preyed upon by diverse predators establishing an optimum food chain. With respect to the host plant interaction, it is of paramount importance to survey and identify the species prevailing the region and having the potential of causing a havoc to the host plant so as to initiate an effective pest management tactics. This research focuses on the species diversity of insect pest as well as biological control agents in the various crop ecosystem Methods: Sampling of the insects and spider fauna were studied for two years, 2022 and 2023. Samplings were conducted in LPU research farm using three sampling techniques such as yellow sticky trap, sweeping net, pheromone lures. Following sampling of the insects and spiders species diversity was assessed using various indices such as shannon weiner indec, pielous index, simpsons index, margalefs index, Brillouins index and berger parker index Result: 913 insects from 56 species and eight orders-Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera, Odonata, Orthopteraand Thysanoptera-as well as 9 species of spiders were recorded in the current study. Hemiptera, Lepidopteraand Coleoptera were the orders with the highest species abundance, while Hymenoptera was the least. Margalef’s richness index ranged from 0.26-2.6 and 0.21-2.72 respectively, evenness ranged between 0.06-0.20 and 0.08-0.22 and the diversity index for the first and second year, as indicated by the Shannon-Weiner index, varied from 0.11-0.30 and 0.14-0.34, respectively. In order to develop a strategy framework for monitoring insect biodiversity, which depends on a number of factors, it is necessary to analyze and document insect diversity.

The review comprehensively explores factors influencing enzyme activities in insects and their implications in defense against toxicants. It encompasses a diverse array of enzymes involved in metabolizing synthetic chemicals, emphasizing their dynamic regulation. Key factors affecting enzyme activities, such as external and internal influences, are discussed, shedding light on the intricate regulatory mechanisms. The inhibitory effects of various compounds on insect GST activity are thoroughly examined, providing insights into potential avenues for insect control. The implications of insect defenses against toxicants are elucidated, emphasizing the complexity of plant–insect interactions. The review delves into the evolutionary adaptations of insects to plant defense mechanisms, highlighting the role of enzymes like thioglucosidase and myrosinase in detoxifying glucosinolates. The co-evolutionary dynamics between insects and plants, particularly in the Brassicaceae family, are explored, underscoring the intricate biochemical strategies employed by both parties. Additionally, the review addresses the challenges associated with developing pest-resistant crop plants through traditional breeding or genetic engineering. It discusses the need for a nuanced approach, considering the adaptability of insects to various toxicants and the potential drawbacks of repeated exposures. The success of chemical plant defenses, particularly monoterpene synthesis in pine trees, is noted, along with the distinctive biodegradability of plant metabolites. The review provides a thorough examination of the mechanisms underlying insect responses to toxic plant metabolites, offering valuable insights into the dynamic interplay between insects and plants. It suggests potential targets for insect control programs and highlights the importance of understanding the co-evolutionary processes that shape these interactions.

The cultivation of grapevines in India, particularly in the key producing regions of Maharashtra and Karnataka, faces significant threats from stem borers, primarily Celosterna scabrator and Stromatium barbatum. This review comprehensively examines the impact of these pests on grapevine yields, detailing their biology, lifecycle, and the complexities involved in their management. It highlights how the behaviour of these pests, such as pupating inside the stems, complicates conventional control methods, making them less effective. The review also explores the role of forest trees as alternate hosts, suggesting that changes in land use, such as deforestation, exacerbate the vulnerability of grapevines to these pests. With S. barbatum noted for its extensive host range and distribution across various regions, the review underscores the importance of thoroughly understanding pest biology and behaviour in developing effective pest management strategies. It advocates for integrated pest management (IPM) approaches that combine biological control, habitat management, and the judicious use of pesticides. By focusing on the specific challenges posed by C. scabrator and S. barbatum, this review contributes to the broader discourse on sustainable pest management in grapevine cultivation, aiming to mitigate yield losses and ensure the viability of this important agricultural sector in India.

Plant disease detection is an important part of the agricultural section owing to the natural phenomena of occurrence of plant disease identification for timely management. If this area is not adequately cared for, it might have an important effect on the plant's productivity, product quality, and quantity. The leaf is an essential part of plants for rapid growth and increased crop yield. Crop diseases infected on leaves can reduce the yield and quality of the product. Farmers are facing difficulties in identifying diseases in plant leaves, fruits, and any other parts of the plants. Early crop health and disease detection can aid in suppressing disease infection and dissemination by implementing proper management practices. In this paper, we described many algorithms which are used for identifying and classifying plant diseases through image processing, machine learning, and deep learning. A number of collections of papers and standards provide important information to agricultural researchers and farmers.

Lasioglossum marginatum sp. is the most efficient pollinator of stone fruit crops (such as peach, plum, and cherry). Its proportional contribution to the total visitation is significantly higher than the conventional visitation. This species is polylectic, polyandrous, and an efficient pollinator that is endogeic in nature. Its nesting behavior has been studied earlier. Various nest soil physical characteristics like tumulus length, turret height, nest depth, nest density, nest diameter, nest length, and the height from the plane have been determined across the experimental sites and found to be statistically significantly higher than the normal range. Various parameters were found to be most significant in Budgam (Experiment location-I) located at an altitude of 1700 m above sea level (a. s. l.) higher than Srinagar (Experiment location-II) which is situated at an altitude of 1400 m a. s. l. The current study investigated some of the parameters like the total number of individuals per nest, cell length, cell diameter, cell area, sex ratio per nest, and the weight of pollen mass. Based on the Kolmogorov–Smirnov analysis outcomes, it was found that the species prefers clay loam soil. Further, various soil gravimetrical characteristics such as gravel/sand, slit, and clay in soils at different locations were significantly higher than the normal range. In addition to these, the Chi-square results regarding various nest soil chemical characteristics like the hydrogen ion concentration (pH), electric conductivity (dSm−1), organic matter (

The agricultural sector is the source of production of vegetables and fruits and largely contributes to a country's economy. Several techniques are being used throughout the world for boosting agricultural productivity which has a major influence on global economic development. Even though such techniques expand agricultural crop production, still there are different challenges faced by the farmers in the production process. The crop damage, poor productivity, soil management, water management, weather forecasting, and insect-pest attacks are illustrations of such kinds of problems. The modern agriculture practices have been observed to be capable to address the aforementioned issues. The farmers can be trained to incorporate Information and Communication Technology (ICT) with modern farming practices. The low agricultural yields are also a result of uncertain climatic changes, insufficient irrigation facilities, decreasing soil fertility, and outdated farming methods. Recently, machine learning is the latest evolving technology that can be used for resolving agricultural issues, thereby fostering agricultural development. This paper explores how machine learning can play a significant role in addressing agricultural problems.

The data on number of fruit flies trapped per week for a period of 7 weeks were taken at cucumber field var., Pusa Sanyog. It can be inferred from the data that irrespective of container, the mean number of trapped fruit flies was found to be significantly more in lure stored for 10 days (259.25 flies/trap/7 weeks) and was at par those stored for 15 days (184.75 flies/trap/7 weeks). Overall mean numbers of fruit flies trapped at other storage periods were found at par. The trap catch of males in parapheromone traps is affected by a large number of factors including the abiotic factors of the environment which indirectly alter the target insect i.e., fruit fly and behavior of the target pest. The maximum trap catch at 10 days storage period could be ascribed due to peak activity of the pest during this period. The critical examination of data indicated a consistent trapping between 127.5–142.5 flies/trap/7 weeks at 5, 20, 25 and 30 days storage period. Efficacy on different insecticide revealed that wooden pheromones lures containing spinosad as insecticide resulted in attracting maximum fruit fly population (88.65 flies/trap/weeks) followed by malathion (28.32 flies/trap/weeks) and Lambdacyhalothrin (8.67 flies/trap/weeks).