Crop Pest Management Research Articles

Precision Agriculture Technologies for Early Detection of Crop Pests and Diseases

Crop pests and diseases pose significant challenges to agricultural productivity and food security worldwide. Traditional methods for detecting and managing these threats often rely on manual scouting and blanket pesticide applications, which can be labor-intensive, time-consuming, and environmentally harmful. Precision agriculture technologies offer promising solutions for early detection and targeted management of crop pests and diseases. This review article provides a comprehensive overview of the latest precision agriculture tools and techniques for monitoring crop health, detecting pests and diseases, and guiding site-specific interventions. Key technologies discussed include remote sensing, proximal sensing, machine learning, robotics, and Internet of Things (IoT) sensors. The article highlights the potential of these technologies to improve the timeliness, accuracy, and efficiency of pest and disease detection while reducing reliance on chemical inputs. It also discusses the challenges and opportunities for integrating these technologies into current agricultural practices and extension services. The review concludes with recommendations for future research and development to advance precision agriculture solutions for sustainable crop protection.

An Expert Model Using Deep Learning for Image-based Pest Identification with the TSLM Approach for Enhancing Precision Farming

The rise of digital agriculture has sparked considerable interest in its potential to revolutionize farming practices by integrating of advanced technologies. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a crucial tool in precision agriculture, offering diverse applications for real-world scenarios. This research focuses on harnessing the power of drones in the context of digital agriculture support, particularly in addressing the challenges of crop protection and pest management. The objective is to develop an innovative approach for crop-disease diagnosis using an upgraded Transfer-Driven Self-Adaptive Learning Model (TSLM) that leverages multispectral remote sensing data of drone-captured images to improve pest classification and streamline pesticide application. The study explores nine potent deep neural network models' capabilities for identifying plant diseases using various approaches within the digital agriculture framework. Transfer learning and advanced feature extraction techniques are employed to tailor these deep neural networks to the specific crop protection context. Using of pre-trained deep learning models for feature extraction and fine-tuning enhances the effectiveness of the proposed model. Evaluating the model's performance, precision, sensitivity, specificity, and F1-score metrics are assessed, leading to the discovery that deep feature extraction and Self-Adaptive Learning Model (SLM) classification outperform traditional transfer learning methods. The novelty of this work lies in its application of communication protocols to coordinate a fleet of drones for crop protection within the digital agriculture framework. By combining deep learning techniques with transfer-driven self-adaptive learning, the proposed approach significantly enhances the accuracy and efficiency of pest classification in precision agriculture. The study's findings offer valuable insights into optimizing drone-based technologies to combat plant disease epidemics, thereby contributing to the advancement of digital agriculture and its role in supporting sustainable farming practices.

INVESTIGATION OF COMBINED EFFECT OF THREE PLANT POWDERS WITH DIATOMACEOUS EARTH AGAINST CALLOSOBRUCHUS MACULATUS

The study was conducted to investigate the combined effects of various plant powders and Silico-sec, a diatomaceous earth, on Callosobruchus maculatus at the research laboratory of the Department of Crop Pest and Soil Management, the Federal University of Technology, Akure. The study included three replications. The treatments consisted of three (3) plant materials derived from the dry flower buds of Eugenia aromatica, seeds of Piper guineense, and leaves of Nicotiana tabacum and their combination with Silico-sec. In one experiment, 0.4g of each plant material was added to 20 C. maculatus adults (10 couples, aged 1-2 days) in 20g of Ife Brown cowpea seeds, all contained within an air-tight plastic container. Another experiment involved the addition of 0.4g mixtures of Silico-sec and each plant powder (E. aromatica, P. guineense, and N. tabacum) in various ratios (1:5, 5:1, 1:10, 10:1, and 1:1) to the same number of C. maculatus adults in 20g of cowpea seeds, also in separate air-tight plastic containers. All the treatments and their combination were laid out in a completely randomized design (CRD) repeated three times. Data were collected at 12, 24, and 48 at p,0hours post-treatment. Oviposition, adult emergence, and seed weight loss were evaluated. The results demonstrated that both the plant powders and Silico-sec were effective in controlling C. maculatus infestation on cowpea by causing significant adult mortality at p< 0.05, reducing oviposition, suppressing F1 adult emergence, and minimizing seed damage. Among single applications, Silico-sec and E. aromatica powder provided the best protection for stored cowpea, yielding the highest adult mortality, lowest oviposition, most suppressed F1 adult emergence, and minimal seed damage. However, the mixture of Silico-sec and P. guineense at ratios of 5:1 and 10:1 offered the best protection, exhibiting significantly higher mortality, lower oviposition rates, suppressed F1 adult emergence, and reduced seed damage compared to other treatments. This study suggests that combination of P. guineense and Silico-sec is particularly effective in controlling cowpea storage pests and could serve as a viable alternative to synthetic insecticides for protecting stored cowpea against C. maculatus.

A Framework for Enhancing Adoption of Mobile-based Surveillance for Crop Pest and Disease Management by Farmers in Kenya

Crop pests and diseases are ranked as some of the world’s leading threats to agricultural productivity. Though different digital solutions have been developed in the last decade to monitor and control biosecurity threats, their adoption in developing nations remain sluggish. The need to improve adoption of digital solutions prompted a review on the applicability of emerging digital solutions in large-scale surveillance of crop pest and diseases in developing nations. The study presents findings on key requirements for achieving digitized large-scale pest surveillance, fitness for purpose of common autonomous biosecurity surveillance technologies, and prospects of smartphones as alternative surveillance solution. Firstly, the research identified appropriateness of the solution, availability of supporting infrastructure and level of stakeholder involvement in solution formulation as some of the key determinants of digital solution adoption. Secondly, though most common autonomous biosecurity surveillance technologies are promising, their adoption in developing nations are limited by operational cost, legal requirements, required skillset, and operational environment among others. Thirdly, recent advancements in smartphones and wide spread ownership among farmers provide a unique opportunity for advancing Mobile Crowd-Sensing solutions in achieving large-scale pest surveillance. Lastly, we recommend designing an incentive mechanism to motivate farmers’ participation in MCS based surveillance solution.

New insights on entomopathogenic bacteria isolated from soil of citrus crops to combat the polyphagus aphid pest Hyalopterus pruni (Geoffroy 1762) (Hemiptera, Aphididae)

BackgroundThe employment of entomopathogenic microorganisms is a promising approach for ensuring optimal phytosanitary protection in the framework of biological management of insect crop pests. Among these microbes, entomopathogenic soil-borne bacteria are preferred over pesticides because they help successfully in the natural regulation of arthropod populations, as soil has a favorable ecology for the availability and richness of many beneficial bacterial species. In this study, it was focused on the isolation, identification and characterization of entomopathogenic bacteria isolated from cultivated citrus soils and on the evaluation of their insecticidal potential in the laboratory against the mealy plum aphid, Hyalopterus pruni (Geoffroy 1762) (Hemiptera: Aphididae), a polyphagous and major pest attacking and virus-transmitting of several Prunus crops.ResultsIsolation results gave 11 bacterial isolates, which were more than 50% effective in selection tests on H. pruni aphids at 1 × 108 CFU/ml. A total of seven isolates generated significant insecticidal potential at different concentrations, and their molecular identification based on 16S rRNA genome sequencing yielded the following results: Lysinibacillus fusiformis (B4), Bacillus thuringiensis (B13), B. thuringiensis (B22), B. thuringiensis (B23), B. thuringiensis (B24), Pseudomonas sp. (P2) and Enterococcus gallinarum (P4). The most pathogenic potential of these strains was for E. gallinarum (P4), which induced 100% H. pruni mortality after 72 h of treatment at the concentration (C3 = 1 × 106 CFU/ml), followed by B. thuringiensis (B23), (B22), (B4), (P2), (B13) and (B24) isolates, which caused 96, 91, 85, 83, 65 and 50% mortality rates, respectively, at the lowest concentration (C4 = 1 × 105 CFU/ml). The LC50 and LT50 values were calculated for the entomopathogenic isolates of (P2), (P4), (B23) and (B24). A lowest LC50 value was 1.08 × 102 CFU/ml for (P4) E. gallinarum after 72 h of treatment, whereas (P2) Pseudomonas sp. presented the shortest LT50 of 33.6 h at concentration (C4 = 1 × 105 CFU/ml).ConclusionsThe present study's outcomes have confirmed the existence, abundance, and variety of entomopathogenic bacteria at the soil level in citrus groves. Interestingly, these bacteria could be useful for aphids’ population control on a wide scale through the utilization of their toxins and enzymes, even against insect pests of a broad order.

Decision fusion-based system to detect two invasive stink bugs in orchards

Accurate and early detection of insect pests plays an important role in crop protection and pest management in agriculture, especially in orchards. This paper is focused on evaluating and improving the performance of insect detection algorithms by adopting an ensemble approach of artificial neural networks. A set of advanced object detection models including YOLOv8, Faster R-CNN, RetinaNet, SSD, and FCOS were selected, and the models were trained and evaluated on a common dataset representing digital images of different insect species pests. Two classes were considered represented by quite similar invasive stink bugs, Halyomorpha Halys and Nezara Viridula. These architectures were optimized to identify significant peculiarities and variations between reference insects, including size, shape, and color. Each model has been implemented and optimized to achieve the best possible performance before integrating into an ensemble system. By integrating the predictions of these models through a weighted ensemble mechanism that leverages the F1 Score of each model, a more performant global system was developed capable of detecting insect pests with improved performance over individual models. This significant improvement in insect detection highlights the potential of the proposed ensemble system in efficient and rapid insect pest identification, ultimately providing valuable opportunities for implementing crop monitoring technologies. The research highlights the importance of implementing and developing deep-learning technologies for solving specific challenges in agriculture and brings innovative ways of strategic pest management for sustainable agricultural practices.

Producción de beauveria bassiana para la formulación de bioplaguicidas

The harmful effects of chemicals in conventional agriculture and the growing demand for food free of toxic residues has developed environmentally sustainable strategies. An effective alternative for integrated pest management in agricultural crops are biopesticides formulated with microorganic structures or from the production of active compounds. This paper describes the production processes of Beauveria bassiana for formulating biopesticides for agricultural use. The information was collected through a systematic search in Research Gate, Google Scholar, Science Direct and PubMed, using keywords such as production, Beauveria bassiana, solid fermentation, liquid fermentation and metabolites. The results affirm that B. bassiana is one of the microorganisms with great potential to produce biopesticides, due to the entomopathogenic mechanism of action and secondary metabolites, which can be used for the biological control of phytophagous insects. Likewise, for the formulation of B. bassiana it should be considered a profitable culture medium for large-scale production, also the control of environmental variables such as temperature at 25 oC, relative humidity 65-70 %, pH of 5.4, propagation time between 4 to 8 days, and for the liquid fermentation process, a constant agitation between 200 to 400 rpm must be maintained. Biological products represent an alternative to minimize the use of synthetic pesticides, reduce environmental pollution and ensure food safety and security.

Smart Agriculture Application in Rice Cultivation

Rice is a staple food crop that feeds a significant portion of the world's population. Ensuring sustainable and efficient rice production is crucial for global food security. In recent years, the integration of smart agriculture technologies has shown promise in improving various aspects of rice cultivation. This paper provides an overview of the key smart agriculture applications that can enhance rice cultivation practices. The review discusses the use of precision farming techniques, such as GPS-guided tractors and drones for site-specific management of inputs like fertilizers and pesticides. It also examines how sensor networks and Internet of Things (IoT) devices can monitor environmental conditions, soil fertility, and plant health to enable data-driven decision-making. Smart irrigation systems leveraging soil moisture sensors and weather forecasts are highlighted as a means to optimize water usage. Furthermore, the paper explores the role of automation and robotics in automating labor-intensive tasks like transplanting, weeding, and harvesting. It also discusses the potential of machine learning and predictive analytics to improve crop yield forecasting, pest and disease management, and supply chain logistics. The adoption of these smart agriculture technologies in rice farming has demonstrated improvements in productivity, resource efficiency, and environmental sustainability. However, challenges remain in scaling up these solutions and ensuring their accessibility to smallholder farmers. The paper concludes by outlining future research directions and policy considerations to further advance smart agriculture in the rice cultivation sector.

Effect of hyaluronic acid-stabilized silver nanoparticles on lettuce (Lactuca sativa L.) seed germination

Nanotechnology has brought significant advancements to agriculture through the development of engineered nanomaterials (ENPs). Silver nanoparticles (AgNPs) capped with polysaccharides have been applied in agricultural diagnostics, crop pest management, and seed priming. Hyaluronic acid (HA), a natural polysaccharide with bactericidal properties, has been considered a growth regulator for plant tissues and an inducer of systemic resistance against plant diseases. Additionally, HA has been employed as a stabilizing agent for AgNPs. This study investigated the synthesis and effects of hyaluronic acid-stabilized silver nanoparticles (HA-AgNPs) as a seed priming agent on lettuce (Lactuca sativa L.) seed germination. HA-AgNPs were characterized using several techniques, exhibiting spherical morphology and good colloidal stability. Germination assays conducted with 0.1, 0.04, and 0.02 g/L of HA-AgNPs showed a concentration-dependent reduction in seed germination. Conversely, lower concentrations of HA-AgNPs significantly increased germination rates, survival, tolerance indices, and seed water absorption compared to silver ions (Ag+). SEM/EDS indicated more significant potential for HA-AgNPs internalization compared to Ag+. Therefore, these findings are innovative and open new avenues for understanding the impact of Ag+ and HA-AgNPs on seed germination.

Harnessing Ai for Socio-economic Equity in Uganda: Bridging the Digital Divide through Agricultural Innovation

The paper explores the application of artificial intelligence (AI) technologies in addressing digital divide challenges and enhancing socio-economic equity in Uganda. The digital divide, characterized by disparities in access to digital resources, internet connectivity, and digital literacy, significantly impacts socio-economic development. This research focuses on AI technologies such as machine learning, image recognition, natural language processing (NLP), and GIS-integrated AI, which are mildly actively used in Uganda’s agricultural sector. Studies have shown that digital technologies can address key challenges in agriculture, such as access to extension services, marketing systems, and financial inclusion. For example, mobile money has significantly increased financial inclusion among Uganda's unbanked populations, including women, youth, and rural households. However, barriers such as low digital literacy, limited smartphone ownership, and inadequate internet access hinder the adoption of these technologies, especially among disadvantaged groups. Therefore, while digital innovations have the potential to improve productivity and inclusiveness in agriculture, their limited use could exacerbate existing inequalities by leaving behind those who cannot easily access or afford these technologies. Leveraging these technologies, the study examines how they contribute to improving crop yield predictions, pest and disease management, and precision agriculture. Through the analysis of initiatives undertaken by the National Agricultural Research Organization (NARO), including the use of AI-driven image recognition for plant disease identification and NLP for farmer support systems, highlighting the practical applications and benefits. The findings emphasize the critical infrastructure requirements, ethical considerations, and potential challenges in deploying AI solutions in low-resource communities [9]. Ultimately this research aims to provide a comprehensive framework for utilizing AI to foster digital inclusivity and promote socio-economic growth in developing regions, using Uganda as a case study

Ethnobotanical insights on the management of plant pests and diseases by smallholder farmers in Mpumalanga Province of South Africa

BackgroundPests and diseases are a major contributor to yield losses in sub-Saharan Africa, prompting smallholder farmers to seek cost-effective, accessible and ecologically friendly alternatives for crop protection. This study explored the management of pests and diseases affecting crops across eight selected villages in Ehlanzeni District, Mpumalanga Province, South Africa.MethodsA total of 120 smallholder farmers were purposefully selected utilising the snowball technique. Information on the management of plant pests and diseases was collected through interviews and focus group discussions using semi-structured interview schedules. Ethnobotanical indices, including relative frequency of citation (RFC), use-value (UV) and informant consensus factor (Fic), were used to quantify and rank the plants used for crop protection in the study area.ResultsTwenty-three plant species (16 naturalised exotics and seven indigenous plants) belonging to 16 families were used for managing pests (vertebrates and invertebrates) and diseases (fungal and bacterial related) affecting crops in the study area. The dominant (100%) crops cultivated by the participants were Allium cepa L., Mangifera indica L., Solanum lycopersicum L. and Zea mays L. The RFC value ranged from 0.08 to 0.83 and the three most popular plants for crop protection were Capsium annuum L. (0.83), A. cepa (0.63) and Dichrostachys cinerea (L.) Wight & Arn. (0.43). In terms of the UV, the five most promising plants used as biocontrol were Tulbaghia violacea (0.13), A. cepa (0.12), C. annuum L. (0.09), Solanum campylacanthum Hochst. Ex A.Rich.(0.09) and Pinus pinaster (0.08). Based on the Fic, four categories were established and dominated by fungal diseases (0.64). Furthermore, T. violacea and A. cepa were the most often mentioned plants used against fungal conditions. Other categories cited were bacterial diseases (0.3), invertebrate pests (0.11) and vertebrate pests (0.14), an indication that smallholder farmers had limited agreement or common knowledge about the plants used for their management. The preparation methods included maceration (38%), decoction (38%) and burning (24%). Foliar application (67%) and soil drenching (33%) were used for administering plant extracts during the management of crop pests and diseases.ConclusionThe study highlights the importance of botanicals and associated indigenous knowledge among smallholder farmers in Mpumalanga Province, South Africa. It is pertinent to explore the valorisation of these botanicals by generating empirical data on their biological efficacies and phytochemical profiles.

Response of soil nematode community structure and metabolic footprint to nitrogen addition in alfalfa fields on the Loess Plateau

Nematodes serve as key indicators of soil health in ecological studies. Therefore, the current study examined the community structure and metabolic footprint of soil nematodes in alfalfa fields across varying levels of N supply in the semi-arid Loess Plateau. The findings offer theoretical guidance for the sustainable management of artificial alfalfa grasslands in this region. The research was based on alfalfa fields with different N application rates (0, 50, 100, 150 kg/ha2) as the research object, The shallow plate method was used to separate and extract soil nematodes, identify soil nematode groups, calculate ecological function index and metabolic footprint, and identify indicator species. A total of 6346 nematodes were isolated in this study, belonging to 27 genera and 19 families. Notably, the plant parasitic nematode Helicotylenchus was predominant. As N addition increased, the plant parasitic index (PPI) increased significantly. A N50 application significantly enhanced the soil nematode diversity index (H\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{H}$$\\end{document}) and the free-living index (MI). The findings showcased a noticeable decrease in disturbance within the N50 soil nematode community. This resulted in a mature and stable community structure primarily attributed to the heightened abundance of omnivorous/predatory nematodes. Across various N levels, soil nematode communities underwent significant alterations in the soil food web structure through shifts in their metabolic footprint. Future strategies should focus on refining N management practices and integrating sustainable approaches like crop rotation and pest management. These efforts will contribute to guidelines ensuring artificial alfalfa grasslands lasting health and productivity.

Determinants of Maize Production: Intercropping Maize and Beans in South Western Uganda

Background: Intercropping is the practice of growing two or more crops nearby: in the same row or bed, or rows or strips near enough to allow biological interactions. Advantages of inter cropping include: improving soil’s nutrients to better support crops' growth and development thus improving yields; it improves soil-water retention and diversifies the farmers' incomes in case of failure for one crop. It is one of the natural methods for management of crop pests and diseases. Inter cropping maize and beans is one of the climate-smart agricultural approaches used to boost crop production across the globe. However, some smallholder farmers have a negative attitude toward it due to limited knowledge regarding its benefits. The study aimed at establishment of the effect of intercropping maize and beans on the maize yields in Isingiro District, South Western Uganda. Methods: The experiment was conducted in two seasons (March-May 2020 and August- November 2021). Standard agronomical practices were followed from planting to harvesting, after which the dry weight of maize was measured and recorded. Data analysis was done using ONEWAY ANOVA in R Software Version 4.2.2. Result: Results show that intercropping maize and beans in season one significantly increased maize yields (p less than 0.001) more than it did in season two (p=0.0211). The main effect of Season is statistically significant and large (p less than .001). The main effect of Treatment is statistically significant and large (p less than .001). The study concluded that reduced rainfall negatively affects maize yields and intercropping Longe5 with NABE16 increases maize yields. Early planting and good agronomic practices were recommended to improve maize productivity.

Causality-inspired crop pest recognition based on Decoupled Feature Learning.

Ensuring the efficient recognition and management of crop pests is crucial for maintaining the balance in global agricultural ecosystems and ecological harmony. Deep learning-based methods have shown promise in crop pest recognition. However, prevailing methods often fail to address a critical issue: biased pest training dataset distribution stemming from the tendency to collect images primarily in certain environmental contexts, such as paddy fields. This oversight hampers recognition accuracy when encountering pest images dissimilar to training samples, highlighting the need for a novel approach to overcome this limitation. We introduce the Decoupled Feature Learning (DFL) framework, leveraging causal inference techniques to handle training dataset bias. DFL manipulates the training data based on classification confidence to construct different training domains and employs center triplet loss for learning class-core features. The proposed DFL framework significantly boosts existing baseline models, attaining unprecedented recognition accuracies of 95.33%, 92.59%, and 74.86% on the Li, DFSPD, and IP102 datasets, respectively. Extensive testing on three pest datasets using standard baseline models demonstrates the superiority of DFL in pest recognition. The visualization results show that DFL encourages the baseline models to capture the class-core features. The proposed DFL marks a pivotal step in mitigating the issue of data distribution bias, enhancing the reliability of deep learning in agriculture. © 2024 Society of Chemical Industry.

Could social media be a game-changer in improving agricultural extension delivery in Ghana?

In this study, we set out to investigate the transforming power of social media for agricultural extension delivery services in Ghana. We employed a quantitative research approach and drew insights from 374 farmers. We used descriptive and inferential statistics to analyse the data. Cocoa farmers have some level of awareness of agricultural information on social media (Overall Mean = 1.88). Farmers regard social media platforms as potential sources of agricultural information (Perception Index = 3.38). Majority of farmers own smartphones (53.74 %) and have internet access (53.74 %). About 31.86 % of farmers spend 30 min to 1 h daily time browsing social media for agricultural information. About 57.65 % use social media for accessing agricultural information and implementing farming practices. According to 89.38 % of farmers, social media information helps to improve crop yield and pest management. The main constraint facing farmers in the use of social media is high data costs (Mean = 7.30). We recommend that the government in collaboration with telecommunication companies should explore innovative pricing models to reduce the cost barrier for farmers accessing agricultural content online.

Development of biopellet formulation of Lecanicillium saksenae for the management of pepper root mealybug (Formicoccus polysperes)

Present study was carried out during 2020–23 in the Biocontrol Laboratory for Crop Pest Management, College of Agriculture, Vellayani, Thiruvananthapuram, Kerala. An indigenous isolate of Lecanicillium saksenae (Kushwaha) Kurihara and Sukarno, ITCC 7714 was assessed for its effectiveness against the pepper root mealybug (Formicoccus polysperes Williams). Peculiar symptoms like sudden dissolution of mealy coating subsequent to the application of conidial suspension and paralysis occurring 12 h post-treatment was observed in mealybugs upon infection. Its high potency to mealybug was established with its low LC50 value of 2.2 × 105 conidia/ml and its quick killing ability was proved with a lethal time of 23.13 h. Of the various carriers tested for pelleting, talc + chitin (95:5) was found to be the best as it exhibited highest conidial viability of 78.54%, 5-weeks after storage. Other carriers such as talc, chitin, chitosan and talc + chitosan (90:10) exhibited conidial viability of 33.96–74.58%. Among the different binding agents examined, the combination of talc + chitin (95:5) + carboxymethyl cellulose 6% was the superior one to maintain conidial viability to the tune of 80.63% when compared to microcrystalline cellulose 6% and acacia gum, Arabic 5% which exhibited 30.67–51.50% viability. The investigation therefore concluded that L. saksenae is a promising bioagent for the management of root mealybugs with high speed of action unlike the other microbes. Biopellets formulated at 109 conidia/ml using talc + chitin + 6% CMC at 15% moisture content were found to retain its viability and virulence during a storage period of three months, under ambient conditions.

Activity of insecticide detoxification enzymes in Pectinophora gossypiella (Saunders): Implications for insect pest management in cotton crops

The pink bollworm Pectinophora gossypiella is one of the most important endocarpic pests in cotton. In the management of this pest, the intensive use of synthetic insecticides has led to the reduction of its sensitivity to certain active ingredients. It is in this perspective that the study was conducted to determine the enzymes involved in the metabolism of insecticides in P. gossypiella. Insects were collected in five localities (Bouaké, Bouaflé, Vavoua, Sésuéla and Kounahiri) with high pest pressure in the southern cotton zone. The collected insects were ground for the determination of enzymes involved in insecticide metabolism. The analyses showed a significant increase in glutathione-δ-transferase activity in populations of Bouaké compared to the other localities. However, individuals from Séguéla expressed more esterase (alpha and beta) and oxidase activities. Alpha and beta esterase activities were also more expressed in Bouaflé individuals. The high enzymatic activity of esterases and oxidases in individuals from Séguéla and Bouaflé reflects a low sensitivity to insecticides in which esterases and oxidases are involved in the metabolism. On the other hand, in Bouaké, only glutathione- δtransferases are involved in the detoxification of insecticides.

Editorial: Integrated Pest Management of tropical crops

Editorial: Integrated Pest Management of tropical crops

Biological Approaches for Sustainable Pest and Disease Management in Pulse Crops

Pulse crops, including lentils, peas, chickpeas, and beans, play a crucial role in addressing global food security and nutritional needs. However, their production is often hampered by various pests and diseases, leading to significant yield losses. Conventional pest and disease management practices, heavily reliant on synthetic chemical pesticides, have raised concerns about environmental sustainability, human health, and the development of resistance in target organisms. In recent years, biological approaches have emerged as promising alternatives for sustainable pest and disease management in pulse crops. This review provides a comprehensive overview of various biological control strategies, including the use of beneficial microorganisms, plant extracts, and other eco-friendly methods. The review discusses the mode of action, efficacy, and potential applications of these approaches in combating major pests and diseases affecting pulse crops. Additionally, it highlights the challenges and future prospects of integrating biological control methods into integrated pest and disease management programs. By adopting these sustainable practices, pulse crop production can be enhanced while minimizing the environmental footprint and promoting long-term ecological balance. The review serves as a valuable resource for researchers, extension specialists, and stakeholders in the agricultural sector, emphasizing the importance of biological approaches in achieving sustainable and resilient pulse crop production systems.

Impact of Thermal Stress on Biological Parameters of Chrysoperla zastrowi sillemi (Esben Petersen) under Laboratory Conditions

Aphid lions (Chrysoperla zastrowi sillemi, Esben-Petersen) are important predators in integrated pest management for field crops and vegetables (Neuroptera: Chrysopidae). It is found in almost every environment on Earth, except temperate ones. One of the most significant environmental variables influencing an insect species' growth rate is temperature. Five consistent temperatures were observed: 20±1°C, 25±1°C, 30±1°C, 35±1°C, and 40±1°C. The impact of temperature on biological parameters and developmental phases of Aphid lions feeding on rice moth eggs was noted. The development time of C. zastrowi sillemi was found to be 47.26 days at 20°C and 22.18 days at 40°C. The lifespan of adult males and females was more than twice as long at the lower temperature range as it was at the higher one. The temperature of 25°C produced the greatest duration of oviposition (55.12 days), the highest percentage of pupils (91.43%) and adults’ emergence (90.25%), the maximum fecundity (903.33 eggs/female), and the maximum degree of hatchability (90.84%) of eggs, suggesting that this temperature regime was more suited for the C. zastrowi sillemi that originated in Delhi. The results demonstrate significant variations in the developmental cycle of the immature stages at five distinct temperatures, with a negative relation between temperature increase and developmental duration.