Niranjan Mandi

Agricultural pest surveillance is shifting from periodic manual inspection towards digitally mediated observation, combining automated traps, computer vision, environmental sensors and statistical or machine-learning forecasts. The technological literature reports increasingly sophisticated species recognition, remote counting and near-real-time data transmission. Whether these capabilities generate reliable estimates of pest pressure or improve integrated pest management, however, depends on ecological sampling, field validation and decisions taken after an alert. This critical narrative review evaluates the measurement-to-decision pathway rather than treating classification accuracy as evidence of agronomic benefit. Peer-reviewed research and relevant methodological literature were identified through accessible scholarly sources, with thematic emphasis on camera-equipped pheromone, light and adhesive traps; image-recognition architectures; optical, acoustic and environmental sensing; predictive modelling; and operational evaluation. The synthesis indicates that instrumented traps and image-processing methods can reduce some observation burdens and produce useful within-system temporal information. Their reported performance is nevertheless strongly conditioned by trap selectivity, target species, illumination, specimen overlap, annotation quality, seasonal distribution shifts and the choice of validation dataset. Forecasting approaches can exploit temperature, phenology and historical trap catches, but retrospective fit does not establish transferable warning value. Ecological inference also requires distinguishing insect activity, trap catches, abundance, crop injury and economic risk. The most consequential weaknesses are limited independent multi-site prospective testing, incomplete uncertainty reporting, insufficient comparison with manual scouting, and few controlled evaluations of management consequences. A deployment-oriented framework is proposed in which measurement validity, calibration, forecast skill, decision thresholds, costs and non-target effects are appraised as connected requirements. Progress will depend less on marginal improvements in benchmark detection scores than on prospective, locally calibrated studies that measure timeliness, intervention quality, labour, pesticide use, yield and biodiversity outcomes.

Semiochemicals occupy a distinctive position in agricultural entomology because they can convert species- and context-specific chemical communication into tools for surveillance, behavioural manipulation and population suppression. This critical narrative review evaluates the evidence for semiochemical use in pest monitoring, mass trapping, attract-and-kill, mating disruption and integrated pest management, with particular attention to the transition from chemical discovery to reliable field performance. The literature was selected from agricultural, biological and multidisciplinary scholarly sources for the period 1 January 1990 to 19 June 2026, while retaining seminal studies needed to interpret current practice. The evidence is strongest for sex-pheromone monitoring and for mating disruption in well-characterised lepidopteran systems. Monitoring benefits from high sensitivity and taxonomic selectivity, but trap catch is not a direct measure of population density or crop injury unless lure performance, trap design, crop phenology, weather and management context are calibrated. Mass trapping and attract-and-kill can suppress pests, yet their success is more conditional because it depends on population density, immigration, target sex, lure competitiveness, trap retention, spatial deployment and persistence of the killing or release system. Plant volatiles, aggregation pheromones, repellents and herbivore-induced cues broaden the behavioural repertoire available for integrated pest management, although context dependence is greater and field validation is less mature. Digital camera traps, machine vision, receptor-guided discovery and biodegradable controlled-release matrices are important advances, but each introduces new validation requirements rather than eliminating ecological uncertainty. Across applications, the central translational weakness is a tendency to infer crop protection from behavioural response or trap capture without demonstrating injury, yield, economic or area-wide outcomes. Future progress requires standardised reporting of release rates and trap performance, multi-site field validation, explicit spatial design, integration with economic thresholds and biological control, and independent assessment of non-target and adoption consequences. Semiochemicals are therefore best understood not as stand-alone replacements for pesticides, but as information-rich components of ecologically designed pest management systems.

Cotton (Gossypium hirsutum L.) is a major fibre crop underpinning the global textile industry; however, its productivity is increasingly threatened by climatic variability and the resurgence of sap-sucking insect pests. The interaction between genotype and environment (G × E) further complicates the identification of stable and high-yielding genotypes, particularly under rainfed conditions. The present study evaluated 7 cotton genotypes across 9 environments (three locations over 3 consecutive years: 2021–24) in Odisha, India, to assess G × E interaction for 14 quantitative traits related to yield and sucking pest resistance using advanced statistical approaches. Combined analysis of variance revealedhighly significant (p < 0.01) effects of genotypes, environments and G × E interaction for all traits studied. The interaction component was particularly significant for key traits, including seed cotton yield (SCY), lint yield (LY) and populations of major sucking pests, necessitating a comprehensive stability analysis. High broad-sense heritability coupled with moderate to high genetic advance for yield and pest resistance traits indicated the predominance of additive gene action. Stability analyses using the Eberhart and Russell model, additive main effects and multiplicative interaction (AMMI) and genotype plus genotype by environment (GGE) biplot consistently identified genotype BS 3-17 as superior, exhibiting the highest mean SCY (1978 kg ha-1) and LY (675 kg ha-1), along with the lowest mean populations of aphids (APH) (5.28 per 3 leaves) and jassids (JAS) (1.82 per 3 leaves) across environments. The multi-trait stability index (MTSI) further ranked BS 3-17 as the most desirable genotype (MTSI score = 5.51), indicating its closest proximity to the ideotype. Agronomic validation trials demonstrated that high-density planting (90 × 30 cm) combined with 125 % of the recommended dose of fertilisers significantly enhanced the yield potential of BS 3-17, achieving up to 3114 kg ha-1. These findings establish BS 3-17 as a climate-resilient, high-yielding genotype suitable for commercial cultivation and a promising donor parent for breeding programs targeting yield stability and sucking pest resistance.