AN INNOVATIVE METHOD FOR FORECASTING THE RISK OF LONG-HORNED (DOLICHERA) LOCUST-LIKE INSECTS TO AGRICULTURAL CROPS BASED ON METEOROLOGICAL DATA

SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 12 DECEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 52 AN INNOVATIVE METHOD FOR FORECASTING THE RISK OF LONG-HORNED (DOLICHERA) LOCUST-LIKE INSECTS TO AGRICULTURAL CROPS BASED ON METEOROLOGICAL DATA I.A. Khamroev1, A.B. Membetnazarov2 Head of Department, Agency for Quarantine and Plant Protection, Independent Researcher, Research Institute for Quarantine and Plant Protection, Doctor of Philosophy in Agricultural Sciences (PhD)1 Head of Laboratory, Research Institute of Quarantine and Plant Protection, Doctor of Philosophy in Agricultural Sciences, Senior Researcher2 https://doi.org/10.5281/zenodo.18027841 Abstract. This article highlights the capabilities of the information system "Dala Nazorati" ("Field Monitoring"), which enables forecasting the development of long-horned locust-like pests (Dolichera) up to 10 days in advance. The system calculates the phenological development stages of harmful insects based on temperature data and assists in planning agro-technical measures to prevent crop damage. As a result of the study, the optimal temperature range and base temperature for pests belonging to the genus Dolichera were determined, and automatic forecasting algorithms were developed based on the degree-day model. Keywords: long-horned locust, Dolichera, pest forecasting, field monitoring, degree-day model, phenological calendar, Uzbekistan. Introduction The importance of monitoring and forecasting systems in protecting crops from pests and plant diseases is growing every year. In the context of climate change and the renewal of agricultural practices, as well as the increased activity of harmful organisms, there is a need to introduce new approaches and technologies. Traditional forecasting methods based on field observations are largely dependent on the human factor, which often leads to delays in the implementation of protective measures. In this regard, there is an urgent need to develop digital solutions based on modern information and communication technologies. In Uzbekistan's agriculture, orthopteran insects (Orthoptera), particularly pests of the genus Dolichera, which are long-horned grasshoppers, cause significant damage to agricultural crops. Global warming has a significant impact on their population and phenological development. Traditional field observations do not allow for the timely detection of these insects. In this regard, there is a growing need for modern systematic methods based on real-time pest monitoring and automatic forecasting of their spread. Literature review. Digital technologies are widely used in the development of monitoring and forecasting systems. Remote observation systems, in particular automated monitoring tools, have become one of the key areas of modern phytosanitary control. For example, the Insect Monitoring Radar (IMR) system, which has been operating in Australia since 1999, has proven its effectiveness in tracking insect migration. It allows analyzing their movement based on radar data and meteorological observations (Drake, 2002). The Plant Pest Forecasting System (NAPPFAST), SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 12 DECEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 53 developed by North Carolina State University (USA), provides users with templates for phenological modeling of plant pests and diseases. The system is integrated with meteorological databases and uses degree-day models, infection models, and simple empirical models (Magarey et al., 2015). Using the eBoss system, developed on the basis of infrared sensors, 302,093 insects were recorded during 9 months of observation. Statistical analysis confirmed the existence of a relationship between their density and weather factors (Topu et al., 2023). The Zoolog VARL system, equipped with an automated pheromone probe, has demonstrated high efficiency in detecting harmful insects (Miklós Tóth). Automated systems allow insects to be identified by visual and chemical characteristics. For example, an automatic response to the sex pheromones of Helicoverpa armigera helps predict periods of their activity (Bakthavatsalam et al., 2016). In recent years, technologies for insect recognition and damage assessment based on images using artificial intelligence and deep neural networks have been actively improved. Čirjak et al. (2023) developed statistical models based on more than 52,000 images. However, there are certain limitations to the use of radar systems for detecting small insects, but this problem can be solved with improved sensors and computing systems (Rydhmer et al., 2023). At present, universal systems capable of accurately predicting the appearance of pests 10 days before they become active are still under development and have not been widely implemented. Research methods. This study was based on the study of the phenological development of pests and diseases of agricultural crops in various agroclimatic zones of Uzbekistan. To identify harmful species, especially representatives of the Lepidoptera order, pheromone traps were used to monitor their flight activity on a daily basis. Forecasting methods based on phenological calendars and satellite remote sensing data provide more accurate and timely information compared to traditional methods. In turn, this method allows farmers and regional specialists to implement effective control measures in a timely manner. This approach has been particularly effective against pests from the Orthoptera order and long-horned locusts of the Dolichera genus. The following formula was used to calculate the accumulated effective temperature required for pests to reach certain stages of development: S = ((Tmax + Tmin) / 2) – Tbase Where: • Tmax — maximum daily temperature (°C), • Tmin — minimum daily temperature (°C), • Tbase — base temperature set individually for each pest species. In each region, the activity of moths (Noctuidae) and locusts was monitored using pheromone and light traps. The data obtained was transmitted in real time to the Field Monitoring system (Dala nazorati). Research results. Timely detection and forecasting of pests and their development allow for the effective implementation of protective measures in the agricultural sector. Traditional methods involve lengthy and inaccurate observations. In this regard, an innovative system has been developed based on automated calculations, international meteorological data, and the “Dala nazorati” (field monitoring) information platform, which includes the following stages: 1. Egg-laying period 2. Larval stage 3. Imago flight period 4. Period of maximum damage SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 12 DECEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 54 Due to 10-day forecast data, specialists are able to take the following precautions: • Proper planning of field work and pesticide application schedules • Strengthening control measures in compliance with environmental safety requirements • Proper allocation of resources and increasing the effectiveness of pest control For example, in the Tashkent and Jizzakh regions in the spring season of 2025, the flight activity of locust pests of the genus Dolichera was confirmed with a deviation of only 2–3 days from the predicted dates. This indicates the high accuracy of the developed system. This system provides a 10-day forecast of pest development based on temperature, humidity, and other meteorological factors. The stages of development are displayed in tabular form (Table 1). The system integrates and uses phenological data on 47 major pest species and 8 disease species, corresponding to the phenological calendar, based on the degree-day model. Conclusions As a result of the study, an automated forecasting module for long-horned locust pests (Dolichera) was developed based on the “Dala nazorati” (field monitoring) information system. The developed system has the following advantages: • Ability to determine development phases 10 days before they occur • Use of a degree-day model • Prompt sending of warnings to farmers and specialists In the future, it is planned to expand the functionality of the system by integrating artificial intelligence technologies, which will enable official identification of pests and the provision of recommendations in real time. This, in turn, will improve the quality of pest control measures in Uzbekistan's agriculture. The system enables accurate planning of agronomic and phytosanitary measures. 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