The need for automated pest monitoring in agricultural

The gap

Evidence profile

Sourced from the stated challenges and future-work section and stated research gap and future work and recommendations of the source papers, classified as general, spanning 7 journals.

Supporting evidence — 8 representative gaps

• An AI-Driven End-to-End Agricultural Guidance System with Multilingual and Voice Support (2026) · International Research Journal on Advanced Engineering Hub (IRJAEH) · doi

  Climate variability and unusual weather patterns. Pest resistance and disease outbreaks. Lack of access to expert advice and limited digital literacy among farmers.

  generalstated challenges
  Keywords: climate variability unusual weather patterns pest resistance disease
• Agricultural Transformation and Sustainable Crop Production in India: Integrating Nutrient Management, Crop Diversification, and Rural Development Strategies (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  Future research should focus on the development of precision agriculture and climate-resilient cropping systems. It should explore the potential of digital agriculture and artificial intelligence in improving crop yields and reducing environmental impacts.

  generalfuture-work section
  Keywords: future research focus development precision agriculture climate-resilient cropping
• Fine-Grained Recognition of Insect Pests from Digital Images: A Survey (2026) · Neotropical Entomology · doi

  The need for automated pest monitoring in agricultural environments. The lack of effective methods for multi-species recognition.

  generalstated research gap
  Keywords: need automated pest monitoring agricultural environments lack effective
• FERTICAST – Data-Driven Fertilizer Optimization System Embedded with Rainfall Prediction (2026) · International Research Journal on Advanced Engineering and Management (IRJAEM) · doi

  Ferticast can be further improved by integrating IoT sensors to collect real-time soil data such as moisture and nutrient levels, which would increase prediction accuracy. Developing a mobile application can enhance accessibility for farmers. The model can also be strengthened using larger and region-specific datasets and advanced techniques like deep learning. Additionally, incorporating satellite data and real- time alerts can make the system more accurate and practical for precision agriculture.

  generalfuture workevidence 5/5
  Keywords: real time ferticast further improved integrating sensors collect soil moisture nutrient levels increase prediction accuracy
• AI-Driven Crop Disease Prediction System (2026) · International Journal of Science, Strategic Management and Technology · doi

  The AI-Driven Crop Disease Prediction System bridges conventional agricultural practices with advanced automation. By combining computer vision, deep learning, and environmental data analysis, the system provides farmers with an efficient, accurate, and accessible solution for early disease identification. It reduces dependency on manual expert inspection, supports localized decision-making, and promotes sustainable crop management through timely intervention and prevention strategies. Future work will focus on the following enhancements: • Blockchain-based record storage to ensure transparency and traceability of disease data. • Federated AI learning models for privacy-preserving and region-specific training. • Integration of conversational chatbots to assist farmers with instant, context-aware recommendations. • Predictive modeling for disease outbreak forecasting using weather and soil parameters. • IoT-based real-time monitoring for automated image and sensor data collection. • Cloud-based synchronization for scalable deployment across multiple agricultural zones. © Author(s). This work is peer-reviewed, openly published, and permanently archived This article is openly accessible and reusable with proper attribution.

  generalfuture workevidence 5/5
  Keywords: disease based crop system agricultural learning farmers accessible management peer reviewed openly ijsmt international journal
• Evolving pest management paradigms through information and communication technologies (2026) · Plant Science Today · doi

  Addressing the constraints of ICT-based pest management calls for a comprehensive and vision-oriented approach. Rural digital infrastructure investment is essential, particularly in enhancing Internet connectivity and electricity supply. Access to ICT tools must be provided on an equal basis. Developing user-friendly applications in local languages, with voice-based interfaces and intuitive designs, will further enhance accessibility for elderly and low-literacy communities. Advances in AI and machine learning should be supported by the development of large, high-quality, open-source pest image datasets that represent diverse crops and agroecological regions. This will raise the accuracy of computerised pest identification systems in various farming systems. Sensor calibration and farmers training are key to addressing data quality challenges in pest monitoring. Additionally, predictive modelling systems must be continuously refined to incorporate evolving pest dynamics and climate variability. Stronger collaboration among ICT developers, extension agencies, research institutions and private-sector partners is essential for overcoming current limitations. Integrated digital platforms that consolidate pest advisories from multiple sources can https://plantsciencetoday.online reduce contradictory recommendations and promote consistent, science-based guidance. Ensuring the timely dissemination of ICT innovations to farmers will further support the adoption of sustainable, ICT-enabled pest management practices.

  generalfuture workevidence 5/5
  Keywords: pest based systems addressing management digital essential must further quality farmers constraints calls comprehensive vision
• Evolving pest management paradigms through information and communication technologies (2026) · Plant Science Today · doi

  for pest diagnosis and management. Representative Android-based mobile applications used for pest management in various countries are presented in Table 6 (83-92). Mobile applications integrated with AI-based chatbots and cloud computing platforms are emerging as highly effective ICT tools for pest management advisories. They enable the real-time collection, integration and analysis of available data. AI chatbots provide interactive, personalised recommendations, while cloud platforms support large-scale data processing and continuous Table 5. Some of the important SMS and voice call providers for agriculture/pest related inforation in india S.No.

  generalrecommendationsevidence 5/5
  Keywords: pest management based mobile applications chatbots cloud platforms diagnosis representative android used various countries presented
• Smart Greenhouse Automation Research as a Multidimensional Field: A Comparative Bibliometric Analysis of WoS and Scopus Literature (2026) · Turkish Journal of Agriculture - Food Science and Technology · doi

  Future research should focus on examining the impact of IoT-based monitoring systems, machine learning applications, and data-driven automation approaches on agricultural productivity. Further studies should investigate the potential applications of smart greenhouse automation in different regions and contexts.

  generalfuture-work sectionevidence 5/5
  Keywords: future research focus examining impact iot-based monitoring systems

Questions about this gap