Summary of: Adopting Digital Solutions for Large Scale Surveillance of Crop Pests and Diseases in Developing Countries—A Review, by Gordon Otieno Ouma et al

In recent years, the rapid growth of smartphone technology has presented new opportunities for addressing agricultural challenges in developing nations. With the integration of novel sensing technologies, artificial intelligence (AI), and machine learning (ML) algorithms, smartphones have emerged as powerful tools in crop pest and disease surveillance. This article explores how smartphone applications, advanced sensor technology, and citizen science are revolutionizing farmers’ detection and monitoring of crop pests.

Sensor-based smartphones offer numerous advantages in biosecurity surveillance, including mobility, accessibility, and computing power. These devices, equipped with physical sensors such as positioning, gyroscopes, motion, and high-resolution cameras, provide a unique opportunity for large-scale sensing surveillance with fine-grained spatiotemporal coverage. The growth and diversity of smartphone sensors, coupled with their mobility, enable farmers to collect data on crop pests and diseases efficiently and accurately.

Mobile technology has also given rise to the development of mobile applications, known as “Ag-Apps,” that assist agricultural consultants and farmers in various tasks. These apps can aid in crop pest identification, disease diagnosis, and nutrient analysis and suggest control solutions, bridging the gap between information-haves and information-have-nots in the agricultural sector. By leveraging the power of AI and ML algorithms, these applications enhance the accuracy and speed of pest and disease diagnosis, reducing human error and improving crop management practices.

Mobile Crowd-Sensing (MCS) is an emerging paradigm that harnesses the collective sensing capabilities of smartphone users to gather data on the surrounding environment. While MCS has been predominantly used in environmental pollution and traffic monitoring, its application in agriculture, particularly pest surveillance, still needs to be explored. MCS enables large-scale pest surveillance through AI and advanced sensor technology by encouraging smartphone users to contribute resources, such as device power, communication, time, and effort.

Despite the potential of smartphone technology in crop pest surveillance, several challenges hinder its widespread adoption in developing nations. These challenges include technology constraints, lack of regulatory frameworks, resource requirements, absorptive capability, and the digital divide. However, integrating smartphones into surveillance frameworks presents an opportunity to overcome these challenges and improve agricultural pest management practices.

Smartphone technology, AI, and MCS are transforming crop pest surveillance in developing nations. The advancements in semiconductor technology and the proliferation of smartphone sensors provide a unique opportunity for large-scale data sensing and analysis. By seamlessly integrating technology and stakeholders, including farmers, and addressing the challenges associated with surveillance technologies, we can harness the full potential of smartphones to enhance crop pest management and improve agricultural productivity in developing nations.

This review was published in the Modern Sciences Journal.

Link to the article: https://doi.org/10.57184/msj.v12i2.37

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References

• Ouma, G. O., Okanda, P., Awuor, F. M., & Makiya, C. R. (2023). Adopting Digital Solutions for Large Scale Surveillance of Crop Pests and Diseases in Developing Countries—A Review. Modern Sciences Journal, 12(2). https://doi.org/10.57184/msj.v12i2.37