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Stream/Area:
-
Subject:
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Paper ID:
IJSREM62999
Volume:
10 |
5 |
May 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Electronics and Telecommmunication Engineering
JSPM's Bhivarabai Sawant Institute of Technology and Research, Pune,
Department of Electronics and Telecommmunication Engineering
JSPM's Bhivarabai Sawant Institute of Technology and Research, Pune,
Department of Electronics and Telecommmunication Engineering
JSPM's Bhivarabai Sawant Institute of Technology and Research, Pune,
Precision farming tackles problems in industry such as the rising global population and their increasing food demands and the pressing need to modernize and streamline farming methods. Typical forms of monitoring in farming are tedious, slow, and unreliable, and often only catch crop diseases in their advanced stages. This work attempts to fill the gaps using swarm drones that work synchronously with each other as a software defined, flying tractor. This work proposes the Embedded Drone Swarm for Precision Agriculture, integrated with Vision AI for surveying crops, disease breakout detection, and outbreaks mitigation. The proposed system has created a Frogeye Leaf Spot, Rust, and Normal (Healthy) detection model for sequential and gradual implementation as a diagnostic system for crop health disease detection aimed at preventative measures. This is the first work that attempts real-time disease detection in both structured and unstructured environments, and addresses the gaps in controlled, efficient and drone-interlinked disease detection with real-time processing of data during or post analysis for informed decisions, all achieved by using inbuilt hardware. The system is built on the MobileNetV2 architecture, using a deep learning model which is integrated along Raspberry Pi, NodeMCU, GPS, and a camera, with various components added to the model in post-processing to cloud services. Integration of buffered predictions with confidence scores and voting aims to minimize the false discovery rate and improve the accuracy of the model post processing. The system Automation of disease predictions and recommended treatments is achieved by switching farming apparatus with a relay and HTTP API to be either a water pump or a sprayer. It is further accomplished by sending the IoT Device result to an actuator (or relay) to switch on/off the equipment. For disease prediction and recommended treatments, an automated system was built that alerts a farmer using Twilio services. In conclusion, this system is an intelligent, flexible, and affordable solution for advanced precision agriculture. By providing automated decision-making, real-time crop monitoring, and automated precise intervention for soybean diseases, this solution decreases the need for labor and encourages sustainable agriculture practices.
KEYWORDS
I. Conclusion Farming can be done with much more precision with smart farming techniques by combining Vision AI with drone swarming. IoT, deep learning, and embedded systems can be combined into one system that monitors crops, detects diseases, and makes automatic decisions, all in real time. MobileNetV2 can classify images and acts to get immediate feedback by control actions to the farmers with Raspberry Pi. The system identifies diseases, such as Rust and Frogeye Leaf Spot, concerning soybean, and minimizes unsure predictions and enhances the accuracy of the subsequent predictions. Swarming makes field tracking and control via GPS boundaries possible. Cloud-based and geo-referenced data provides trust and data scalability. This system is cost effective and flexible for smart farming. Enhanced control and learning techniques can be utilized for additional predictive control and automation. To modern precision farming, this system can be implemented for the control of the farming and farming losses by providing optimal control to the farmers.
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Publication Date
5/10/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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