Detection of Pesticides in Organic Fruits and Vegetables Using Artificial Intelligence and IOT

Authors

  • Mrs. C. Vijaya Lakshmi Assistant Professor, M.Tech., (Ph.D), Department of Electronics and Communication Engineering, Sri Venkatesa Perumal College of Engineering and Technology, Puttur, Andhra Pradesh, India Author
  • Nadimpalli Rajasekhar M.Tech- ECE, Embedded Systems, Sri Venkatesa Perumal College of Engineering and Technology, Puttur, Andhra Pradesh, India Author

Keywords:

IOT, Arduino Mega, GSM, PH Sensor, Gas Sensor, NODE MCU

Abstract

The escalating usage of pesticides, steroids, and fertilizers in agriculture poses significant risks to human health. These substances infiltrate the human body through the consumption of contaminated fruits and vegetables. To address these concerns, an effective solution for pesticide analysis in everyday produce is essential. Below is a detailed overview of the issue, supported by data on the adverse impacts on human health and potential solutions for effective pesticide analysis. Through the integration of hardware and software designs, this project endeavors to provide accurate, real-time detection of pesticide levels. Utilizing sensors and Arduino technology, the system aims to deliver comprehensive insights into pesticide presence. Embedded C Program facilitates the establishment of a threshold value for pesticide concentration deemed safe for consumption by both humans and animals. Any fruit registering pesticide levels exceeding this threshold is flagged as potentially hazardous, highlighting the importance of vigilant monitoring in ensuring food safety. Efforts to address the pervasive issue of pesticide contamination in agricultural produce have led to the development of innovative solutions. This project leverages sensor technology and Arduino platforms to enable precise detection of pesticide residues in fruits and vegetables. By establishing a predefined threshold for acceptable pesticide levels, the system provides valuable guidance for consumers concerned about food safety. Through real-time analysis and embedded programming, it offers a proactive approach to identifying produce that may pose health risks due to elevated pesticide concentrations. This integration of hardware and software underscores the potential of technology-driven solutions in safeguarding public health and promoting informed decision-making regarding food consumption.

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References

In 2020, the study "Detection of Pesticide Residues on Strawberries Using Hyperspectral Imaging and Machine Learning" by Liu Y, Chen S, and Yao X was published in Food Control.

In 2019, "A Survey of Hyperspectral Image Analysis" by Xu L and Chen X was published in IEEE Transactions on Geoscience and Remote Sensing, spanning pages 6556-6579.

In 2019, Barbin D F and Sun D W authored a review titled "Hyperspectral Imaging Technology for Non-destructive Quality Evaluation of Fruits and Vegetables" in Comprehensive Reviews in Food Science and Food Safety, spanning pages 1329-1346.

In 2019, Ren J, Wang S, Zhang Z, and Wang Y published the paper "A deep learning approach for recognizing fruit quality based on hyperspectral images" in IEEE Access, spanning pages 22505-22515.

In 2019, the paper "A Machine Learning Approach for Food Quality Analysis Using Hyperspectral Imaging" authored by Wang Y and Ying Y was published in Food Control, spanning pages 72-80.

In 2019, the research paper "Hyperspectral Imaging Combined with Machine Learning for Predicting Quality Attributes of Strawberries During Storage" authored by Wang J, Li W, Li Y, and Li J was published in the Journal of Food Engineering, spanning pages 78-86.

In 2019, Lai C T and Liu C C published the article "A Review of Machine Learning-Based Quality Inspection of Food Products" in IEEE Access, spanning pages 78236-78250.

In 2019, Kusuma A H, Nugroho A W, and Izzatullah O conducted a study titled "Apple Sorting and Grading Using Machine Learning," which was published in Food and Bioprocess Technology, pp. 125-136.

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Published

24-06-2024

Issue

Section

Research Articles

How to Cite

Detection of Pesticides in Organic Fruits and Vegetables Using Artificial Intelligence and IOT. (2024). International Journal of Scientific Research in Science and Technology, 11(3), 734-742. https://ijsrst.com/index.php/home/article/view/IJSRST24113244

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