A Model for Detecting the Presence of Pesticide Residues in Edible Parts of Tomatoes, Cabbages, Carrots, and Green Pepper Vegetables

Authors

  • Nabaasa Evarist Department of Computer Science, Mbarara University of Science and Technology, Uganda https://orcid.org/0000-0002-1722-767X
  • Natumanya Deborah Department of Computer Science, Mbarara University of Science and Technology, Uganda https://orcid.org/0000-0002-4491-0650
  • Grace Birungi Department of Chemistry, Mbarara University of Science and Technology, Uganda
  • Caroline Nakiguli Department of Chemistry, Mbarara University of Science and Technology, Uganda
  • Baguma John Department of Accounting and Finance, Mbarara University of Science and Technology, Uganda

DOI:

https://doi.org/10.47852/bonviewAIA42021388

Keywords:

pesticide residues, artificial intelligence and vegetables

Abstract

With increased resistant pests and low crop yields, farmers especially in sub-Saharan Africa have greatly embraced usage of chemicals. These chemicals include pesticides used in gardens for better yields and also in the stalls for longer shelf life by sellers of farm products especially fresh perishables like tomatoes, cabbages, carrots, and green pepper vegetables. This, if not checked, may expose humans and animals to pesticide residues. In this research, a model for detecting the presence of pesticide residues in edible parts of vegetables (tomatoes, cabbages, carrots, and green pepper) was developed. A dataset consisting of 1094 images of both contaminated and uncontaminated vegetables including tomatoes, cabbages, carrots, and green pepper with a scale magnification of 800 × 1276 pixels taken using InfiRay P2 pro Night Vision Go Mini Infrared Thermal camera with a thermal module was taken from different daily markets in Mbarara city, South Western Uganda. Image preprocessing was done by noise removal and grayscale conversion. Both the neural network and median filter were applied on the images. A python script was used to cluster the dataset based on chemical concentrations rates of 0.1–0.8 mg/kg, 0.9–1.3 mg/kg, and 1.4–1.7 mg/kg, and this was done for both training and testing dataset. Feature extraction was done to detect the presence of mancozeb, dioxacarb, and methidathion residues from the cleaned images. To test the developed model, convolutional neural networks transfer learning models, Inception V3, VGG16, VGG19, ResNet50, and the scratch model were used. From the results obtained, Inception V3 achieved better performance compared to other transfer learning models with 96.77% followed by VGG16 at 86.98%, VGG19 at 87.56%, and ResNet50 at 82.11%, whereas the developed scratch model achieved 89.13% classification accuracy.

 

Received: 21 July 2023 | Revised: 15 November 2023 | Accepted: 10 January 2024

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

 

Data Availability Statement

The data that support the findings of this study are openly available in vegetable chemical residue detection dataset at https://www.kaggle.com/datasets/vegetabledataset/mancozeb-and-other-chemical-residue.

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Published

2024-01-22

How to Cite

Evarist, N., Deborah, N., Birungi, G., Caroline, N. K., & Kule, B. J. M. (2024). A Model for Detecting the Presence of Pesticide Residues in Edible Parts of Tomatoes, Cabbages, Carrots, and Green Pepper Vegetables. Artificial Intelligence and Applications, 2(3), 225–232. https://doi.org/10.47852/bonviewAIA42021388

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Section

Research Article