A Model for Detecting the Presence of Pesticide Residues in Edible Parts of Tomatoes, Cabbages, Carrots and Green Pepper Vegetables - Evarist, Nabaasa; Deborah, Natumanya; Birungi, Grace; Caroline, Nakiguli Kiwanuka; Kule, Baguma John Muhunga (In Artificial Intelligence and Applications, 2022)
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 paper 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 800x1276 pixels taken using InfiRay P2 pro Night Vision Go Mini Infrared Thermal camera with a thermal module were taken from different daily markets in Mbarara city, South Western Uganda. Image preprocessing was done by noise removal and gray scale 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 of0.1-0.8mg/kg, 0.9-1.3mg/kg and 1.4-1.7mg/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 (CNN) 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.
Keywords: Pesticide residues; Artificial intelligence and vegetables