Abstract:
Indoor cactus variant detection usually consists in recognizing and categorizing various
types of indoor cacti through the application of image analysis and artificial intelligence.
This research work introduces a new class of indoor cactus varieties identification
technique based on a specific deep learning model called Convolutional Neural Network
(CNN). To gather the data, 1200 images of six types of indoor cactus were obtained, and
the dataset was expanded to 2620 images to yield higher accuracy. The preprocessed
dataset was then fed to the custom CNN architecture designed for identifying cactus
variants from the given backgrounds. It can be compared with the other well-known
transfer learning models containing Xception, MobileNetV2, DenseNet201 and
InceptionV3. The findings highlighted the fact that the proposed custom CNN has the best
accuracy compared to the other transfer learning-based models with the recognition
accuracy of 99. 62%. This goes to support the effectiveness of the proposed CNN
architecture in the accurate differentiation between such visually similar cactus variants.
