Using Convolutional Neural Networks for Nutrient Deficiency Detection and Classification in Strawberry Plants

Strawberry plants exhibit various nutrient deficiency symptoms on their leaves. The discoloration and pattern can be used to detect and classify nitrogen, potassium, and phosphorous deficiencies. Given the effectiveness and efficiency of Convolutional Neural Networks (CNNs) in classification tasks, we chose several pretrained CNN architectures and modified them to fit our custom image dataset. After compiling and labeling a dataset of 1008 strawberry leaf images, it was divided into training and test sets, which were subsequently evaluated on several CNN models. The following model architectures were selected based on the performance and computational cost: ResNet50 V2, EfficientNetBO, MobileNet V3 Small, and MobileNet V3 Large. All of the models achieved a test accuracy of at least 90%. This study demonstrates the effectiveness of using CNNs in detecting and classifying nutrient deficiency in strawberry plants.