Smart weed recognition in saffron fields based on an improved EfficientNetB0 model and RGB images

Smart weed-crop discrimination is crucial for modern precision weed management. In this study, we aimed to develop a robust system for site-specific weed control in saffron fields by utilizing color images and a deep learning approach to distinguish saffron from four common weeds: flixweed, hoary cress, mouse barley, and wild garlic. A total of 504 images were taken in natural and unstructured field settings. Eight state-of-the-art deep learning networks - VGG19, ResNet152, Xception, InceptionResNetV2, EfficientNetB0, EfficientNetB1, EfficientNetV2B0, and EfficientNetV2B1 were evaluated as potential base networks. These networks underwent pre-training on ImageNet using transfer learning, followed by fine-tuning and improvement with additional layers to optimize performance on our dataset. The improved EfficientNetB0 model stood out as the top performer among the eight models, achieving an accuracy rate of 94.06% and a loss value of 0.513 on the test dataset. This proposed model excelled in accurately classifying plant categories, obtaining f1-scores ranging from 82 to 100%. We scrutinized fifteen scenarios of weed presence in saffron fields, focusing on various weed types, to propose efficient management tactics using the model. These discoveries lay the groundwork for precise saffron weed management strategies that reduce herbicide use, environmental impact, and boost yield and quality.