Machine learning algorithms are increasingly used to enhance agricultural productivity cost-effectively. A critical task in precision agriculture is locating a plant’s root collar. This is required for the site-specific fertilization of the plants. Though state-of-the-art machine learning models achieve stellar performance in object detection, they are often sensitive to noisy inputs and variation in environment settings. In this paper, we propose an innovative technique of smooth perturbations to improve the robustness of root collar detection tasks using the YOLOv5 neural network model. We train a YOLOv5 model on blueberry image data for root collar detection. A small amount noise is added as a smooth perturbation to the bounding box of dimensions 50× 50, and this perturbed image is fed for training. Furthermore, we introduce an additional test set that represents the out-of-distribution (O.O.D.) case by applying Gaussian blur on test images to simulate particle situation. We use three different image datasets to train our model, the (i) Estonian blueberry, (ii) Serbian blueberry image, and (iii) public dataset sourced from Roboflow datasets, of sample size 118, 2779, and 2993, respectively. We achieve an overall precision of 0.886 on perturbed blueberry images compared to 0.871 on original (unperturbed) images for the O.O.D. test set. Similarly, our smooth perturbation training has achieved an mAP of 0.828, which significantly increases against the result of normal training, which only reaches 0.794. The result proves that our proposed smooth perturbation is an effective method to increase the robustness and generalizability of the object detection task.

Image classification with deep neural networks has reached state-of-the-art with high accuracy. The unreasonable effectiveness of deep neural networks is credited to the manifold hypothesis that states natural data lies on a low-dimensional manifold embedded in the high-dimensional space. The machine learning models learn patterns on these low-rank representations, which gives the learning algorithms robustness. We test the robustness of learning algorithms using the paradigm of “perturb and learn”. This paper proposes a novel technique called Low-Rank Perturbation Adjustment (LoPA), an implicit regularization method used by machine learning models for resisting external perturbations. LoPA exploits the dependencies in model weights that lie in high-dimensional space and projects to low-dimensional while resisting perturbation. We validate LoPA through singular value decomposition (SVD) theory and empirical experiments, showing the statistical distribution of trained model weights of zero mean and small variance. We inject perturbations into our model by hot-swapping the activation functions and interchanging loss functions during the training. An InceptionV3 Neural Network is trained on common FruitFly Drosophila images for binary classification tasks of cancer cells. The Drosophila cancer images are prepared in our lab through immunostaining protocol.