Informed-Learning-Guided Visual Question Answering Model of Crop Disease.

In contemporary agriculture, experts develop preventative and remedial strategies for various disease stages in diverse crops. Decision-making regarding the stages of disease occurrence exceeds the capabilities of single-image tasks, such as image classification and object detection. Consequently, research now focuses on training visual question answering (VQA) models.

Optimizing tomato detection and counting in smart greenhouses: A lightweight YOLOv8 model incorporating high- and low-frequency feature transformer structures.

Tomato harvesting in intelligent greenhouses is crucial for reducing costs and optimizing management. Agricultural robots, as an automated solution, require advanced visual perception. This study proposes a tomato detection and counting algorithm based on YOLOv8 (TCAttn-YOLOv8).

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification.

To solve the problems of weak generalization of potato early and late blight recognition models in real complex scenarios, susceptibility to interference from crop varieties, colour characteristics, leaf spot shapes, disease cycles and environmental factors, and strong dependence on storage and computational resources, an improved YOLO v5 model (DA-ActNN-YOLOV5) is proposed to study potato diseases of different cycles in multiple regional scenarios. Thirteen data augmentation techniques were used to expand the data to improve model generalization and prevent overfitting; potato leaves were extracted by YOLO v5 image segmentation and labelled with LabelMe for building data samples; the component modules of the YOLO v5 network were replaced using model compression technology (ActNN) for potato disease detection when the device is low on memory. Based on this, the features extracted from all network layers are visualized, and the extraction of features from each network layer can be distinguished, from which an understanding of the feature learning behavior of the deep model can be obtained.

Monocular Depth Estimation with Self-Supervised Learning for Vineyard Unmanned Agricultural Vehicle.

To find an economical solution to infer the depth of the surrounding environment of unmanned agricultural vehicles (UAV), a lightweight depth estimation model called MonoDA based on a convolutional neural network is proposed. A series of sequential frames from monocular videos are used to train the model. The model is composed of two subnetworks-the depth estimation subnetwork and the pose estimation subnetwork.