Unmanned aircraft systems or drones enable us to record or capture many scenes from the bird’s-eye view and they have been fast deployed to a wide range of practical domains, i.e., agriculture, aerial photography, fast delivery and surveillance. Object detection task is one of the core steps in understanding videos collected from the drones. However, this task is very challenging due to the unconstrained viewpoints and low resolution of captured videos. While deep-learning modern object detectors have recently achieved great success in general benchmarks, i.e., PASCAL-VOC and MS-COCO, the robustness of these detectors on aerial images captured by drones is not well studied. In this paper, we present an evaluation of state-of-the-art deep-learning detectors including Faster R-CNN (Faster Regional CNN), RFCN (Region-based Fully Convolutional Networks), SNIPER (Scale Normalization for Image Pyramids with Efficient Resampling), Single-Shot Detector (SSD), YOLO (You Only Look Once), RetinaNet, and CenterNet for the object detection in videos captured by drones. We conduct experiments on VisDrone2019 dataset which contains 96 videos with 39,988 annotated frames and provide insights into efficient object detectors for aerial images.

Nozzles are ubiquitous in agriculture: they are used to spray and apply nutrients and pesticides to crops. The properties of droplets sprayed from nozzles are vital factors that determine the effectiveness of the spray. Droplet size and other characteristics affect spray retention and drift, which indicates how much of the spray adheres to the crop and how much becomes chemical runoff that pollutes the environment. There is a critical need to measure these droplet properties to improve the performance of crop spraying systems. This paper establishes a deep learning methodology to detect droplets moving across a camera frame to measure their size. This framework is compatible with embedded systems that have limited onboard resources and can operate in real time. The method leverages a combination of techniques including resizing, normalization, pruning, detection head, unified feature map extraction via a feature pyramid network, non-maximum suppression, and optimization-based training. The approach is designed with the capability of detecting droplets of various sizes, shapes, and orientations. The experimental results demonstrate that the model designed in this study, coupled with the right combination of dataset and augmentation, achieved a 97% precision and 96.8% recall in droplet detection. The proposed methodology outperformed previous models, marking a significant advancement in droplet detection for precision agriculture applications.

Continuous efforts were made in detecting cracks in images. Varied CNN models were developed and tested for detecting or segmenting crack regions. However, most datasets used in previous works contained clearly distinctive crack images. No previous methods were validated on blurry cracks captured in low definitions. Therefore, this paper presented a framework of detecting the regions of blurred, indistinct concrete cracks. The framework divides an image into small square patches which are classified into crack or non-crack. Well-known CNN models were employed for the classification and compared with each other with experimental tests. This paper also elaborated on critical factors—the patch size and the way of labeling patches—which had considerable influences on the training performance. Furthermore, a series of post-processes for measuring crack lengths were introduced. The proposed framework was tested on the images of bridge decks containing blurred thin cracks and showed reliable performance comparable to practitioners.

Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e., how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep learning-based approach for droplet detection and tracking which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep learning-based tracking methods. These methods are adept at handling challenges such as droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera operating at 2000 frames per second coupled with innovative automatic annotation tools enables the creation of a large and accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm’s performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays and representing a significant step toward sustainable agricultural practices.