Recent advances in convolutional neural networks (CNN) have achieved remarkable results in locating objects in images. In these networks, the training procedure usually requires providing bounding boxes or the maximum number of expected objects. In this paper, we address the task of estimating object locations without annotated bounding boxes which are typically hand-drawn and time consuming to label. We propose a loss function that can be used in any fully convolutional network (FCN) to estimate object locations. This loss function is a modification of the average Hausdorff distance between two unordered sets of points. The proposed method has no notion of bounding boxes, region proposals, or sliding windows. We evaluate our method with three datasets designed to locate people's heads, pupil centers and plant centers. We outperform state-of-the-art generic object detectors and methods fine-tuned for pupil tracking.
Methods for pupil tracking and precision agriculture are usually domain-specific. In pupil tracking, the center of the pupil must be resolved in images obtained in real-world illumination conditions @cite_19 . A wide range of applications, from commercial applications such as video games @cite_32 , driving @cite_48 @cite_24 or microsurgery @cite_44 rely on accurate pupil tracking. In remote precision agriculture, it is critical to locate the center of plants in a crop field. Agronomists use plant traits such as plant spacing to predict future crop yield @cite_20 @cite_35 @cite_54 @cite_47 @cite_51 , and plant scientists to breed new plant varieties @cite_10 @cite_11 . @cite_36 , Aich al count wheat plants by first segmenting plant regions and then counting the number of plants in each segmented patch.
@cite_35 @cite_36 @cite_48 @cite_54 @cite_10 @cite_32 @cite_24 @cite_19 @cite_44 @cite_47 @cite_51 @cite_20 @cite_11
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Recently, a body of computer vision research has studied the task of high-throughput plant phenotyping (measurement of plant attributes). The goal is to more rapidly and more accurately estimate plant properties as compared to conventional manual methods. In this work, we develop a method to measure two primary yield attributes of interest; stalk count and stalk width that are important for many broad-acre annual crops (sorghum, sugarcane, corn, maize for example). Prior work of using convolutional deep neural networks for plant analysis has either focused on object detection or dense image segmentation. In our work, we develop a novel pipeline that accurately extracts both detected object regions and dense semantic segmentation for extracting both stalk counts and stalk width. A ground-robot called the Robotanist is used to deploy a high-resolution stereo imager to capture dense image data of experimental plots of Sorghum plants. We ground-truth validate data extracted using two humans who assess the traits independently and we compare both accuracy and efficiency of human versus robotic measurements. Our method yields R-squared correlation of 0.88 for stalk count and a mean absolute error of 2.77 mm where average stalk width is 14.354 mm. Our approach is 30 times faster for stalk count and 270 times faster for stalk width measurement. The Remote Sensing Technologies Center (RSTC) at Mississippi State University (MSU) is investigating the application of high-resolution imagery in the areas of precision agriculture, forest resource management, and environmental assessments of transportation systems. For precision agriculture, the focus of the research is to develop the expertise to utilize this imagery to facilitate management decisions at the farm level. Decision making at the farm level requires high spatial resolution imagery (on the order of 1 meter or less) in order to develop prescriptions for nutrient supplements, integrated pest management (insects, weeds), irrigation, and to serve as a harvesting aid. This degree of analysis of the plant and soil also requires that higher spectral resolution be available to ascertain the nuances in the target vegetation's reflectance and to be able to relate this to specific agricultural problems. In addition, decisions at the farm level must be made within a few days of onset of a problem. Therefore, revisit frequencies for precision agriculture are on the order of 10-14 days. The requirement for high spatial, spectral, and temporal resolution imagery creates special image processing and analysis challenges. These include-data management, geometric and radiometric corrections, ground control points and georeferencing, and data analysis via such approaches as data fusion. The purpose of this paper is twofold. First, to illustrate the diversity of high spatial, spectral, and temporal resolution imagery required for precision agriculture applications. Second, to offer the opportunity to other data fusion researchers to join the author of this paper in developing new data fusion techniques for high-resolution imagery. Agricultural robots are expected to increase yields in a sustainable way and automate precision tasks, such as weeding and plant monitoring. At the same time, they move in a continuously changing, semi-structured field environment, in which features can hardly be found and reproduced at a later time. Challenges for Lidar and visual detection systems stem from the fact that plants can be very small, overlapping and have a steadily changing appearance. Therefore, a popular way to localize vehicles with high accuracy is based on ex- pensive global navigation satellite systems and not on natural landmarks. The contribution of this work is a novel image- based plant localization technique that uses the time-invariant stem emerging point as a reference. Our approach is based on a fully convolutional neural network that learns landmark localization from RGB and NIR image input in an end-to-end manner. The network performs pose regression to generate a plant location likelihood map. Our approach allows us to cope with visual variances of plants both for different species and different growth stages. We achieve high localization accuracies as shown in detailed evaluations of a sugar beet cultivation phase. In experiments with our BoniRob we demonstrate that detections can be robustly reproduced with centimeter accuracy. Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called “deep learning meta-architectures”. We combine each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant’s surrounding area.