In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns. Given a geographical region with fully mapped coffee plantations, we observe that this knowledge can be used to train a classifier and to map a new county with no need of samples indicated in the target region. Experimental results show that transferring knowledge via some UDA strategies performs better than just applying a classifier trained in a region to predict coffee crops in a new one. However, UDA methods may lead to negative transfer, which may indicate that domains are too different that transferring knowledge is not appropriate. We also verify that normalization affect significantly some UDA methods; we observe a meaningful complementary contribution between coffee crops data; and a visual behavior suggests an existent of a cluster of samples that are more likely to be drawn from a specific data.
University of California, Davis（加利福尼亚大学戴维斯分校）；National Taiwan University（台湾大学）；National Tsing Hua University（清华大学（台湾））
Automatic photo cropping is an important tool for improving visual quality of digital photos without resorting to tedious manual selection. Traditionally, photo cropping is accomplished by determining the best proposal window through visual quality assessment or saliency detection. In essence, the performance of an image cropper highly depends on the ability to correctly rank a number of visually similar proposal windows. Despite the ranking nature of automatic photo cropping, little attention has been paid to learning-to-rank algorithms in tackling such a problem. In this work, we conduct an …
Pest and disease control plays a key role in agriculture since the damage caused by these agents are responsible for a huge economic loss every year. Based on this assumption, we create an algorithm capable of detecting rust (Hemileia vastatrix) and leaf miner (Leucoptera coffeella) in coffee leaves (Coffea arabica) and quantify disease severity using a mobile application as a high-level interface for the model inferences. We used different convolutional neural network architectures to create the object detector, besides the OpenCV library, k-means, and three treatments: the RGB and value to q…
From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments
Fraunhofer Institute for Physical Measurement Techniques IPM（弗劳恩霍夫物理测量技术研究所IPM）；University of Freiburg, Department of Sustainable Systems Engineering INATECH（弗赖堡大学可持续系统工程系INATECH）
From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping
College of Forest Resources and Environmental Science, Michigan Technological University（密歇根理工大学森林资源与环境科学学院）；Warnell School of Forestry and Natural Resources, University of Georgia（佐治亚大学沃内尔林业与自然资源学院）；Annis Water Resources Institute, Grand Valley State University（大峡谷州立大学安尼斯水资源研究所）；BioSense Institute（生物感知研究所）；Department of Agricultural, Food, and Resource Economics, Michigan State University（密歇根州立大学农业、食品与资源经济学系）
Image matching is a classic and fundamental task in computer vision. In this paper, under the hypothesis that the areas outside the co-visible regions carry little information, we propose a matching key-points crop (MKPC) algorithm. The MKPC locates, proposes and crops the critical regions, which are the co-visible areas with great efficiency and accuracy. Furthermore, building upon MKPC, we propose a general two-stage pipeline for image matching, which is compatible to any image matching models or combinations. We experimented with plugging SuperPoint + SuperGlue into the two-stage pipeline, …
A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa
Bayero University（巴耶罗大学）；Alpen-Adria-Universität Klagenfurt（克拉根福阿尔卑斯-亚得里亚大学）；Gombe State University（贡贝州立大学）；Aliko Dangote University of Science and Technology（阿里科·丹格特科技大学）；Ahmadu Bello University（艾哈迈杜·贝洛大学）；Federal University Dutse（杜塞联邦大学）
Neural Network-based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-based Model and Direct Imaging Model
Department of Computer Science & Engineering, Netrokona University（计算机科学与工程系，纳托克纳大学）；Department of Computer and Communication Engineering, International Islamic University Chittagong（计算机与通信工程系，国际伊斯兰大学钦德加角分校）；School of Computing Sciences and Computer Engineering, University of Southern Mississippi（计算科学与计算机工程学院，密西根州立大学）；Department of CSE, Daffodil International University（计算机科学与工程系，达福尔国际大学）
