The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.

Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images - eScholarship

3D symmetry detection is a fundamental problem in computer vision and graphics. Most prior works detect symmetry when the object model is fully known, few studies symmetry detection on objects with partial observation, such as single RGB-D images. Recent work addresses the problem of detecting symmetries from incomplete data with a deep neural network by leveraging the dense and accurate symmetry annotations. However, due to the tedious labeling process, full symmetry annotations are not always practically available. In this work, we present a 3D symmetry detection approach to detect symmetry from single-view RGB-D images without using symmetry supervision. The key idea is to train the network in a weakly-supervised learning manner to complete the shape based on the predicted symmetry such that the completed shape be similar to existing plausible shapes. To achieve this, we first propose a discriminative variational autoencoder to learn the shape prior in order to determine whether a 3D shape is plausible or not. Based on the learned shape prior, a symmetry detection network is present to predict symmetries that produce shapes with high shape plausibility when completed based on those symmetries. Moreover, to facilitate end-to-end network training and multiple symmetry detection, we introduce a new symmetry parametrization for the learning-based symmetry estimation of both reflectional and rotational symmetry. The proposed approach, coupled symmetry detection with shape completion, essentially learns the symmetry-aware shape prior, facilitating more accurate and robust symmetry detection. Experiments demonstrate that the proposed method is capable of detecting reflectional and rotational symmetries accurately, and shows good generality in challenging scenarios, such as objects with heavy occlusion and scanning noise. Moreover, it achieves state-of-the-art performance, improving the F1-score over the existing supervised learning method by 2&#x0025;-11&#x0025; on the ShapeNet and ScanNet datasets. 

Learning to Detect 3D Symmetry From Single-View RGB-D Images With Weak Supervision

3D symmetry detection is a fundamental problem in computer vision and graphics. Most prior works detect symmetry when the object model is fully known, few studies symmetry detection on objects with partial observation, such as single RGB-D images. Recent work addresses the problem of detecting symmetries from incomplete data with a deep neural network by leveraging the dense and accurate symmetry annotations. However, due to the tedious labeling process, full symmetry annotations are not always practically available. In this work, we present a 3D symmetry detection approach to detect symmetry from single-view RGB-D images without using symmetry supervision. The key idea is to train the network in a weakly-supervised learning manner to complete the shape based on the predicted symmetry such that the completed shape be similar to existing plausible shapes. To achieve this, we first propose a discriminative variational autoencoder to learn the shape prior in order to determine whether a 3D shape is plausible or not. Based on the learned shape prior, a symmetry detection network is present to predict symmetries that produce shapes with high shape plausibility when completed based on those symmetries. Moreover, to facilitate end-to-end network training and multiple symmetry detection, we introduce a new symmetry parametrization for the learning-based symmetry estimation of both reflectional and rotational symmetry. The proposed approach, coupled symmetry detection with shape completion, essentially learns the symmetry-aware shape prior, facilitating more accurate and robust symmetry detection. Experiments demonstrate that the proposed method is capable of detecting reflectional and rotational symmetries accurately, and shows good generality in challenging scenarios, such as objects with heavy occlusion and scanning noise. Moreover, it achieves state-of-the-art performance, improving the F1-score over the existing supervised learning method by 2%-11% on the ShapeNet and ScanNet datasets.

https://ieeexplore.ieee.org/ielx7/34/4359286/09808406.pdf

A novel vision-based weakly supervised framework for autonomous yield estimation in agricultural applications

Nutrient contents are important for plants. Lack of macronutrients causes plant damage. Several macronutrient deficiencies exhibit similar visual characteristics that are difficult for ordinary farmers to identify. Collaboration between Computer Vision technology and IoT has become a nondestructive method for nutrient monitoring and control, included in the hydroponic system. Computer vision plays a role in processing plant image data based on specific characteristics. However, the analysis of one characteristic cannot represent plant health. In addition, knowing the percentage of macronutrient deficiencies is also needed to support precision agriculture systems. Therefore, we propose a Multi Layer Perceptron architecture that can perform multi-tasks, namely, identification and estimation. In addition, the optimal architecture will also be sought based on the characteristics of the combination of three features in the form of texture, color, and leaf shape. Based on analysis and design, our proposed model has a high potential for identifying and estimating macronutrient deficiency at the same time as well and can be applied to support precision agriculture in Indonesia. Keywords—Multi Layer perceptron; internet of things; feature combination; leaf image; nutrient deficiency

/pdf/design-of-an-intelligent-hydroponics-system-to-identify-25mnit6b.pdf

Design of an Intelligent Hydroponics System to Identify Macronutrient Deficiencies in Chili

A major component of an efficient farming strategy is the precise detection and characterization of plant deficiencies followed by the proper deployment of fertilizers. Through the thoughtful utilization of modern computer vision techniques, it is possible to achieve positive financial and environmental results for these tasks. This work introduces an automation framework that attempts to address the three main drawbacks of existing approaches: 1) lack of generality (methods are tuned for specific data sets); 2) difficulty to apply in variable field conditions; and 3) lack of tool sophistication that limits their applicability. The cultivation of corn lies in the core of the American and global economy with 81.7 million acres harvested only in the USA for the year 2018. The ubiquity of its cultivation makes it an ideal candidate to highlight the large economic benefits from even a small improvement in nutrient deficiency detection. The proposed methodology utilizes drone collected images to detect nitrogen (N) deficiencies in maize fields and assess their severity using low-cost RGB sensors. The proposed methodology is twofold. A low complexity recommendation scheme identifies candidate plants exhibiting N deficiency and, with minimal interaction, assists the annotator in the creation of a training data set that is then used to train an object detection deep neural network. Results on data from experimental fields support the merits of the proposed methodology with mean average precision for the detection of N-deficient leaves reaching 82.3%. Note to Practitioners —The motivation behind this article is the problem of inefficient fertilizer application in corn fields throughout the cultivation season. Current widely spread techniques to counter plant malnutrition suggest the application of excessive amounts of nitrogen fertilizer prior to seeding or the uniform application during the plant growth. These practices result in financial losses and have severe environmental consequences, e.g., the dead zone in the Gulf of Mexico. We propose an automation framework that automatically detects corn nitrogen deficiencies in the field during the plants’ growth, and to achieve our goal, we employ low-cost robotic platforms and RGB sensors. The framework that we developed is able to detect the characteristic pattern of nitrogen deficiency on corn leaves and provide an estimation of the in-field spatial variability of the deficiency.

A Methodology for the Detection of Nitrogen Deficiency in Corn Fields Using High-Resolution RGB Imagery

With the rise of herbicide-resistant weeds, standard farming practices are no longer effective. Precision agriculture technologies can replace some of the standard practices but require information on the physical distribution of weed and crop species. In this work we outline a robotic weed management system and develop the image analysis portion of this system to test it with real aerial survey data. This portion of the system acts as an integral part of a robotic control loop, providing information on weed location and species to a robotic sprayer that will precisely apply the correct herbicide. The image analysis pipeline we develop proves the feasibility of obtaining this information from high altitude aerial surveys of agricultural land that already commonly take place. Our system performs well enough to reliably detect the presence of weed vegetation and to correctly classify it by species with an accuracy of 93.8 % between four classes of weed in images taken at an effective altitude of 80 feet (24.4 m).

Weed Detection and Classification in High Altitude Aerial Images for Robot-Based Precision Agriculture

Continuous representations of objects have always been used in robotics in the form of geometric primitives and surface models. Recently, learning techniques have emerged which allow more complex continuous representations to be learned from data, but these learning techniques require training data in the form of watertight meshes which restricts their application as meshes of this form are difficult to obtain from real data. This paper proposes a modification to existing methods that allows real world point cloud data to be used for training these surface representations allowing the techniques to be used in broader applications. The modification is evaluated on ModelNet10 to quantify the difference between the existing and the proposed methods as well as on a novel precision agriculture dataset that has been released publicly to show the modification's applicability to new areas. The proposed method enables obtaining training data from real world sensors that produce point clouds rather than requiring an expensive meshing step which may not be possible for some applications. This opens the possibility of using techniques like this for complex shapes in areas like grasping and agricultural data collection.

Learning Continuous Object Representations from Point Cloud Data

In view planning, the position and orientation of the cameras have been a major contributing factor to the quality of the resulting 3D model. In applications such as precision agriculture, a dense and accurate reconstruction must be obtained quickly while the data is still actionable. Instead of using an arbitrarily large number of images taken from every possible position and orientation in order to cover the desired area of study, a more optimal approach is required. We present an efficient and realistic pipeline, which aims to optimize the positioning of cameras and hence the quality of the 3D reconstruction of a field of row crops. This is achieved with four steps; an initial flight to obtain a sparse point cloud, the fitting of a simple mesh model, the planning of images via a discrete optimization process, and a second flight to obtain the final reconstruction. We demonstrate the effectiveness of our method by comparing it with baseline methods commonly used for agricultural data collection and processing.

View Planning Using Discrete Optimization for 3D Reconstruction of Row Crops

In order to apply the recent successes of automated plant phenotyping and machine learning on a large scale, efficient and general algorithms must be designed to intelligently split crop fields into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper we notice a similarity between the current state-of-the-art for this problem and a commonly used density-based clustering algorithm, Quickshift. Exploiting this similarity we propose a number of novel, application specific algorithms with the goal of producing a general and scalable plant segmentation algorithm. The novel algorithms proposed in this work are shown to produce quantitatively better results than the current state-of-the-art while being less sensitive to input parameters and maintaining the same algorithmic time complexity. When incorporated into field-scale phenotyping systems, the proposed algorithms should work as a drop in replacement that can greatly improve the accuracy of results while ensuring that performance and scalability remain undiminished.

Henry J. Nelson

Papers

A Methodology for the Detection of Nitrogen Deficiency in Corn Fields Using High-Resolution RGB Imagery

Weed Detection and Classification in High Altitude Aerial Images for Robot-Based Precision Agriculture

Learning Continuous Object Representations from Point Cloud Data

View Planning Using Discrete Optimization for 3D Reconstruction of Row Crops

Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods.