Instance and semantic segmentation form the backbone of robotic perception and are crucial to many tasks. While most research in the area focuses on improving segmentation quality metrics, there are plenty of applications where approximate methods are adequate as long as they are fast, especially in applications with large amounts of data like precision agriculture. In order to apply the recent successes of machine learning and computer vision on a large scale using robotics, efficient and general algorithms must be designed to intelligently split point clouds into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper, we capitalize on a similarity between the current state-of-the-art for roughly segmenting corn plants and a commonly used density-based clustering algorithm, Quickshift. Exploiting this similarity we propose a novel algorithm, Ground-Density Quickshift++, with the goal of producing a general and scalable field segmentation algorithm that segments individual plants and their stems. This algorithm produces quantitatively better results than the current state-of-the-art on both plant separation and stem segmentation while being less sensitive to input parameters and maintaining the same algorithmic time complexity. When incorporated into field-scale phenotyping systems, the proposed algorithm should work as a drop-in replacement that can greatly improve the accuracy of results while ensuring that performance and scalability remain undiminished.

The determination of a crop's growth-stage is critical information for precision agriculture. Estimates of the growth-stage are used to guide irrigation and the application of agrochemicals. Of particular importance is the use of fertilizers, however, growth-stage estimates may also suggest further investigation of potential crop infections and infestations. Traditionally, the growth-stage is based upon a manual random sample of a very small number of plants that are then analyzed to produce an estimate for the entire crop (up to thousands of acres). In order to increase the sample size (and thus accuracy) and to enable precision agriculture to address non-uniform crop development across a field, we present an analysis methodology that facilitates the automated growth-stage analysis of dense point clouds that are derived from drone imagery. Our method utilizes a standard camera drone and does not use specialized sensors or geo-spatial tagging. We propose a multi-stage unsupervised method, which provides information about the individual plant locations in a field plot with a high probability. The method also produces a measure of individual plant heights, which along with their location are critical for later growth-stage estimation and necessary for robotic precision application. We confirm our method's efficacy with experimental results on corn fields in Minnesota.

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.

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.

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.