The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

To develop new crop varieties and monitor plant growth, health, and traits, automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection. To perform per-microplot phenotypic analysis, localizing and detecting individual microplots in an orthomosaic image of a field are major steps. Our algorithm uses an automatic initialization of the known field layout over the orthomosaic images in roughly the right position. Since the orthomosaic images are stitched from a large number of smaller images, there can be distortion causing microplot rows not to be entirely straight and the automatic initialization to not correctly position every microplot. To overcome this, we have developed a three-level hierarchical optimization method. First, the initial bounding box position is optimized using an objective function that maximizes the level of vegetation inside the area. Then, columns of microplots are repositioned, constrained by their expected spacing. Finally, the position of microplots is adjusted individually using an objective function that simultaneously maximizes the area of the microplot overlapping vegetation, minimizes spacing variance between microplots, and maximizes each microplot’s alignment relative to other microplots in the same row and column. The orthomosaics used in this study were obtained from multiple dates of canola and wheat breeding trials. The algorithm was able to detect 99.7% of microplots for canola and 99% for wheat. The automatically segmented microplots were compared to ground truth segmentations, resulting in an average DSC of 91.2% and 89.6% across all microplots and orthomosaics in the canola and wheat datasets.

.Abstract-This paper scrutinizes Mongol Barota -a fully functional, stand-alone mobile platform rover which is capable to act as a human assistant to perform various scientific tasks in extreme adversities. The control system of the rover is designed in such a way that it can be commanded from a blind station within 1 kilometer range. It has successfully taken part in 8 th annual University Rover Challenge organized by the Mars Society at the Mars Desert Research Station (MDRS) in the remote, barren desert of southern Utah, USA in late May, 2014. It has been traced out as the first entrance in this competition from Bangladesh and occupied 12 th position out of 31 registered teams from 6 countries of 4 continents. The rover architecture maps the associated components to make it capable to perform the assigned tasks namely -Sample Return Task, Astronaut Assistance Task, Equipment Servicing Task and Terrain Traversing Task. Among these, the first task refers to search for the evidence to identify the existence of life after detailed analysis of collected soil sample from a selected site. In Equipment servicing task, rover has to perform a sequence of operations that mainly includes switching on a compressor and working with a series of pipes, hoses, valves and other such equipment. Astronaut assistance task intends the rover to collect tools from some given GPS locations and then delivery of each of them to the corresponding locations with provided GPS coordinates. Rover has to traverse an adverse terrain in order to pass through a set of target gates for completion of the terrain traversing task. This paper provides a detailed demonstration of the Mongol Barota rover, ins and outs of its architecture, facts and features, system components, logic, logistics and techniques adopted to implement several tasks representing its overall capabilities.