GobhiSet: Dataset of raw, manually, and automatically annotated RGB images across phenology of Brassica oleracea var. Botrytis.

This research introduces an extensive dataset of unprocessed aerial RGB images and orthomosaics of Brassica oleracea crops, captured via a DJI Phantom 4. The dataset, publicly accessible, comprises 244 raw RGB images, acquired over six distinct dates in October and November of 2020 as well as 6 orthomosaics from an experimental farm located in Portici, Italy. The images, uniformly distributed across crop spaces, have undergone both manual and automatic annotations, to facilitate the detection, segmentation, and growth modelling of crops. Manual annotations were performed using bounding boxes via the Visual Geometry Group Image Annotator (VIA) and exported in the Common Objects in Context (COCO) segmentation format. The automated annotations were generated using a framework of Grounding DINO + Segment Anything Model (SAM) facilitated by YOLOv8x-seg pretrained weights obtained after training manually annotated images dated 8 October, 21 October, and 29 October 2020. The automated annotations were archived in Pascal Visual Object Classes (PASCAL VOC) format. Seven classes, designated as Row 1 through Row 7, have been identified for crop labelling. Additional attributes such as individual crop ID and the repetitiveness of individual crop specimens are delineated in the Comma Separated Values (CSV) version of the manual annotation. This dataset not only furnishes annotation information but also assists in the refinement of various machine learning models, thereby contributing significantly to the field of smart agriculture. The transparency and reproducibility of the processes are ensured by making the utilized codes accessible. This research marks a significant stride in leveraging technology for vision-based crop growth monitoring.

RafanoSet: Dataset of raw, manually, and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation.

The rationale for this data article is to provide resources which could facilitate the studies focussed over weed detection and segmentation in precision farming using computer vision. We have curated Multispectral (MS) images over crop fields of Triticum Aestivum containing heterogenous mix of Raphanus raphanistrum in both uniform and random crop spacing. This dataset is designed to facilitate weed detection and segmentation based on manual and automatically annotated Raphanus raphanistrum, commonly known as wild radish. The dataset is publicly available through the Zenodo data library and provides annotated pixel-level information that is crucial for registration and segmentation purposes. The dataset consists of 85 original MS images captured over 17 scenes covering various spectra including Blue, Green, Red, NIR (Near-Infrared), and RedEdge. Each image has a dimension of 1280 × 960 pixels and serves as the basis for the specific weed detection and segmentation. Manual annotations were performed using Visual Geometry Group Image Annotator (VIA) and the results were saved in Common Objects in Context (COCO) segmentation format. To facilitate this resource-intensive task of annotation, a Grounding DINO + Segment Anything Model (SAM) was trained with this manually annotated data to obtain automated Visual Object Classes Extended Markup Language (PASCAL VOC) annotations for 80 MS images. The dataset emphasizes quality control, validating both the 'manual" and 'automated" repositories by extracting and evaluating binary masks. The codes used for these processes are accessible to ensure transparency and reproducibility. This dataset is the first-of-its-kind public resource providing manual and automatically annotated weed information over close-ranged MS images in heterogenous agriculture environment. Researchers and practitioners in the fields of precision agriculture and computer vision can use this dataset to improve MS image registration and segmentation at close range photogrammetry with a focus on wild radish. The dataset not only helps with intra-subject registration to improve segmentation accuracy, but also provides valuable spectral information for training and refining machine learning models.

Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment.

This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing Triticum aestivum crop and Raphanus raphanistrum weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a mAP@0.5 of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances.

Non-potable water reuse and the public health risks from protozoa and helminths: a case study from a city with a semi-arid climate.

The study estimated the risk due to Cryptosporidium, Giardia, and Ascaris, associated with non-potable water reuse in the city of Jaipur, India. The study first determined the exposure dose of Cryptosporidium, Giardia, and Ascaris based on various wastewater treatment technologies for various scenarios of reuse for six wastewater treatment plants (WWTPs) in the city. The exposure scenarios considered were (1) garden irrigation; (2) working and lounging in the garden; and (3) consumption of crops irrigated with recycled water. The estimated annual risk of infection varied between 8.57 × 10-7 and 1.0 for protozoa and helminths, respectively. The order of treatment processes, in decreasing order of annual risk of infection, was found to be: moving-bed bioreactor (MBBR) technology > activated sludge process (ASP) technology > sequencing batch reactor (SBR) technology. The estimated annual risk was found to be in this order: Ascaris > Giardia > Cryptosporidium. The study also estimated the maximum allowable concentration (Cmax) of pathogen in the effluent for a benchmark value of annual infection of risk equal to 1:10,000, the acceptable level of risk used for drinking water. The estimated Cmax values were found to be 6.54 × 10-5, 1.37 × 10-5, and 2.89 × 10-6 (oo) cysts/mL for Cryptosporidium, Giardia, and Ascaris, respectively.

Quantitative Assessment of Microbial Pathogens and Indicators of Wastewater Treatment Performance for Safe and Sustainable Water Reuse in India.

Currently, there is no data on the molecular quantification of microbial indicators of recycled water quality in India. In this study, multiple microbial pathogens and indicators of water quality were evaluated at three wastewater treatment plants located in two Indian cities (New Delhi and Jaipur) to determine the treatment performance and suitability of recycled water for safe and sustainable reuse applications. Real-time polymerase chain reaction (PCR) was used for the rapid evaluation of six human pathogens and six microbial indicators of fecal contamination. Among the microbial indicators, pepper mild mottle virus (PMMoV), F+RNA-GII bacteriophage, Bacteroides thetaiotamicron, and four human pathogens (Norovirus genogroups I & II, Giardia, and Campylobacter coli) were detected in all of the influent samples analyzed. This work suggests that the raw influents contain lower levels of noroviruses and adenoviruses and higher levels of Giardia compared to those reported from other geographic regions. Overall, the efficacy of the removal of microbial targets was over 93% in the final effluent samples, which is consistent with reports from across the world. PMMoV and Giardia were identified as the best microbial targets, from the microbial indicators spanning across bacteria, bacteriophages, DNA/RNA viruses, and protozoan parasites, by which to evaluate treatment performance and recycled water quality in Indian settings, as they were consistently present at high concentrations in untreated wastewater both within and across the sites. Also, they showed a strong correlation with other microbial agents in both the raw influent and in the final effluent. These findings provide valuable insights into the use of culture-independent molecular indicators that can be used to assess the microbial quality of recycled water in Indian settings. IMPORTANCE Wastewater treatment plants (WWTPs) have rapidly increased in India during the last decade. Nonetheless, there are only a few labs in India that can perform culture-based screening for microbial quality. In the last 2 years of the pandemic, India has witnessed a sharp increase in molecular biology labs. Therefore, it is evident that culture-independent real-time PCR will be increasingly used for the assessment of microbial indicators/pathogens in wastewater, especially in resource-limited settings. There is no data available on the molecular quantitation of microbial indicators from India. There is an urgent need to understand and evaluate the performance of widely used microbial indicators via molecular quantitation in Indian WWTPs. Our findings lay the groundwork for the molecular quantitation of microbial indicators in WWTPs in India. We have screened for 12 microbial targets (indicators and human pathogens) and have identified pepper mild mottle virus (PMMoV) and Giardia as the best molecular microbiological indicators in Indian settings.