Cyber-Agricultural Systems for Crop Breeding and Sustainable Production

The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing Climate. Finally, We Identify Underexplored and Promising Future Directions for CAS Research and Development

The Gulf of Mexico in trouble: Big data solutions to climate change science

The latest technological advancements in the development and production of sensors have led to their increased usage in marine science, thus expanding data volume and rates within the field. The extensive data collection efforts to monitor and maintain the health of marine environments supports the efforts in data driven learning, which can help policy makers in making effective decisions. Machine learning techniques show a lot of promise for improving the quality and scope of marine research by detecting implicit patterns and hidden trends, especially in big datasets that are difficult to analyze with traditional methods. Machine learning is extensively used on marine science data collected in various regions, but it has not been applied in a significant way to data generated in the Gulf of Mexico (GOM). Machine learning methods using ocean science data are showing encouraging results and thus are drawing interest from data science researchers and marine scientists to further the research. The purpose of this paper is to review the existing approaches in studying GOM data, the state of the art in machine learning techniques as applied to the GOM, and propose solutions to GOM data problems. We review several issues faced by marine environments in GOM in addition to climate change and its effects. We also present machine learning techniques and methods used elsewhere to address similar problems and propose applications to problems in the GOM. We find that Harmful Algal Blooms (HABs), hypoxia, and sea-level rises have not received as much attention as other climate change problems and within the machine learning literature, the impacts on estuaries and coastal systems, as well as oyster mortality (also major problems for the GOM) have been understudied – we identify those as important areas for improvement. We anticipate this manuscript will act as a baseline for data science researchers and marine scientists to solve problems in the GOM collaboratively and/or independently

Cyber-agricultural systems for crop breeding and sustainable production

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS – sensing, modeling, and actuation – and the emerging concept of agricultural digital twins (DTs). We also discuss how scalable CI is becoming a key enabler of smart agriculture. In this review we shed light on the significance of CAS in revolutionizing crop breeding and production by enhancing efficiency, productivity, sustainability, and resilience to changing climate. Finally, we identify underexplored and promising future directions for CAS research and development.This article is published as Sarkar, S., Ganapathysubramanian, B., Singh, A., Fotouhi Ardakani, F., Kar, S., Nagasubramanian, K., Chowdhary, G., Das, S.K., Kantor, G., Krishnamurthy, A., Merchant, N., Singh, A.K. Cyber-agricultural systems for crop breeding and sustainable production. Trends in Plant Science. PECIAL ISSUE: 21ST CENTURY TOOLS IN PLANT SCIENCE. August 28, 2023 https://doi.org/10.1016/j.tplants.2023.08.001. Posted with permission. © 2023 The Authors. Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0

Self-supervised learning improves classification of agriculturally important insect pests in plants

Insect pests cause significant damage to food production, so early detection and efficient mitigation strategies are crucial. There is a continual shift toward machine learning (ML)-based approaches for automating agricultural pest detection. Although supervised learning has achieved remarkable progress in this regard, it is impeded by the need for significant expert involvement in labeling the data used for model training. This makes real-world applications tedious and oftentimes infeasible. Recently, self-supervised learning (SSL) approaches have provided a viable alternative to training ML models with minimal annotations. Here, we present an SSL approach to classify 22 insect pests. The framework was assessed on raw and segmented field-captured images using three different SSL methods, Nearest Neighbor Contrastive Learning of Visual Representations (NNCLR), Bootstrap Your Own Latent, and Barlow Twins. SSL pre-training was done on ResNet-18 and ResNet-50 models using all three SSL methods on the original RGB images and foreground segmented images. The performance of SSL pre-training methods was evaluated using linear probing of SSL representations and end-to-end fine-tuning approaches. The SSL-pre-trained convolutional neural network models were able to perform annotation-efficient classification. NNCLR was the best performing SSL method for both linear and full model fine-tuning. With just 5% annotated images, transfer learning with ImageNet initialization obtained 74% accuracy, whereas NNCLR achieved an improved classification accuracy of 79% for end-to-end fine-tuning. Models created using SSL pre-training consistently performed better, especially under very low annotation, and were robust to object class imbalances. These approaches help overcome annotation bottlenecks and are resource efficient.This article is published as Kar, S., Nagasubramanian, K., Elango, D., Carroll, M. E., Abel, C. A., Nair, A., Mueller, D. S., O’Neal, M. E., Singh, A. K., Sarkar, S., Ganapathysubramanian, B., & Singh, A. (2023). Self-supervised learning improves classification of agriculturally important insect pests in plants. The Plant Phenome Journal, 6, e20079. https://doi.org/10.1002/ppj2.20079.Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted

Self‐supervised learning improves classification of agriculturally important insect pests in plants

Abstract Insect pests cause significant damage to food production, so early detection and efficient mitigation strategies are crucial. There is a continual shift toward machine learning (ML)‐based approaches for automating agricultural pest detection. Although supervised learning has achieved remarkable progress in this regard, it is impeded by the need for significant expert involvement in labeling the data used for model training. This makes real‐world applications tedious and oftentimes infeasible. Recently, self‐supervised learning (SSL) approaches have provided a viable alternative to training ML models with minimal annotations. Here, we present an SSL approach to classify 22 insect pests. The framework was assessed on raw and segmented field‐captured images using three different SSL methods, Nearest Neighbor Contrastive Learning of Visual Representations (NNCLR), Bootstrap Your Own Latent, and Barlow Twins. SSL pre‐training was done on ResNet‐18 and ResNet‐50 models using all three SSL methods on the original RGB images and foreground segmented images. The performance of SSL pre‐training methods was evaluated using linear probing of SSL representations and end‐to‐end fine‐tuning approaches. The SSL‐pre‐trained convolutional neural network models were able to perform annotation‐efficient classification. NNCLR was the best performing SSL method for both linear and full model fine‐tuning. With just 5% annotated images, transfer learning with ImageNet initialization obtained 74% accuracy, whereas NNCLR achieved an improved classification accuracy of 79% for end‐to‐end fine‐tuning. Models created using SSL pre‐training consistently performed better, especially under very low annotation, and were robust to object class imbalances. These approaches help overcome annotation bottlenecks and are resource efficient