A review of remote sensing for potato traits characterization in precision agriculture

Sun, Chen; Zhou, Jing; Ma, Yuchi; Xu, Yijia; Pan, Bin; Zhang, Zhou

doi:10.3389/fpls.2022.871859

Cited by 13 publications

(2 citation statements)

References 168 publications

(262 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Light Detection and Ranging (LiDAR) can capture 3D data and has been employed for harvest and topographic mapping [51,52]. Nowadays, unmanned aerial vehicles (UAVs) mounted with high-resolution sensors have seen vast applications in precision agriculture [53,54] as they provide high flexibility and permit real-time data acquisition. Therefore, the advancement of sensing technologies revolutionized how someone observes the Earth's surface and delivered strategies for comprehending environmental transformations that may not be noticeable from the ground.…”

Section: Resource-constrained Environmentsmentioning

confidence: 99%

Emerging Technologies for Automation in Environmental Sensing: Review

Borah,

Khanal,

Sundaravadivel

2024

Applied Sciences

View full text Add to dashboard Cite

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.

show abstract

Section: Resource-constrained Environmentsmentioning

confidence: 99%

Emerging Technologies for Automation in Environmental Sensing: Review

Borah,

Khanal,

Sundaravadivel

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Besides PDANN, three other approaches were selected as comparison methods. The first comparison model is the random forest (RF), which is one of the most widely used ML-based yield prediction methods [9,[49][50][51]. We implemented the RF model in Python using the scikit-learn library, a widely-used ML toolkit that provides efficient and user-friendly implementations of various algorithms [52].…”

Section: Experiments Setupmentioning

confidence: 99%

Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach

Ma,

Yang,

Huang

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Over the past few years, there has been extensive exploration of machine learning (ML), especially deep learning (DL), for crop yield prediction, resulting in impressive levels of accuracy. However, such models are highly dependent on training samples with ground truth labels (i.e., crop yield records), which are not available in some regions. Additionally, due to the existence of domain shifts between different spatial regions, DL models trained within one region (i.e., source domain) tend to have poor performance when directly applied to other regions (i.e., target domain). Unsupervised domain adaptation (UDA) has become a promising strategy to improve the transferability of DL models by aligning the feature distributions in the source domain and the target domain. Despite the success, existing UDA models generally assume an identical label space across different domains. This assumption can be invalid in crop yield prediction scenarios, as crop yields can vary significantly in heterogeneous regions. Due to the mismatch between label spaces, negative transfer may occur if the entire source and target domains are forced to align. To address this issue, we proposed a novel partial domain adversarial neural network (PDANN), which relaxes the assumption of fully, equally shared label spaces across domains by downweighing the outlier source samples. Specifically, during model training, the PDANN weighs each labeled source sample based on the likelihood of its yield value given the expected target yield distribution. Instead of aligning the target domain to the entire source domain, the PDANN model downweighs the outlier source samples and performs partial weighted alignment of the target domain to the source domain. As a result, the negative transfer caused by source samples in the outlier label space would be alleviated. In this study, we assessed the model’s performance on predicting yields for two main commodities in the U.S., including corn and soybean, using the U.S. corn belt as the study region. The counties under study were divided into two distinct ecological zones and alternatively used as the source and target domains. Feature variables, including time-series vegetation indices (VIs) and sequential meteorological variables, were collected and aggregated at the county level. Next, the PDANN model was trained with the extracted features and corresponding crop yield records from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated for three testing years from 2019 to 2021. The experimental results showed that the developed PDANN model had achieved a mean coefficient of determination (R2) of 0.70 and 0.67, respectively, in predicting corn and soybean yields, outperforming three other ML and UDA models by a large margin from 6% to 46%. As the first study performing partial domain adaptation for crop yield prediction, this research demonstrates a novel solution for addressing negative transfer and improving DL models’ transferability on crop yield prediction.

show abstract