general update

config.yml

@@ -31,7 +31,7 @@ menu:
           weight: 3
 
 params:
-    description: "I am a researcher currently pursuing my PhD in Precision Agriculture at North Dakota State University."
+    description: "I am a tech enthusiast working as a postdoctoral researcher at North Dakota State University"
     author: Billy G. Ram
     # googleAnalyticsID: "G-97G4MZ4061"
     DateFormat: "January 2006"
@@ -56,11 +56,11 @@ params:
     profileMode:
           enabled: true
           title: Billy G. Ram
-          subtitle: "Hey there! I’m a tech enthusiast working as a postdoctoral researcher at North Dakota State University.
-          My current research involves hyperspectral imaging, artificial intelligence and precision agriculture. I also work with drones (UAVs), robots and systems for high throughtput phenotyping.
+          subtitle: "Hey there! I am a technology enthusiast working as a postdoctoral researcher at North Dakota State University.
+          My current research involves hyperspectral imaging, artificial intelligence and robotics. I also work with drones (UAVs), robots and systems for high throughtput phenotyping.
           I believe in building things from scratch that can help the community at large.
           My hobbies are photography, cimematography and anything that can invoke an intellectual thought in me."
-          imageUrl: "/picture.jpg"
+          imageUrl: "/picture.png"
           imageWidth: 160
           imageHeight: 160
           imageTitle: Billy Graham Ram

content/papers/6.md

@@ -1,53 +1,60 @@
 ---
 title: " Systematic Review of Hyperspectral Imaging in Precision Agriculture: Analysis of its Current State and Future Prospects " 
-date: 2023-09-12
+date: 2024-07-01
 # url: /1/
 # aliases: 
 #     - /static/1/1.pdf
 #     - /static/1/1.jpg
 
 tags: ["classification", "machine learning", "weed identification","deep learning", "GPU", "hyperspectral", "review"]
-author: [ "<span style='color: blue; font-weight: bold;'>Ram, B.G.</span>", "Oduor, P.", "Igathinathane, C.", "Howatt, K.", "Sun, X." , "<span style='color: green; font-weight: bold;'><i> under submission.</i></span>" ]
+author: [ "<span style='color: blue; font-weight: bold;'>Ram, B.G.</span>", "Oduor, P.", "Igathinathane, C.", "Howatt, K.", "Sun, X." ]
 
 
-description: "This research focused on the development and comparison of nine supervised machine-learning models for data analysis. A hyperspectral data collection platform was created specifically for field data collection, enhancing the accuracy and efficiency of the process. The study also examined the effectiveness of Matthews Correlation Coefficient (MCC) and F1 scores in handling imbalanced datasets. After rigorous training and comparison, a Quadratic Discriminant Classifier was chosen due to its impressive F1 score of 0.95 and MCC of 0.85, demonstrating its superior performance in classifying and predicting data." 
+description: "This research paper provides a comprehensive overview of the latest advancements and trends in hyperspectral imaging (HSI) for real-time applications in precision agriculture. HSI, which captures detailed spectral information about objects, has the potential to revolutionize farming practices by enabling accurate and timely detection of issues like diseases, weeds, crop stress, and nutrient deficiencies. However, realizing the full potential of HSI requires overcoming challenges related to data processing, analysis, and hardware implementation." 
 
-summary: "This research focused on the development and comparison of nine supervised machine-learning models for data analysis. A hyperspectral data collection platform was created specifically for field data collection, enhancing the accuracy and efficiency of the process. The study also examined the effectiveness of Matthews Correlation Coefficient (MCC) and F1 scores in handling imbalanced datasets. After rigorous training and comparison, a Quadratic Discriminant Classifier was chosen due to its impressive F1 score of 0.95 and MCC of 0.85, demonstrating its superior performance in classifying and predicting data." 
+summary: "The paper conducted a systematic review of 97 scientific articles published between 2003 and 2023 to analyze current practices and identify future research directions. Key areas explored include data preprocessing techniques, hyperspectral data acquisition, data compression methods, segmentation, and the role of hardware accelerators like FPGAs and GPUs in speeding up data processing." 
 cover:
     # image: "1.jpg"
-    alt: "Palmer amaranth Identification using Hyperspectral Imaging and Machine Learning Technologies in Soybean Field."
+    alt: "Systematic Review of Hyperspectral Imaging in Precision Agriculture: Analysis of its Current State and Future Prospects"
     relative: false
 editPost:
-    URL: "https://www.sciencedirect.com/journal/biosystems-engineering"
-    Text: "Biosystems Engineering (under submission)"
+    URL: "https://doi.org/10.1016/j.compag.2024.109037"
+    Text: "Computer and Electronics in Agriculture"
 
 
 ---
 
 ##### Download
 
-<!-- + [Paper will be available after submission](p3.pdf) -->
-+ _Paper will be available after submission._
++ [Paper](p6.pdf)
+
 
 
 
 ---
 
 ##### Abstract
 
-With the advent of precision agriculture, data is seen as a valuable resource for optimizing crop yields and improving farming practices. Different sensors provide distinct and valuable perspectives on plant behavior with the help of data. Hyperspectral imaging is data rich in solving agricultural problems like disease detection, weed detection, stress detection, crop monitoring, nutrient application, soil mineralogy, yield estimation, and sorting applications. With modern precision agriculture, the challenge now is to bring these applications to the field for real-time solutions, where machines are enabled to carry out these analyses without expert supervision and communicate the results to users for better management of farmlands; a necessary step to gain complete autonomy in agriculture. Significant advancements in hyperspectral imaging technology for precision agriculture are required to fully realize its potential. As a wide-ranging collection of the status of hyperspectral imaging and analysis in precision agriculture is lacking, this review endeavors to provide a comprehensive overview of the recent advancements and trends in high spatial resolution hyperspectral imaging for precision agriculture for real-time applications. In this study, a systematic review of 163 scientific articles published over the past twenty years (2003-2023) was conducted. Of these, 97 were selected for further analysis based on their relevance to the topic at hand. Topics include conventional data preprocessing techniques, hyperspectral data acquisition, data compression methods, and segmentation methods. The hardware implementation of field-programmable gate arrays (FPGAs) and graphics processing units (GPUs) for high-speed data processing and application of machine learning and deep learning technologies were explored. This review highlights the potential of hyperspectral imaging as a powerful tool for precision agriculture, particularly in real-time applications, discusses limitations, and provides insights into future research directions.
+Hyperspectral sensor adaptability in precision agriculture to digital images is still at its nascent stage. Hyperspectral imaging (HSI) is data rich in solving agricultural problems like disease detection, weed detection, stress detection, crop monitoring, nutrient application, soil mineralogy, yield estimation, and sorting applications. With modern precision agriculture, the challenge now is to bring these applications to the field for real-time solutions, where machines are enabled to conduct analyses without expert supervision and communicate the results to users for better management of farmlands; a necessary step to gain complete autonomy in agricultural farmlands. Significant advancements in HSI technology for precision agriculture are required to fully realize its potential. As a wide-ranging collection of the status of HSI and analysis in precision agriculture is lacking, this review endeavors to provide a comprehensive overview of the recent advancements and trends of HSI in precision agriculture for real-time applications. In this study, a systematic review of 163 scientific articles published over the past twenty years (2003–2023) was conducted. Of these, 97 were selected for further analysis based on their relevance to the topic at hand. Topics include conventional data preprocessing techniques, hyperspectral data acquisition, data compression methods, and segmentation methods. The hardware implementation of field-programmable gate arrays (FPGAs) and graphics processing units (GPUs) for high-speed data processing and application of machine learning and deep learning technologies were explored. This review highlights the potential of HSI as a powerful tool for precision agriculture, particularly in real-time applications, discusses limitations, and provides insights into future research directions.
 
-<!-- ---
+---
 
-##### Figure 1: Commercial robots that use computer vision techniques to perform in-field weeding tasks. 
+##### Figure 1: Flowchart illustrating the outline of the systematic literature review study. 
 
-![](p3.png) -->
+![](p6_flowchart.png)
 
 ---
 
+##### Figure 2: Literature screening process according to PRISMA guidelines (Haddaway et al., 2022). 
+
+![](p6_prisma.png)
+
+---
+
+
 ##### Citation
 
-_Citation will be updated after submission._
+Ram, B. G., Oduor, P., Igathinathane, C., Howatt, K., & Sun, X. (2024). A systematic review of hyperspectral imaging in precision agriculture: Analysis of its current state and future prospects. Computers and Electronics in Agriculture, 222, 109037.
 
 ---
 

content/papers/_index.md

@@ -1,5 +1,5 @@
 ---
 title: "Papers"
 aliases: /archive/
-description: "Preprints and articles on unemployment, economic slack, business cycles, monetary policy, fiscal policy, and science-related topics."
+description: "Preprints and articles on hyperspectral imaging, precision agriculture, and robotics."
 ---