diff --git a/css/styles.css b/css/styles.css
index aab9152..f441b06 100644
--- a/css/styles.css
+++ b/css/styles.css
@@ -521,7 +521,7 @@ input[type='checkbox'] {
 .header {
 	padding-top: 7rem;
 	padding-bottom: 5.5rem;
-	background: linear-gradient(rgba(0, 0, 0, 0.5), rgba(0, 0, 0, 0.3)), url('../images/header-background.jpg') center center no-repeat;
+	background: linear-gradient(rgba(0, 0, 0, 0.5), rgba(0, 0, 0, 0.3)), url('../images/mlcas_bg_all.png') center center no-repeat;
 	background-size: cover;
 	text-align: center;
 }
@@ -997,8 +997,10 @@ a:hover.back-to-top {
 
 	/* Header */
 	.header {
-		padding-top: 11rem;
-		padding-bottom: 9rem;
+		/* padding-top: 11rem;
+		padding-bottom: 9rem; */
+		padding-top: 7rem;
+		padding-bottom: 5rem;
 	}
 	/* end of header */
 
diff --git a/images/mlcas_bg_all.png b/images/mlcas_bg_all.png
new file mode 100644
index 0000000..f3416d9
Binary files /dev/null and b/images/mlcas_bg_all.png differ
diff --git a/index.html b/index.html
index e960470..eaebd42 100644
--- a/index.html
+++ b/index.html
@@ -104,7 +104,7 @@
                 <div class="col-lg-10">
                     <div class="text-container">
                         <h3 class="h1-large">Sixth International Workshop on Machine Learning for Cyber-Agricultural
-                            Systems <br>(MLCAS2024)</h3>
+                            Systems <br>(MLCAS2024)</h3> 
                         <!-- <h5 class="text-warning">Step 1: Register</h5><a class="btn-solid-lg page-scroll" href="https://forms.gle/CM497FP3jGVY2JRb8" target="_blank">Pre-Registration</a> TOBEDONE-->
                         <h5 class="text-warning">Step 1: Register</h5><a class="btn-solid-lg page-scroll" href="" target="_blank">Pre-Registration Opens Soon</a>
                     </div> <!-- end of text-container -->
@@ -138,27 +138,25 @@ <h5 class="text-warning mt-3">Step 2: Payment <span class="text-white">(Please u
         <div class="container">
             <div class="row">
                 <div class="col-xl-10 offset-xl-1">
-                    <h5 class="text-center text-muted mt-2">Previous MLCAS workshops: <a href="https://2023.mlcas.site/" target="_blank">MLCAS2022</a>; <a href="https://mlcas2022.github.io/" target="_blank">MLCAS2021</a></h5>
+                    <h5 class="text-center text-muted mt-2">Previous MLCAS workshops: <a href="https://2023.mlcas.site/" target="_blank">MLCAS2023</a>; <a href="https://mlcas2022.github.io/" target="_blank">MLCAS2022</a></h5>
 
                     <h5 class="text-center text-muted mt-2">This workshop is supported by NSF (U.S.A), USDA-NIFA (U.S.A), and JST (Japan)</h5>
 
-                    <p class="mt-2">Today, efficient and cost-effective sensors as well as high performance
-                        computing technologies are looking to transform traditional plant-based agriculture into an
-                        efficient cyber-physical system. The easy availability of cheap, deployable, connected
-                        sensor technology has created an enormous opportunity to collect vast amount of data at
-                        varying spatial and temporal scales at both experimental and production agriculture levels.
-                        Therefore, both offline and real-time agricultural analytics that assimilates such
-                        heterogeneous data and provides automated, actionable information is a critical needed for
-                        sustainable and profitable agriculture.</p>
-                    <p class="mb-3">Data analytics and decision-making for Agriculture has been a
-                        long-standing application area. The application of advanced machine learning methods to this
-                        critical societal need can be viewed as a transformative extension for the agriculture
-                        community. In this workshop, we intend to bring together academic and industrial researchers
-                        and practitioners in the fields of machine learning, data science and engineering, plant
-                        sciences and agriculture, in the collaborative effort of identifying and discussing major
-                        technical challenges and recent results related to machine learning-based approaches. It
-                        will feature invited talks, oral/poster presentation of accepted papers, and a panel
-                        discussion. </p>
+                    <p class="mt-2">Today, efficient, cost-effective sensors as well as high performance computing technologies 
+                        are looking to transform traditional plant-based agriculture into an efficient cyber-physical system. The 
+                        easy availability of cheap, deployable, connected sensor technology has created an enormous opportunity to 
+                        collect vast amounts of data at varying spatial and temporal scales at both experimental and production 
+                        agriculture levels. Therefore, both offline and real-time agricultural analytics that assimilates such 
+                        heterogeneous data and provides automated, actionable information is a critical need for sustainable and 
+                        profitable agriculture.</p>
+                    <p class="mb-3">Data analytics and decision-making for Agriculture has been a long-standing application area. 
+                        The application of advanced Artificial Intelligence (AI) and Machine Learning (ML) methods to this critical 
+                        societal need can be viewed as a transformative extension for the agriculture community. In this workshop, 
+                        we intend to bring together academic and industrial researchers and practitioners in the fields of machine 
+                        learning, data science and engineering, plant sciences and agriculture, in the collaborative effort of 
+                        identifying and discussing major technical challenges and recent results related to machine learning-based 
+                        approaches. It will feature invited talks, oral/poster presentations of accepted papers, and an Ag-ML 
+                        competition. </p>
 
                     <ul class="list-unstyled li-space-lg mb-3">
                         <li class="media">
@@ -184,8 +182,8 @@ <h5 class="text-center text-muted mt-2">This workshop is supported by NSF (U.S.A
                         <h5 class="text-center text-warning mt-3"> Gold Sponsors</h5>
                         <div class="icons-container">
                             <div class="d-flex justify-content-center">
-                                <img src="https://mlcas2022.github.io/images/jd_logo.png" alt="John Deere Logo" height="100px" class="m-2" onclick="javascript:window.open('https://www.deere.com/en/', '_blank');">
-                                <img src="https://mlcas2022.github.io/images/bayer_logo.svg" alt="Bayer Logo" height="100px" class="m-2" onclick="javascript:window.open('https://www.bayer.com/en/', '_blank');">
+                                <img src="images/jd_logo.png" alt="John Deere Logo" height="100px" class="m-2" onclick="javascript:window.open('https://www.deere.com/en/', '_blank');">
+                                <img src="images/bayer_logo.svg" alt="Bayer Logo" height="100px" class="m-2" onclick="javascript:window.open('https://www.bayer.com/en/', '_blank');">
                             </div>
                         </div>
                     </div>