From 3681287f7b3c1d38e7beba6bbb2b2d91746a8385 Mon Sep 17 00:00:00 2001 From: Timilehin Ayanlade Date: Mon, 29 Apr 2024 11:49:41 -0500 Subject: [PATCH] added new bkgnd image --- css/styles.css | 8 +++++--- images/mlcas_bg_all.png | Bin 0 -> 1732070 bytes index.html | 40 +++++++++++++++++++--------------------- 3 files changed, 24 insertions(+), 24 deletions(-) create mode 100644 images/mlcas_bg_all.png 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 { - 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zrT`Tr^Cj6J9SCZ?4)!2eBQemxBZxmRCPA+=>W2b;@&5tL5%XJF-O?=p0000

Sixth International Workshop on Machine Learning for Cyber-Agricultural - Systems
(MLCAS2024)

+ Systems
(MLCAS2024)
Step 1: Register
Pre-Registration Opens Soon @@ -138,27 +138,25 @@
Step 2: Payment (Please u
-
Previous MLCAS workshops: MLCAS2022; MLCAS2021
+
Previous MLCAS workshops: MLCAS2023; MLCAS2022
This workshop is supported by NSF (U.S.A), USDA-NIFA (U.S.A), and JST (Japan)
-

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.

-

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.

+

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.

+

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.

  • @@ -184,8 +182,8 @@
    This workshop is supported by NSF (U.S.A
    Gold Sponsors
    - John Deere Logo - Bayer Logo + John Deere Logo + Bayer Logo