7 pages, this paper is accepted in the 12th ICCCNT 2021 conference at IIT Kharagpur, India. The final version of this paper will appear in the conference proceedings
Precision agriculture system is an arising idea that refers to overseeing farms utilizing current information and communication technologies to improve the quantity and quality of yields while advancing the human work required. The automation requires the assortment of information given by the sensors such as soil, water, light, humidity, temperature for additional information to furnish the operator with exact data to acquire excellent yield to farmers. In this work, a study is proposed that incorporates all common state-of-the-art approaches for precision agriculture use. Technologies like the Internet of Things (IoT) for data collection, machine Learning for crop damage prediction, and deep learning for crop disease detection is used. The data collection using IoT is responsible for the measure of moisture levels for smart irrigation, n, p, k estimations of fertilizers for best yield development. For crop damage prediction, various algorithms like Random Forest (RF), Light gradient boosting machine (LGBM), XGBoost (XGB), Decision Tree (DT) and K Nearest Neighbor (KNN) are used. Subsequently, Pre-Trained Convolutional Neural Network (CNN) models such as VGG16, Resnet50, and DenseNet121 are also trained to check if the crop was tainted with some illness or not.
Internet of Things-Based Smart Precision Farming in Soilless Agriculture: Opportunities and Challenges for Global Food Security
Chitkara University Institute of Engineering and Technology（吉特卡拉大学工程技术学院）；Free University of Bozen-Bolzano (Unibz)（博尔扎诺自由大学）；DBS Global University（DBS全球大学）；IILM University（IILM大学）；SR University（SR大学）；King Saud University（沙特国王大学）
MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT
Hong Kong University of Science and Technology（香港科技大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；U.S. Army DEVCOM Army Research Laboratory（美国陆军DEVCOM陆军研究实验室）
LoRa Communication for Agriculture 4.0: Opportunities, Challenges, and Future Directions
College of Engineering, United Arab Emirates University（阿联酋大学工程学院）；College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；King Abdullah University of Science And Technology（阿卜杜拉国王科技大学）
Farm-LightSeek: An Edge-centric Multimodal Agricultural IoT Data Analytics Framework with Lightweight LLMs
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Engineering, Swinburne University of Technology（斯威本科技大学工程学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；School of Computing, Engineering and Mathematical Sciences, La Trobe University（拉筹伯大学计算、工程与数学科学学院）
Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions
Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校梅塔数据科学与人工智能学院）；Indian Statistical Institute（印度统计研究所）；Department of Applied Mathematics and Scientific Computing, Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校应用数学与科学计算系）；University of Chile（智利大学）；VSB-Technical University of Ostrava（奥斯特拉瓦VSB技术大学）；Department of Mathematics, Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校数学系）
Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review
International Water Research Institute (IWRI)（国际水研究所（IWRI））；Mohammed VI Polytechnic University (UM6P)（穆罕默德六世理工大学（UM6P））；Research and Development Unit in Agriculture and Agri-food of Abitibi-Témiscamingue (URDAAT)（阿比蒂比-泰米斯卡曼格农业与农业食品研发部（URDAAT））；Université du Québec en Abitibi-Témiscamingue（魁北克大学阿比蒂比-泰米斯卡曼格分校）
