Results 11 to 20 of about 163,439 (186)

Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN

Sensors, 2021
Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield.
Liyun Gong, Miao Yu, Shouyong Jiang, Vassilis Cutsuridis, Simon Pearson   +4 more
doaj   +3 more sources

Crop Yield Prediction in Precision Agriculture

Agronomy, 2022
Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield.
Anikó Nyéki, Miklós Neményi
doaj   +2 more sources

Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications

IEEE Access, 2020
Predicting crop yield based on the environmental, soil, water and crop parameters has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features for prediction. Though these methods could resolve the
Dhivya Elavarasan, P. M. Durairaj Vincent   +1 more
doaj   +3 more sources

Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction

International Journal of Applied Earth Observations and Geoinformation, 2020
Large-scale crop yield prediction is critical for early warning of food insecurity, agricultural supply chain management, and economic market. Satellite-based Solar-Induced Chlorophyll Fluorescence (SIF) products have revealed hot spots of photosynthesis
Bin Peng, Kaiyu Guan, Wang Zhou, Chongya Jiang, Christian Frankenberg, Ying Sun, Liyin He, Philipp Köhler   +7 more
doaj   +3 more sources

Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review

Remote Sensing, 2023
Reliable and timely crop-yield prediction and crop mapping are crucial for food security and decision making in the food industry and in agro-environmental management.
Abhasha Joshi, Biswajeet Pradhan, Shilpa Gite, Subrata Chakraborty   +3 more
doaj   +3 more sources

A Hybrid Approach to Tea Crop Yield Prediction Using Simulation Models and Machine Learning. [PDF]

Plants (Basel), 2022
Batool D, Shahbaz M, Shahzad Asif H, Shaukat K, Alam TM, Hameed IA, Ramzan Z, Waheed A, Aljuaid H, Luo S.   +9 more
europepmc   +2 more sources

Crop Yield Prediction

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021
Cotton, popularly known as White Gold has been an important commercial crop of National significance due to the immense influence of its rural economy. Transfer of technology to identify the quality of fibre is gaining importance for crop yield is compared with Random forest, Support Vector Machine, Weather, K Nearest neighbor.
Pallavi Shankarrao Mahore, Dr. Aashish A. Bardekar   +1 more
openaire   +1 more source

Crop Yield Prediction Using Machine-Learning

Journal of Innovative Computing and Emerging Technologies
This study examines the utilization of machine learning techniques for forecasting agricultural yields. This is critical because it helps to improve farm productivity and food security. Agriculture is the backbone of Pakistan’s economy, and accurate crop yield prediction can greatly assist in economic planning and allocation of resources.
null Mr. Laxmikant Malphedwar, null Dr. Alpana Adsul, null Mr. Sanket Nagare, null Mr. Yash Nimse, null Mrs. Sanchiti Nimble, null Mrs. Sakshi Pakhle   +5 more
  +8 more sources

Predicting Maize Yield at the Plot Scale of Different Fertilizer Systems by Multi-Source Data and Machine Learning Methods

Remote Sensing, 2021
Timely and reliable maize yield prediction is essential for the agricultural supply chain and food security. Previous studies using either climate or satellite data or both to build empirical or statistical models have prevailed for decades.
Linghua Meng, Huanjun Liu, Susan L. Ustin, Xinle Zhang   +3 more
doaj   +1 more source

Enhancing the utility of daily GCM rainfall for crop yield prediction [PDF]

, 2009
Global climate models (GCMs) are promising for crop yield predictions because of their ability to simulate seasonal climate in advance of the growing season.
Ines, Amor Valeriano M., Hansen, James W., Robertson, Andrew W.   +2 more
core   +4 more sources