no code implementations • 15 Feb 2024 • Saeed Khaki, Jinjin Li, Lan Ma, Liu Yang, Prathap Ramachandra
Finally, we apply DPO with the contrastive samples to align the model to human preference.
no code implementations • 22 Sep 2023 • Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki, Lizhi Wang
This dataset included details on 5, 838 distinct genotypes and daily weather data for a 214-day growing season, enabling comprehensive analysis.
no code implementations • 7 Sep 2023 • Saeed Khaki, Akhouri Abhinav Aditya, Zohar Karnin, Lan Ma, Olivia Pan, Samarth Marudheri Chandrashekar
Our first step involves encoding a sample of production data as the target distribution, and the model training data as the reference distribution.
no code implementations • 29 May 2021 • Mohsen Shahhosseini, Guiping Hu, Saeed Khaki, Sotirios V. Archontoulis
Two scenarios for ensemble creation are considered: homogenous and heterogeneous ensembles.
no code implementations • 4 May 2021 • Amit Kumar Srivastava, Nima Safaei, Saeed Khaki, Gina Lopez, Wenzhi Zeng, Frank Ewert, Thomas Gaiser, Jaber Rahimi
We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results.
no code implementations • 17 Mar 2021 • Saeed Khaki, Nima Safaei, Hieu Pham, Lizhi Wang
To help mitigate this data collection bottleneck in wheat breeding, we propose a novel deep learning framework to accurately and efficiently count wheat heads to aid in the gathering of real-time data for decision making.
no code implementations • 5 Dec 2020 • Saeed Khaki, Hieu Pham, Lizhi Wang
A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields.
no code implementations • 23 Oct 2020 • Saeed Khaki, Hieu Pham, Ye Han, Wade Kent, Lizhi Wang
The future landscape of modern farming and plant breeding is rapidly changing due to the complex needs of our society.
no code implementations • 20 Jul 2020 • Saeed Khaki, Hieu Pham, Ye Han, Andy Kuhl, Wade Kent, Lizhi Wang
In this paper, we propose a novel deep learning method for counting on-ear corn kernels in-field to aid in the gathering of real-time data and, ultimately, to improve decision making to maximize yield.
no code implementations • 30 Jun 2020 • Saeed Khaki, Dan Nettleton
Neural networks are among the most powerful nonlinear models used to address supervised learning problems.
no code implementations • 26 Mar 2020 • Saeed Khaki, Hieu Pham, Ye Han, Andy Kuhl, Wade Kent, Lizhi Wang
The sliding window approach uses a convolutional neural network (CNN) for kernel detection.
1 code implementation • 27 Jan 2020 • Saeed Khaki, Zahra Khalilzadeh, Lizhi Wang
In the 2020 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the historical yield performances of around 4% of total cross combinations of 593 inbreds with 496 testers which were planted in 280 locations between 2016 and 2018 and asked participants to predict the yield performance of cross combinations of inbreds and testers that have not been planted based on the historical yield data collected from crossing other inbreds and testers.
4 code implementations • 20 Nov 2019 • Saeed Khaki, Lizhi Wang, Sotirios V. Archontoulis
Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions.
1 code implementation • 2 Jun 2019 • Saeed Khaki, Zahra Khalilzadeh, Lizhi Wang
Environmental stresses such as drought and heat can cause substantial yield loss in agriculture.
1 code implementation • 7 Feb 2019 • Saeed Khaki, Lizhi Wang
Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions.
Ranked #1 on Crop Yield Prediction on 2018 Syngenta (2016 val)