Search Results for author: Saeed Khaki

Found 15 papers, 4 papers with code

A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection

no code implementations22 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.

Crop Yield Prediction Feature Importance

Uncovering Drift in Textual Data: An Unsupervised Method for Detecting and Mitigating Drift in Machine Learning Models

no code implementations7 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.

Corn Yield Prediction with Ensemble CNN-DNN

no code implementations29 May 2021 Mohsen Shahhosseini, Guiping Hu, Saeed Khaki, Sotirios V. Archontoulis

Two scenarios for ensemble creation are considered: homogenous and heterogeneous ensembles.

Management

Winter wheat yield prediction using convolutional neural networks from environmental and phenological data

no code implementations4 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.

BIG-bench Machine Learning Management

WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting

no code implementations17 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.

Decision Making Head Detection

Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning

no code implementations5 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.

Transfer Learning

High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks

no code implementations23 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.

DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation

no code implementations20 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.

Decision Making

Conformal Prediction Intervals for Neural Networks Using Cross Validation

no code implementations30 Jun 2020 Saeed Khaki, Dan Nettleton

Neural networks are among the most powerful nonlinear models used to address supervised learning problems.

Conformal Prediction Prediction Intervals

Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting

no code implementations26 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.

Marketing

Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach

1 code implementation27 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.

Collaborative Filtering

A CNN-RNN Framework for Crop Yield Prediction

4 code implementations20 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.

Crop Yield Prediction Management

Crop Yield Prediction Using Deep Neural Networks

1 code implementation7 Feb 2019 Saeed Khaki, Lizhi Wang

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions.

Crop Yield Prediction feature selection

Cannot find the paper you are looking for? You can Submit a new open access paper.