852 citations · 2.1k across the 14 of their papers we have counts for
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A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection
Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki +1
Precise crop yield prediction is essential for improving agricultural practices and ensuring crop resilience in varying climates. Integrating weather data across the growing season…
Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
Amit Kumar Srivastava, Nima Safaei, Saeed Khaki +5
Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learnin…
Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach
Saeed Khaki, Zahra Khalilzadeh, Lizhi Wang
Experimental corn hybrids are created in plant breeding programs by crossing two parents, so-called inbred and tester, together. Identification of best parent combinations for cros…
A CNN-RNN Framework for Crop Yield Prediction
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.…
Classification of Crop Tolerance to Heat and Drought: A Deep Convolutional Neural Networks Approach
Saeed Khaki, Zahra Khalilzadeh, Lizhi Wang
Environmental stresses such as drought and heat can cause substantial yield loss in agriculture. As such, hybrid crops that are tolerant to drought and heat stress would produce mo…
Crop Yield Prediction Using Deep Neural Networks
Saeed Khaki, Lizhi Wang
Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understand…