Study proposes hybrid machine learning models for crop yield prediction.
problem Crop yield prediction for food security.
method Hybrid machine learning models (ANN-ICA and ANN-GWO).
result ANN-GWO model outperformed ANN-ICA in crop yield prediction.
A new model predicts crop yields with high accuracy and uncertainty.
problem Uncertainty in crop yield forecasting due to weather extremes.
method Quantile random forest and Epanechnikov kernel function.
result The model captures crop yields with high coverage probability and provides feature importance.
Deep learning model predicts crop yields using CNN-RNN.
problem Challenges in predicting crop yields due to multiple factors.
method CNN-RNN framework using environmental and management data.
result CNN-RNN model outperformed other methods by 9-8% RMSE.
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and…
Eradicating hunger and malnutrition is a key development goal of the 21st century. We address the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision-making framework. Specifically, we introduce a novel hierarchical machine learning mechanism for predicting c…
Deep learning predicts crop yield integrating genotype and weather data.
problem Improving crop yield prediction for diverse climates.
method Long Short Term Memory - Recurrent Neural Network model with temporal attention mechanism.
result Deep learning models outperform traditional methods for yield prediction.
Weather derivatives help farmers hedge against crop yield risks.
problem High basis risks in weather derivatives pricing models.
method Machine learning ensemble technique to determine yield-weather relationships; mean-reverting model with local temperature dependence.
result Average temperature is the most significant weather variable affecting maize yield.
Deep learning classifies corn hybrids' tolerance to drought and heat.
problem Classifying corn hybrids' tolerance to drought and heat stress.
method Unsupervised deep convolutional neural networks approach.
result 121 hybrids labeled drought tolerant, 193 heat tolerant, 29 both.
Developed deep learning models to predict crop yields across diverse environments.
problem Precise crop yield prediction for improved agricultural practices and climate resilience.
method Integrated weather data, used CNN-DNN and CNN-LSTM-DNN models, applied GEM method.
result Achieved superior performance in crop yield prediction with reduced RMSE and MAE.
Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive …
Crop yield forecasting is the methodology of predicting crop yields prior to harvest. The availability of accurate yield prediction frameworks have enormous implications from multiple standpoints, including impact on the crop commodity futures markets, formulation of agricultural policy, as well as crop insurance ratin…
Project forecasts crop prices to help Indian farmers choose optimal crops for better ROI.
problem Indian farmers struggle to choose crops that fetch decent profits due to lack of scientific decision-making.
method Price forecasting to create an optimal portfolio of crops for better ROI.
result Data-driven decision-making for crop selection leads to higher estimated ROI.
Machine learning models accurately predict maize yield but less so for nitrate loss.
problem Predicting maize yield and nitrate loss for decision-making.
method Evaluation of five machine learning algorithms as meta-models for a cropping systems simulator.
result Random forests most accurately predicted maize yield and nitrate loss.
Study predicts doubling of U.S. maize insurance claims due to climate change.
problem Climate change increases U.S. maize loss probability, impacting insurance claims.
method Neural Network Monte Carlo simulations to predict crop loss metrics.
result Doubling of annual probability of maize Yield Protection insurance claims by mid-century.
Method generates uncertainty measures for street scene segmentation.
problem Reliability and uncertainty measures in semantic segmentation of street scenes.
method Nested crops, neural network segmentation, post-processing, uncertainty heat maps.
result Significant improvements in classification and regression performance.
A neural collaborative filtering method predicts corn hybrid yield performance.
problem Predicting yield performance of untested hybrid combinations in plant breeding.
method Ensemble of matrix factorization and neural networks.
result The model significantly outperformed other models in the Syngenta Crop Challenge.
Botswana's agricultural credit scheme is unsustainable and needs reform.
problem The current scheme is unsustainable and based on flawed actuarial principles.
method Proposes a new subsidy and premium rate setting method considering non-drought years.
result A sustainable agricultural credit guarantee scheme is recommended.
First European crop map created using satellite data.
problem Need for detailed parcel-level crop type mapping for EU.
method Used Sentinel-1 radar observations and LUCAS in-situ data.
result 80.3% overall accuracy for 19 crop types, highest for rape and turnip rape.
The paper uses Tukey g-and-h neural networks for non-Gaussian data regression.
problem Regression with non-Gaussian data.
method Training neural networks to predict Tukey g-and-h distribution parameters via negative log-likelihood minimization.
result Efficiency demonstrated in simulated and real-world datasets.
Method uses aggregate crop statistics to improve satellite-based crop type mapping.
problem Limited field-level crop labels for training satellite-based maps.
method Corrects classifier by accounting for shifts in crop type composition and feature means.
result Substantial improvements in overall classification accuracy, reducing misclassifications by 21.9% on average.
The paper classifies U.S. crop types using hyperspectral satellite imagery.
problem Classifying crop types from hyperspectral satellite imagery.
method Gaussian Bayesian models and neural networks applied to NASA data.
result Bayesian methods outperform standard LDA and QDA.
VDTW improves cross-year crop mapping accuracy.
problem Cross-year crop mapping accuracy is poor with existing methods.
method Vector Dynamic Time Warping (VDTW) for multi-year classification.
result VDTW achieves 99.85% and 99.74% overall accuracies for same and cross years, respectively.
Framework generates realistic crop images for growth modeling.
problem Modeling crop growth over time with precision and detail.
method Two-stage framework: image prediction and growth estimation models.
result Framework accurately predicts crop images with varying conditions.
Paper improves crop classification from low-res satellite images.
problem Accurately classify land use change without high res imagery.
method Capsule layers and distributed attention with LSTM.
result State-of-the-art accuracy on crop type classification.
XAI identifies key time steps for early crop classification.
problem Early crop classification with high accuracy and timeliness.
method Training a baseline model with LRP to identify important time steps.
result Identified a 21st April 2019 to 9th August 2019 timeframe with 0.75% accuracy loss.
Paper presents a machine learning framework for corn yield forecasting.
problem Accurate and timely prediction of corn yields in the US Corn Belt.
method Machine learning ensembles considering complete and partial in-season weather data.
result Ensemble models outperform individual models, achieving best prediction accuracy.
BreizhCrops dataset for crop type mapping from satellite imagery.
problem Classifying field crops from satellite time series data.
method Aggregated Sentinel-2 time series data, compared seven deep neural networks.
result Demonstrated the effectiveness of deep learning models for crop type mapping.
This paper shows how rotating, cropping, and translating images improves a reinforcement learning agent's ability to generalize.
problem Reinforcement learning agents struggle to generalize to slight variations of their training environments.
method The authors investigate the impact of rotation, translation, and cropping on the input representation of reinforcement learning agents.
result Cropped, translated, and rotated observations lead to better generalization in reinforcement learning agents.
The paper proposes a soil pH prediction method using nearest fields.
problem Expensive soil sampling and testing in precision agriculture.
method Spatial radius queries and regression techniques in data mining.
result Predicted soil pH values achieved high accuracy (R_2 values of 0.718 and MAE values of 0.29).
Deep learning predicts crop prices with improved accuracy.
problem Accurate prediction of agricultural crop prices for better decision-making.
method Innovative deep learning approach using GNNs and CNN models.
result At least 20% better performance than previous literature.
Paper detects anomalies in wheat and rapeseed crops using satellite data.
problem Detecting anomalies in crop development at parcel-level.
method Unsupervised outlier detection using SAR and multispectral features.
result Best performance with a 10% outlier ratio, achieving 94.1% true positives for rapeseed and 95.5% for wheat.
Study improves paddy rice yield predictions in Peru using sparse regression and climatic variables.
problem Improving precision of paddy rice yield forecasts in Peru.
method Sparse regression, Elastic-Net regularization, climatic variables, dynamic transformations.
result Improved predictive performance of paddy rice yield forecasts.
CROP verifies clean prefixes in reasoning traces, improving downstream repair accuracy.
problem Uncertainty in reasoning traces prevents full certification of entire responses.
method CROP selects a calibrated threshold to certify the longest prefix with low risk proxies.
result CROP improves downstream repair accuracy by preserving valid reasoning and discarding misleading suffixes.
Intelligent control for greenhouses using deep reinforcement learning.
problem Uncertain nonlinear system of greenhouse environment control.
method Model Embedded Deep Reinforcement Learning (MEDRL) with computer vision and crop growth models.
result Precision and convenience in precise control of greenhouse environment.
New method uses UAV imagery and ML to map crops and weeds.
problem Mapping crops and weeds at high resolution.
method Machine Learning algorithms trained on expert-masked images.
result Maps with >90% identification efficiency at 5m altitude.
Tall wheatgrass outperforms rye in energy and environmental metrics, marginally improving economic viability.
problem Finding sustainable alternatives for marginal agricultural areas.
method Economic assessment using profit margin and Life Cycle Assessment (LCA) for energy and environmental performance.
result Tall wheatgrass shows better environmental and energy performance with reduced inputs and lower energy consumption.
Unsupervised deep learning detects and localizes crop leaf diseases.
problem Automated detection and localization of crop diseases.
method Three types of autoencoders (CAE, CVAE, VQ-VAE) applied to an open-source dataset.
result VQ-VAE autoencoder outperforms in image reconstruction, anomaly removal, detection, and localization.
Paper uses robust GMM to reconstruct missing data in Sentinel-2 images for crop monitoring.
problem Missing data in remote sensing images, especially from multispectral and SAR sensors.
method Robust Gaussian Mixture Models (GMM) with outlier detection using isolation forest.
result Robust GMM outperforms standard GMM in reconstructing imputed values, reducing errors.
TinyBayes detects crop diseases from images on edge devices with high accuracy and minimal resources.
problem Automated disease detection for cocoa crops in resource-constrained settings.
method Combines YOLOv8-Nano for lesion localisation, MobileNetV3-Small for feature extraction, and Jacobi prior for Bayesian classification.
result Achieves 78.7% accuracy on Amini Cocoa Contamination Challenge dataset with 9.5 MB model size and 150 ms inference time.
A looming question that must be solved before robotic plant phenotyping capabilities can have significant impact to crop improvement programs is scalability. High Throughput Phenotyping (HTP) uses robotic technologies to analyze crops in order to determine species with favorable traits, however, the current practices r…
In this paper we propose a class of prior distributions on decomposable graphs, allowing for improved modeling flexibility. While existing methods solely penalize the number of edges, the proposed work empowers practitioners to control clustering, level of separation, and other features of the graph. Emphasis is placed…
This paper presents a method to automatically generate high-quality prediction intervals for neural networks.
problem Accurate uncertainty quantification for deep learning models in real-world applications.
method Dual neural network approach with a novel loss function to balance prediction interval width and coverage.
result Our method produces significantly narrower prediction intervals with higher probability coverage compared to state-of-the-art methods.
Early classification improves satellite-based crop type identification.
problem Accurate early identification of crop types from satellite imagery.
method End-to-end trainable recurrent neural network with an additional stopping probability based on previously seen data.
result The model can distinguish crop types before the end of the vegetative period.
New method fuses optical and SAR data to fill LAI gaps during cloudy periods.
problem Cloudy periods mask key crop growth stages, leading to unreliable yield predictions.
method Multi-Output Gaussian Process (MOGP) regression for fusing Sentinel-1 RVI and Sentinel-2 LAI time series.
result MOGP provides improved LAI estimations even during cloudy periods, especially for long gaps.
BPR matches NN accuracy in crop classification while being more transparent.
problem Lack of auditability and alignment with domain knowledge in neural networks for high-dimensional climate data.
method Bagged polynomial regression with random projections (BPR), averaging many low-degree polynomial models.
result BPR matches neural networks in accuracy but is more transparent.
New algorithm CROP achieves asymptotic optimality with bounded regret.
problem Optimistic algorithms fail to achieve asymptotic instance-dependent regret optimality.
method CRush Optimism with Pessimism (CROP) algorithm that eliminates optimistic hypotheses.
result CROP achieves constant-factor asymptotic optimality and bounded regret.
Analyzing available FAO data from 176 countries over 21 years, we observe an increase of complexity in the international trade of maize, rice, soy, and wheat. A larger number of countries play a role as producers or intermediaries, either for trade or food processing. In consequence, we find that the trade networks bec…
Study improves maize yield prediction using BNs with mixed-effects models.
problem Limited causal inference in agronomic data models.
method Integrates random effects into Bayesian networks, leveraging hierarchical data structure.
result Significantly reduces maize yield prediction error from 28% to 17%.