A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper uses robust GMM to reconstruct missing data in Sentinel-2 images for crop monitoring.
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classi…
First European crop map created using satellite data.
Method uses aggregate crop statistics to improve satellite-based crop type mapping.
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…
Deep learning predicts crop yield integrating genotype and weather data.
In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring. It augments existing classification models by an additional stopping probability based on the previously seen information. This mechanism is end-to-end trainable and derives its stopping decision s…
Project forecasts crop prices to help Indian farmers choose optimal crops for better ROI.
Study proposes hybrid machine learning models for crop yield prediction.
The paper classifies U.S. crop types using hyperspectral satellite imagery.
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…
Framework generates realistic crop images for growth modeling.
A reliable and accurate forecasting model for crop yields is of crucial importance for efficient decision-making process in the agricultural sector. However, due to weather extremes and uncertainties, most forecasting models for crop yield are not reliable and accurate. For measuring the uncertainty and obtaining furth…
XAI identifies key time steps for early crop classification.
This paper shows how rotating, cropping, and translating images improves a reinforcement learning agent's ability to generalize.
Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. However, most crop mapping studies are limited to same-year crop mapping in which the present year's labeled data is used to predict the same year's crop map. Classification accurac…
Deep learning predicts crop prices with improved accuracy.
Neural network model improves leaf spectral reflectance prediction for grapevines.
Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNN) and recurrent neural networks (RNN) for crop yield …
Paper detects anomalies in wheat and rapeseed crops using satellite data.
CROP verifies clean prefixes in reasoning traces, improving downstream repair accuracy.
Intelligent control for greenhouses using deep reinforcement learning.
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 more consistent yields compared to the hybrids that are not tolerant to these stresses. In the 2019 Syngenta Crop Challenge, Syngenta released…
Developed deep learning models to predict crop yields across diverse environments.
New method uses UAV imagery and ML to map crops and weeds.
Model predicts one-year NDVI for Four Corners region.
Tall wheatgrass outperforms rye in energy and environmental metrics, marginally improving economic viability.
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…
Unsupervised deep learning detects and localizes crop leaf diseases.
We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time series in the region of Brittany (Breizh in local language), north-east France. We compare seven r…
Study predicts doubling of U.S. maize insurance claims due to climate change.
In the semantic segmentation of street scenes the reliability of the prediction and therefore uncertainty measures are of highest interest. We present a method that generates for each input image a hierarchy of nested crops around the image center and presents these, all re-scaled to the same size, to a neural network …
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 …
Botswana's agricultural credit scheme is unsustainable and needs reform.
BPR matches NN accuracy in crop classification while being more transparent.
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…
New algorithm CROP achieves asymptotic optimality with 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…
The paper presents a method to improve trust in deep learning models for crop and weed classification.
The effects of weather on agriculture in recent years have become a major global concern. Hence, the need for an effective weather risk management tool (i.e., weather derivatives) that can hedge crop yields against weather uncertainties. However, most smallholder farmers and agricultural stakeholders are unwilling to p…
Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to produce up-to-date crop cover maps. Most time series classificati…
The paper uses Tukey g-and-h neural networks for non-Gaussian data regression.
In developing countries like India agriculture plays an extremely important role in the lives of the population. In India, around 80\% of the population depend on agriculture or its by-products as the primary means for employment. Given large population dependency on agriculture, it becomes extremely important for the …
The paper proposes a soil pH prediction method using nearest fields.
Favorit strategy helps farmers mitigate market price fluctuations.
Study combines VICReg and TNC for better encoding of non-stationary seismic signals.
AI systems that explain their decisions can be monitored for harmful intentions.
TinyBayes detects crop diseases from images on edge devices with high accuracy and minimal resources.