相关论文: Winter Wheat Crop Yield Prediction on Multiple Het…
Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to…
The use of machine learning (ML) techniques has allowed rapid advancements in many scientific and engineering fields. One of these problems is that of surface soil taxonomy, a research area previously hindered by the reliance on…
International trade policies have recently garnered attention for limiting cross-border exchange of essential goods (e.g. steel, aluminum, soybeans, and beef). Since trade critically affects employment and wages, predicting future patterns…
Due to population increase and import constraints, maize, a key cereal crop in Africa, is experiencing a boom in demand. Given this, the study's focus is on determining how maize output in Nigeria interacts with various climatic factors,…
Rice is the second most important cereal crop worldwide, and the first in terms of number of people who depend on it as a major staple food. Rice blast disease is the most important biotic constraint of rice cultivation causing each year…
Agriculture has always remained an integral part of the world. As the human population keeps on rising, the demand for food also increases, and so is the dependency on the agriculture industry. But in today's scenario, because of low yield,…
Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A small improvement in forecasting performance can lead to increased profitability; therefore, obtaining a realistic…
Data heterogeneity plays a pivotal role in determining the performance of machine learning (ML) systems. Traditional algorithms, which are typically designed to optimize average performance, often overlook the intrinsic diversity within…
Climate change demands effective legislative action to mitigate its impacts. This study explores the application of machine learning (ML) to understand the progression of climate policy from announcement to adoption, focusing on policies…
The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture…
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…
This study explores the application of machine learning-based genetic linguistics for identifying heavy metal response genes in rice (Oryza sativa). By integrating convolutional neural networks and random forest algorithms, we developed a…
Precision agriculture relies heavily on effective weed management to ensure robust crop yields. This study presents RoWeeder, an innovative framework for unsupervised weed mapping that combines crop-row detection with a noise-resilient deep…
Accurate weed management is essential for mitigating significant crop yield losses, necessitating effective weed suppression strategies in agricultural systems. Integrating cover crops (CC) offers multiple benefits, including soil erosion…
Computer vision techniques have attracted a great interest in precision agriculture, recently. The common goal of all computer vision-based precision agriculture tasks is to detect the objects of interest (e.g., crop, weed) and…
50 year-long time series from a Long Term Agronomic Experiment have been used to to investigate the effects of climate change on yields of Wheat and Maize. Trends and fluctuations, useful to estimate production forecasts and related risks…
Our study aims at quantifying the impact of climate change on corn farming in Ontario under several warming scenarios at the 2068 horizon. It is articulated around a discrete-time dynamic model of corn farm income with an annual time-step,…
Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising…
Detection of wheat heads is an important task allowing to estimate pertinent traits including head population density and head characteristics such as sanitary state, size, maturity stage and the presence of awns. Several studies developed…
It is important to predict how the Global Mean Temperature (GMT) will evolve in the next few decades. The ability to predict historical data is a necessary first step toward the actual goal of making long-range forecasts. This paper…