The agriculture sector is highly vulnerable to natural disasters and climate change, leading
to severe impacts on food security, economic stability, and rural livelihoods. The use of …

The ever-increasing global population presents a looming threat to food production. To meet
growing food demands while minimizing negative impacts on water and soil, agricultural …

Food security is one of the priorities of every country in the World. However, different factors
are making it difficult to meet global targets on food security. Some unprecedented shocks …

This study developed a rapid rice yield estimation workflow and customized yield prediction
model by integrating remote sensing and meteorological data with machine learning (ML) …

An accurate, frequently updated, automatic and reproducible map** procedure to identify
seasonal cultivated crops is a prerequisite for many crop monitoring activities. Deep learning …

Accurate crop classification map is of great significance in various fields such as the survey
of agricultural resource, the analysis of existing circumstance on land application, the yield …

Map** nationwide in-season crop-type data is a significant and challenging task in
agriculture remote sensing. The existing data product for US crop-type planting, such as the …

Crop-specific land cover map** is a vital application in agro-geoinformatics with the
proliferation of remote sensing data and machine learning techniques. This paper presents …

Within-season crop classification using multispectral imagery is an effective way to generate
timely crop maps that can support water and crop management; however, develo** such …

Cyberinformatics tools have supported decision makings in agriculture through cutting-edge
big data, artificial intelligence/machine learning (AI/ML), and high-performance computing …