Remote sensing image classification usually needs many labeled samples so that the target nature can be fully described. For synthetic aperture radar (SAR) images, variations of the target scattering always happen to some extent due to the imaging ge...
Crop type mapping is regarded as an essential part of effective agricultural management. Automated crop type mapping using remote sensing images is preferred for the consistent monitoring of crop types. However, the main obstacle to generating annual...
Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This st...
Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2,...
Despite its high importance for crop yield prediction and monitoring, early-season crop mapping is severely hampered by the absence of timely ground truth. To cope with this issue, this study aims at evaluating the capability of PRISMA hyperspectral...
This work investigates a Sentinel-2 based crop identification methodology for the monitoring of the Common Agricultural Policy’s (CAP) Cross Compliance (CC) and Greening obligations. In this regard, we implemented and evaluated a parcel-based s...
Multitemporal crop classification approaches have demonstrated high performance within a given season. However, cross-season and cross-region crop classification presents a unique transferability challenge. This study addresses this challenge by adop...
Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agr...
Large-scale crop type mapping often requires prediction beyond the environmental settings of the training sites. Shifts in crop phenology, field characteristics, or ecological site conditions in the previously unseen area, may reduce the classificati...
The study presents the preliminary results of two classification exercises assessing the capabilities of pre-operational (August 2015) Sentinel-2 (S2) data for mapping crop types and tree species. In the first case study, an S2 image was used to map...
Accurate crop classification in complex and heterogeneous agricultural landscapes is often challenged by mixed-pixel effects and spatial autocorrelation. This study proposes a prior-guided crop classification framework that integrates accessible Mode...
Accurate crop-type mapping is essential for agricultural monitoring, but pixel-based products often suffer from within-field fragmentation, boundary noise, and limited consistency with field management units. This study developed a parcel-constrained...
Accurate in-season crop type mapping is critical for agricultural monitoring and yield assessment, yet most operational products remain proprietary, post-seasonal or insufficiently tested across contrasting seasons. This study presents an open and tr...
Effective crop monitoring during monsoon growing seasons in Central India faces challenges from persistent cloud cover that limits optical remote sensing during critical agricultural periods. This study presents the first attempt to develop a novel s...
The domestication of animals and the cultivation of crops have been essential to human development throughout history, with the agricultural sector playing a pivotal role. Insufficient nutrition often leads to plant diseases, such as those affecting...
Barley is an ecologically adaptable crop widely used in agriculture and well suited for organic farming. Satellite imagery from Sentinel-2 can support crop monitoring and yield prediction, optimising production processes. This study compares two type...
For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity trader...
Low-cost unmanned aerial vehicle (UAV) visible light remote sensing provides new opportunities for plant community monitoring, but its practical deployment in different ecosystems is still limited by the lack of standardized vegetation index (VI) opt...
Greenhouse cultivation is one of the most crucial circular economy systems in agriculture that allows maximum production with less cultivation area, minimum inputs, and low environmental impact. The data generated in high-tech and sophisticated green...
Non-metallic inclusions are unavoidable defects in steel, and their type, quantity, size, and distribution have a great impact on the quality of steel. At present, non-metallic inclusions are mainly detected manually, which features high work intensi...
A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not r...
Differences in cropland definitions, spatial resolution, and mapping strategies complicate product selection. We harmonized and evaluated eight 2020 cropland and land-cover products across mainland China using hierarchically labeled probability sampl...
Fine land cover classification in an open pit mining area (LCCOM) is essential in analyzing the terrestrial environment. However, researchers have been focusing on obtaining coarse LCCOM while using high spatial resolution remote sensing data and mac...
Rice farming is one of the most important activities in the agriculture sector, producing staple food for the majority of the world's growing population. Accurate and up-to-date assessment of the spatial distribution of rice cultivated area is a key...
Accurate and timely mapping of paddy rice cultivation areas underpins food security assessment, agricultural resource management, and climate change adaptation. Because remote sensing provides repeated, large-scale observations, it has become indispe...
Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this paper, we describe the framework that we developed...
Background: Aspergilloma disease is a fungal mass found in organs such as the sinuses and lungs, caused by the fungus Aspergillus. This disease occurs due to the accumulation of mucus, inflamed cells, and altered blood elements. Various surgical meth...
Recently, the dangers associated with face generation technology have been attracting much attention in image processing and forensic science. The current face anti-spoofing methods based on Generative Adversarial Networks (GANs) suffer from defects...
The increasing need for scalable and efficient crop monitoring systems in industrial plant factories calls for image-based deep learning models that are both accurate and robust to domain variability. This study investigates the feasibility of CNN-ba...
Convolutional Neural Networks (CNNs) such as U-Net have been widely used for medical image segmentation. Dental restorations are prominent features of dental radiographs. Applying U-Net on the panoramic image is challenging, as the shape, size and fr...
Cereal crops are frequently contaminated by deoxynivalenol (DON), a harmful type of mycotoxin produced by several Fusarium species fungi. The early detection of mycotoxin contamination is crucial for ensuring safety and quality of food and feed produ...
Reference labels for crop-type mapping are frequently coarse, and administrative land-use registries such as Japan’s eMAF (electronic Map of Agriculture and Forestry) database routinely group agronomically distinct crops under broad, ambiguous...
The rapid increase in solid waste resulting from rising consumption has increased the need for effective recycling technologies to reduce environmental impacts and recover valuable resources. This study proposes a hybrid approach based on RGB and nea...
Background: Thymoma is a tumor that originates in the thymus gland, a part of the human body located behind the breastbone. It is a malignant disease that is rare in children but more common in adults and usually does not spread outside the thymus. T...
Potato production has become a major global food crop, generating high profitability while reducing the pressure on the world’s food supply chain. Therefore, potato tuber disease inspection for quality control and food security is of utmost imp...
Tea leaf diseases are among the most critical factors affecting the yield and quality of tea harvests. Due to climate change and widespread pesticide use in tea cultivation, these diseases have become more prevalent. As the demand for high-quality te...
Background/Objectives: Artificial intelligence (AI) has been increasingly used to support radiological assessment in dentistry. We benchmarked machine learning (ML), deep learning (DL), and a hybrid optimization-assisted approach for the automatic fi...