Abstract--The widespread use of Exogenous Organic Matter in agriculture necessitates monitoring to assess its effects on soil and crop health. This study evaluates optical Sentinel-2 satellite imagery for detecting digestate application, a practice that enhances soil fertility but poses environmental risks like mi-croplastic contamination and nitrogen losses. In the first instance, Sentinel-2 satellite image time series (SITS) analysis of specific indices (EOMI, NDVI, EVI) was used to characterize EOM's spectral behavior after application on the soils of four different crop types in Thessaly, Greece. Furthermore, Machine Learning (ML) models (namely Random Forest, k-NN, Gradient Boosting and a Feed-Forward Neural Network), were used to investigate digestate presence detection, achieving F1-scores up to 0.85. Agricultural systems can benefit from the application of Exogenous Organic Matter (EOM), which not only enhances soil fertility but also supports waste recycling and promotes circular economies [1], [2].

In this paper, we presents a novel hierarchical federated learning architecture specifically designed for smart agricultural production systems and crop yield prediction. Our approach introduces a seasonal subscription mechanism where farms join crop-specific clusters at the beginning of each agricultural season. The proposed three-layer architecture consists of individual smart farms at the client level, crop-specific aggregators at the middle layer, and a global model aggregator at the top level. Within each crop cluster, clients collaboratively train specialized models tailored to specific crop types, which are then aggregated to produce a higher-level global model that integrates knowledge across multiple crops. This hierarchical design enables both local specialization for individual crop types and global generalization across diverse agricultural contexts while preserving data privacy and reducing communication overhead. Experiments demonstrate the effectiveness of the proposed system, showing that local and crop-layer models closely follow actual yield patterns with consistent alignment, significantly outperforming standard machine learning models. The results validate the advantages of hierarchical federated learning in the agricultural context, particularly for scenarios involving heterogeneous farming environments and privacy-sensitive agricultural data.

We present the AgriCruiser, an open-source over-the-row agricultural robot developed for low-cost deployment and rapid adaptation across diverse crops and row layouts. The chassis provides an adjustable track width of 1.42 m to 1.57 m, along with a ground clearance of 0.94 m. The AgriCruiser achieves compact pivot turns with radii of 0.71 m to 0.79 m, enabling efficient headland maneuvers. The platform is designed for the integration of the other subsystems, and in this study, a precision spraying system was implemented to assess its effectiveness in weed management. In twelve flax plots, a single robotic spray pass reduced total weed populations (pigweed and Venice mallow) by 24- to 42-fold compared to manual weeding in four flax plots, while also causing less crop damage. Mobility experiments conducted on concrete, asphalt, gravel, grass, and both wet and dry soil confirmed reliable traversal consistent with torque sizing. The complete chassis can be constructed from commodity T-slot extrusion with minimal machining, resulting in a bill of materials costing approximately $5,000 - $6,000, which enables replication and customization. The mentioned results demonstrate that low-cost, reconfigurable over-the-row robots can achieve effective weed management with reduced crop damage and labor requirements, while providing a versatile foundation for phenotyping, sensing, and other agriculture applications. Design files and implementation details are released to accelerate research and adoption of modular agricultural robotics.

Accurate and fine-grained crop yield prediction plays a crucial role in advancing global agriculture. However, the accuracy of pixel-level yield estimation based on satellite remote sensing data has been constrained by the scarcity of ground truth data. To address this challenge, we propose a novel approach called the Multi-Task Crop Yield Prediction Network (MT-CYP-Net). This framework introduces an effective multi-task feature-sharing strategy, where features extracted from a shared backbone network are simultaneously utilized by both crop yield prediction decoders and crop classification decoders with the ability to fuse information between them. This design allows MT-CYP-Net to be trained with extremely sparse crop yield point labels and crop type labels, while still generating detailed pixel-level crop yield maps. Concretely, we collected 1,859 yield point labels along with corresponding crop type labels and satellite images from eight farms in Heilongjiang Province, China, in 2023, covering soybean, maize, and rice crops, and constructed a sparse crop yield label dataset. MT-CYP-Net is compared with three classical machine learning and deep learning benchmark methods in this dataset. Experimental results not only indicate the superiority of MT-CYP-Net compared to previous methods on multiple types of crops but also demonstrate the potential of deep networks on precise pixel-level crop yield prediction, especially with limited data labels.

Fine-grained crop type classification serves as the fundamental basis for large-scale crop mapping and plays a vital role in ensuring food security. It requires simultaneous capture of both phenological dynamics (obtained from multi-temporal satellite data like Sentinel-2) and subtle spectral variations (demanding nanometer-scale spectral resolution from hyperspectral imagery). Research combining these two modalities remains scarce currently due to challenges in hyperspectral data acquisition and crop types annotation costs. To address these issues, we construct a hierarchical hyperspectral crop dataset (H2Crop) by integrating 30m-resolution EnMAP hyperspectral data with Sentinel-2 time series. With over one million annotated field parcels organized in a four-tier crop taxonomy, H2Crop establishes a vital benchmark for fine-grained agricultural crop classification and hyperspectral image processing. We propose a dual-stream Transformer architecture that synergistically processes these modalities. It coordinates two specialized pathways: a spectral-spatial Transformer extracts fine-grained signatures from hyperspectral EnMAP data, while a temporal Swin Transformer extracts crop growth patterns from Sentinel-2 time series. The designed hierarchical classification head with hierarchical fusion then simultaneously delivers multi-level crop type classification across all taxonomic tiers. Experiments demonstrate that adding hyperspectral EnMAP data to Sentinel-2 time series yields a 4.2% average F1-scores improvement (peaking at 6.3%). Extensive comparisons also confirm our method's higher accuracy over existing deep learning approaches for crop type classification and the consistent benefits of hyperspectral data across varying temporal windows and crop change scenarios. Codes and dataset are available at https://github.com/flyakon/H2Crop.

This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Na\"ive Bayes, K-Mean Clustering, and Random Forest. The models, particularly Na\"ive Bayes and Random Forest, demonstrate high effectiveness, as shown through data visualizations. The research concludes that integrating these analytical methods significantly enhances the accuracy and reliability of crop yield predictions, offering vital contributions to agricultural data science.

Accurate crop type maps are an essential source of information for monitoring yield progress at scale, projecting global crop production, and planning effective policies. To date, however, crop type maps remain challenging to create in low and middle-income countries due to a lack of ground truth labels for training machine learning models. Field surveys are the gold standard in terms of accuracy but require an often-prohibitively large amount of time, money, and statistical capacity. In recent years, street-level imagery, such as Google Street View, KartaView, and Mapillary, has become available around the world. Such imagery contains rich information about crop types grown at particular locations and times. In this work, we develop an automated system to generate crop type ground references using deep learning and Google Street View imagery. The method efficiently curates a set of street view images containing crop fields, trains a model to predict crop type by utilizing weakly-labelled images from disparate out-of-domain sources, and combines predicted labels with remote sensing time series to create a wall-to-wall crop type map. We show that, in Thailand, the resulting country-wide map of rice, cassava, maize, and sugarcane achieves an accuracy of 93%. We publicly release the first-ever crop type map for all of Thailand 2022 at 10m-resolution with no gaps. To our knowledge, this is the first time a 10m-resolution, multi-crop map has been created for any smallholder country. As the availability of roadside imagery expands, our pipeline provides a way to map crop types at scale around the globe, especially in underserved smallholder regions.

The continuous increase in global population and the impact of climate change on crop production are expected to affect the food sector significantly. In this context, there is need for timely, large-scale and precise mapping of crops for evidence-based decision making. A key enabler towards this direction are new satellite missions that freely offer big remote sensing data of high spatio-temporal resolution and global coverage. During the previous decade and because of this surge of big Earth observations, deep learning methods have dominated the remote sensing and crop mapping literature. Nevertheless, deep learning models require large amounts of annotated data that are scarce and hard-to-acquire. To address this problem, transfer learning methods can be used to exploit available annotations and enable crop mapping for other regions, crop types and years of inspection. In this work, we have developed and trained a deep learning model for paddy rice detection in South Korea using Sentinel-1 VH time-series. We then fine-tune the model for i) paddy rice detection in France and Spain and ii) barley detection in the Netherlands. Additionally, we propose a modification in the pre-trained weights in order to incorporate extra input features (Sentinel-1 VV). Our approach shows excellent performance when transferring in different areas for the same crop type and rather promising results when transferring in a different area and crop type.

Accurate, detailed, and timely crop type mapping is a very valuable information for the institutions in order to create more accurate policies according to the needs of the citizens. In the last decade, the amount of available data dramatically increased, whether it can come from Remote Sensing (using Copernicus Sentinel-2 data) or directly from the farmers (providing in-situ crop information throughout the years and information on crop rotation). Nevertheless, the majority of the studies are restricted to the use of one modality (Remote Sensing data or crop rotation) and never fuse the Earth Observation data with domain knowledge like crop rotations. Moreover, when they use Earth Observation data they are mainly restrained to one year of data, not taking into account the past years. In this context, we propose to tackle a land use and crop type classification task using three data types, by using a Hierarchical Deep Learning algorithm modeling the crop rotations like a language model, the satellite signals like a speech signal and using the crop distribution as additional context vector. We obtained very promising results compared to classical approaches with significant performances, increasing the Accuracy by 5.1 points in a 28-class setting (.948), and the micro-F1 by 9.6 points in a 10-class setting (.887) using only a set of crop of interests selected by an expert. We finally proposed a data-augmentation technique to allow the model to classify the crop before the end of the season, which works surprisingly well in a multimodal setting.

In this paper we provide classification In this paper we will be working hyperspectral pixel data methods for determining crop type in the USGS collected using the NASA Hyperion satellite [3] and organized GHISACONUS data, which contains around 7,000 pixel spectra and meticulously labeled by the USGS. This data, available from the five major U.S. agricultural crops (winter wheat, online from the USGS as the Global Hyperspectral Imaging rice, corn, soybeans, and cotton) collected by the NASA Spectral-library of Agricultural crops for Conterminous United Hyperion satellite, and includes the spectrum, geolocation, States (GHISACONUS) [4], is a library of 6,988 spectra, each crop type, and stage of growth for each pixel. We apply of which is labeled as one of the five major agricultural crops standard LDA and QDA as well as Bayesian custom versions (e.g., winter wheat, rice, corn, soybeans, and cotton) collected that compute the joint probability of crop type and stage, and between 2008 and 2015. The locations for the spectra in the then the marginal probability for crop type, outperforming GHISACONUS library are shown in Figure 1.