Crop Classification Research Articles

Pneumatic conveying plant for sorting fruits and root crops

The article deals with the classification of sorting fruits and root crops. When reviewing existing structures, their shortcomings were identified. The authors proposed a new design of the sorting device. The basic principle of operation of this design is based on the creation of a suction air flow inside a metal pipe. The device consists of a container with unsorted fruit, a corrugated plastic pipe of variable length with a tip, a clamp attached to a square metal pipeline. Inside this pipeline there are three rectangular holes of different sizes equipped with gates are fixed. In the lower part of the adapters, cyclones with corrugated plastic pipes of variable length with tips are fixed, descending into the corresponding containers. A protective mesh is installed in the rear part of the metal pipeline and a fan housing is attached, inside which the fan itself is located, driven by an electric motor and creating a suction air flow. The proposed device makes it possible to simplify the design, reduce the consumption of materials, reduce energy consumption, reduce the labor intensity of maintenance, increase productivity, and reduce the degree of damage to the surfaces of sorted fruits and root crops.

Mitigating Incidence Angle Effects in Airborne SAR Time-Series Crop Classification: Integrating Transfer Learning and Variational Mode Decomposition

Mitigating Incidence Angle Effects in Airborne SAR Time-Series Crop Classification: Integrating Transfer Learning and Variational Mode Decomposition

AgrUNet: a multi-GPU UNet based model for crops classification

AgrUNet: a multi-GPU UNet based model for crops classification

HypsLiDNet: 3-D–2-D CNN Model and Spatial–Spectral Morphological Attention for Crop Classification With DESIS and LiDAR Data

HypsLiDNet: 3-D–2-D CNN Model and Spatial–Spectral Morphological Attention for Crop Classification With DESIS and LiDAR Data

Leveraging Hyperspectral Remote Sensing Imaging for Agricultural Crop Classification Using Coot Bird Optimization With Entropy-Based Feature Fusion Model

Leveraging Hyperspectral Remote Sensing Imaging for Agricultural Crop Classification Using Coot Bird Optimization With Entropy-Based Feature Fusion Model

A Multitree Genetic Programming-Based Feature Construction Approach to Crop Classification Using Hyperspectral Images

A Multitree Genetic Programming-Based Feature Construction Approach to Crop Classification Using Hyperspectral Images

Elevating Crop Classification Performance Through CNN-GRU Feature Fusion

Elevating Crop Classification Performance Through CNN-GRU Feature Fusion

Revolutionizing Unmanned Aerial Vehicle Imagery Classification: A Deep Learning Approach Empowered by Computer Vision

Recently, computer vision, unmanned aerial vehicles (UAV) based remote sensing (RS) and deep learning (DL) technologies have been instrumental in global food productivity and future agriculture. UAV provides several advantages over other possible RS platforms like real-time data acquisition, high flexibility, and the best tradeoff between spatial, low cost, small size, spectral, and temporal resolution. One possible advantage of using UAVs for crop classification is that they can efficiently and quickly cover large areas, and could gather data from different angles and at different times. This might assist in providing detailed knowledge of the crops and their conditions. Earlier research is limited to finding a single crop from the RGB images taken by the UAV and hasn’t explored the possibility of multi-crop classification by carrying out DL algorithms. Thus, this study presents a new Automated Crop Type Classification using Adaptive African Vulture Optimization with Deep Learning (ACCT-AAVODL) technique. The ACCT-AAVODL algorithm aims to investigate the UAV images and determine different types of food crops. To accomplish this, the presented ACCT-AAVODL method uses a densely connected network (DenseNet121) for generating feature vectors. Since the trial and error hyper parameter tuning is a challenging task, the AAVO model is employed for hyper parameter optimization. The ACCT-AAVODL technique involves a sparse auto encoder (SAE) with a Nadam optimizer for crop type classification, the stimulation analysis of the ACCT-AAVODL approach on the drone imagery dataset shows the remarkable performance of the ACCT-AAVODL method over other approaches.

Coherence Matrix Power Model for Scattering Variation Representation in Multi-Temporal PolSAR Crop Classification

Coherence Matrix Power Model for Scattering Variation Representation in Multi-Temporal PolSAR Crop Classification

Large-Scale Cotton Classification under Insufficient Sample Conditions Using an Adaptive Feature Network and Sentinel-2 Imagery in Uzbekistan

Cotton (Gossypium hirsutum L.) is one of the main crops in Uzbekistan, which makes a major contribution to the country’s economy. The cotton industry has played a pivotal role in the economic landscape of Uzbekistan for decades, generating employment opportunities and supporting the livelihoods of countless individuals across the country. Therefore, having precise and up-to-date data on cotton cultivation areas is crucial for overseeing and effectively managing cotton fields. Nonetheless, there is currently no extensive, high-resolution approach that is appropriate for mapping cotton fields on a large scale, and it is necessary to address the issues related to the absence of ground-truth data, inadequate resolution, and timeliness. In this study, we introduced an effective approach for automatically mapping cotton fields on a large scale. A crop-type mapping method based on phenology was conducted to map cotton fields across the country. This research affirms the significance of phenological metrics in enhancing the mapping of cotton fields during the growing season in Uzbekistan. We used an adaptive feature-fusion network for crop classification using single-temporal Sentinel-2 images and automatically generated samples. The map achieved an overall accuracy (OA) of 0.947 and a kappa coefficient (KC) of 0.795. This model can be integrated with additional datasets to predict yield based on the identified crop type, thereby enhancing decision-making processes related to supply chain logistics and seasonal production forecasts. The early boll opening stage, occurring approximately a little more than a month before harvest, yielded the most precise identification of cotton fields.

Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery

Crop phenotype observation techniques via UAV (unmanned aerial vehicle) are necessary to identify different winter wheat cultivars to better realize their future smart productions and satisfy the requirement of smart agriculture. This study proposes a UAV-based hyperspectral remote sensing system for the fine classification of different winter wheat cultivars. Firstly, we set 90% heading overlap and 85% side overlap as the optimal flight parameters, which can meet the requirements of following hyperspectral imagery mosaicking and spectral stitching of different winter wheat cultivars areas. Secondly, the mosaicking algorithm of UAV hyperspectral imagery was developed, and the correlation coefficient of stitched spectral curves before and after mosaicking reached 0.97, which induced this study to extract the resultful spectral curves of six different winter wheat cultivars. Finally, the hyperspectral imagery dimension reduction experiments were compared with principal component analysis (PCA), minimum noise fraction rotation (MNF), and independent component analysis (ICA); the winter wheat cultivars classification experiments were compared with support vector machines (SVM), maximum likelihood estimate (MLE), and U-net neural network ENVINet5 model. Different dimension reduction methods and classification methods were compared to get the best combination for classification of different winter wheat cultivars. The results show that the mosaicked hyperspectral imagery effectively retains the original spectral feature information, and type 4 and type 6 winter wheat cultivars have the best classification results with the classification accuracy above 84%. Meanwhile, there is a 30% improvement in classification accuracy after dimension reduction, the MNF dimension reduction combined with ENVINet5 classification result is the best, its overall accuracy and Kappa coefficients are 83% and 0.81, respectively. The results indicate that the UAV-based hyperspectral remote sensing system can potentially be used for classifying different cultivars of winter wheat, and it provides a reference for the classification of crops with weak intra-class differences.

Estimates of irrigation requirements throughout Germany under varying climatic conditions

As climate change brings about hotter and often drier summers, an improved understanding of how irrigation requirements vary according to climatic conditions is of increasing importance. Within Germany, temperate conditions have historically enabled most agriculture to be supplied solely by green water, but recent crop yield reductions and crop failures have demonstrated its increased vulnerability to climatic conditions. The raster-based mGROWA hydrological water balance model was implemented over all agricultural areas in Germany for the period 1961–2020 at a high spatial (200 m) and temporal (daily) resolution. Grid-cells were each assigned one of 10 major crop classes, which account for 86.7 % of all agricultural areas in Germany, and effectively all irrigated areas. Using crop-specific irrigation rules that reflect actual practices, irrigation requirements were simulated for all crop areas. To investigate the relationship between climatic water balance over the crop growing season and irrigation requirements, the simulated annual irrigation requirements were compared with the standardised precipitation-evapotranspiration index (SPEI-6), calculated at the end of September. Through this comparison, irrigation requirements could be characterised for near-normal and dry conditions, and results were aggregated to the district level. Additionally, using district-level data on the areas with irrigation infrastructure, the actual water used for irrigation was estimated. The results highlight marked increases in irrigation requirements in dry conditions compared to near-normal conditions (median increase of 72 %), which are more pronounced over crops in silty soils than in sandy soils. The results also demonstrate how the increased irrigation requirements in dry years are in many cases higher than what is suggested by guidelines for irrigation management in Germany. This study provides important information for actors related to the agricultural sector and water management and is based on a robust and transferable framework to quantify how irrigation requirements vary according to climatic variability and local soil conditions.

Efficient and Sustainable Crop Intensification: An Assessment of Phenofit Algorithm and Envelope Crop Classification Method for its Monitoring

Efficient and Sustainable Crop Intensification: An Assessment of Phenofit Algorithm and Envelope Crop Classification Method for its Monitoring

UNSUPERVISED WINTER WHEAT MAPPING BASED ON MULTI-SPECTRAL AND SYNTHETIC APERTURE RADAR OBSERVATIONS

Abstract. Annual meteorological variations and the impact of climate change in recent years impacted on agricultural production and distribution. Since wheat is a main food resource and the most widely grown crop in the world, it is essential to ensure the sustainability of its production. Therefore, accurate wheat mapping is essential for agricultural production forecasts. Multi-spectral satellite image analysis, including supervised machine learning (ML) methods, has been applied to wheat and other crop mapping, but such passive, optical imaging approaches are strongly influenced by weather conditions and cloud cover, whilst the supervised ML algorithms are highly reliant on manual labelling and ground control data. To avoid the limitation of weather and cloud, this research integrates Sentinel-1 Synthetic Aperture Radar (SAR) data with Sentinel-2 multi-spectral image products to achieve more reliable and accurate winter wheat mapping. Normalised Difference Vegetation Index (NDVI) retrieved from multi-spectral imagery and Sentinel-1’s dual-polarisation radar signals, VV, VH, acquired in different time series, are used as key inputs to an unsupervised ML model based on Dynamic Time Warping (DTW) and hierarchical clustering to prevent time-consuming manual labelling. The chosen study area lies in Norfolk, UK. The result of winter wheat classification with NDVI time series data in this study reaches 72% accuracy, but the improved classification integrating NDVI, VH, VV and VH/VV values achieves 86% accuracy. Future research will focus on optimizing the ML model with multi data integration in addition additional research sites will test more complicated scenarios and multiple crop classification.

Crop Classification in Mountainous Areas Using Object-Oriented Methods and Multi-Source Data: A Case Study of Xishui County, China

Accurate crop mapping can represent the fundamental data for digital agriculture and ecological security. However, current crop classification methods perform poorly in mountainous areas with small cropland field parcel areas and multiple crops under cultivation. This study proposed a new object-oriented classification method to address this issue, using multi-source data and object features to achieve multi-crop classification in mountainous areas. Firstly, a deep learning method was employed to extract cropland field parcels in mountainous areas. Subsequently, the fusion of multi-source data was carried out based on cropland field parcels, while object features tailored for mountainous crops were designed for crop classification. Comparative analysis indicates that the proposed classification method demonstrates exceptional performance, enabling accurate mapping of various crops in mountainous regions. The F1 score and overall accuracy (OA) of the proposed method are 0.8449 and 0.8502, representing a 10% improvement over the pixel-based random forest classification results. Furthermore, qualitative analysis reveals that the proposed method exhibits higher classification accuracy for smaller plots and more precise delineation of crop boundaries. Finally, meticulous crop mapping of corn, sorghum, rice, and other crops in Xishui County, Guizhou Province, demonstrates the significant potential of the proposed method in crop classification within mountainous scenarios.

A Novel Correction Methodology to Improve the Performance of a Low-Cost Hyperspectral Portable Snapshot Camera.

The development of spectral sensors (SSs) capable of retrieving spectral information have opened new opportunities to improve several environmental and agricultural practices, e.g., crop breeding, plant phenotyping, land use monitoring, and crop classification. The SSs are classified as multispectral and hyperspectral (HS) based on the number of the spectral bands resolved and sampled during data acquisition. Large-scale applications of the HS remain limited due to the cost of this type of technology and the technical difficulties in hyperspectral data processing. Low-cost portable hyperspectral cameras (PHCs) have been progressively developed; however, critical aspects associated with data acquisition and processing, such as the presence of spectral discontinuities, signal jumps, and a high level of background noise, were reported. The aim of this work was to analyze and improve the hyperspectral output of a PHC Senop HSC-2 device by developing a general use methodology. Several signal gaps were identified as falls and jumps across the spectral signatures near 513, 650, and 930 nm, while the dark current signal magnitude and variability associated with instrumental noise showed an increasing trend over time. A data correction pipeline was successfully developed and tested, leading to 99% and 74% reductions in radiance signal jumps identified at 650 and 830 nm, respectively, while the impact of noise on the acquired signal was assessed to be in the range of 10% to 15%. The developed methodology can be effectively applied to other low-cost hyperspectral cameras.

DEEP LEARNING BASED MULTI TASK ROAD EXTRACTOR MODEL FOR PARCEL EXTRACTION AND CROP CLASSIFICATION USING KNOWLEDGE BASED NDVI TIME SERIES DATA

Abstract. The role of agriculture in food security is critical by the rapid increase of the global population. Food production should be strongly increased to secure and provide life necessities. Remote sensing provides a fruitful tool in agriculture management, and it provides advantages in terms of saving time and effort. This study aims to examine the feasibility of deep learning edge detection algorithm in order to automatically extract the agricultural parcel boundaries from the open-source Google Earth data. The potential of using Normalized Difference Vegetation Index (NDVI) in crop classification crop was also presented after analyzing the pattern of Rabi major crops (Wheat and Sugarcane) in Haridwar district, Uttarakhand, India. The advantage of Google Earth Engine cloud-based platform was exploited in generating NDVI data from Sentinel-2 satellite between October 2022 and February 2023 in order to save time and effort. To check the accuracy of the deep learning model, the value of the Mean Intersection of Union (mIoU) was tested and reached 0.79.To examine the results, ground truth data were collected in the study area using Unmanned Aerial Vehicles. The overall accuracy of the rule set-based classification reached 91.17%, and the kappa coefficient value was 0.82.

Synergistic exploitation of localized spectral-spatial and temporal information with DNNs for multisensor-multitemporal image-based crop classification

The challenge of performing efficient and reliable crop classification with multisensor multitemporal (MSMT) images in mixed land cover scenarios i.e. presence of small land parcels (area < 20,000-meter square) of crops and other land covers such as built-up or grasslands, is significant. Specially in countries (ex. India) where diverse crops are practiced in small land parcels. This challenge can be addressed if deep neural network (DNN) based models can exploit all three i.e. spatial, spectral, and temporal information of a crop, present in the MSMT images, efficiently and effectively. Therefore, this study presents a novel DNN based model that exploits all three information in a synergistic fashion to achieve improved crop classification. At first, the model increases the significance of local spectral information via a strategy that creates versions of spectral band set wherein neighbourhood of spectral bands is permuted. Then, the model utilizes three-dimensional convolutions, in a time-distributed fashion, to extract local spectral-spatial features. Finally, the model utilizes bidirectional long short-term memory or LSTM-RNNs to extract the temporal information embedded in the time-distributed feature-space created after the convolutions. The developed model is trained and evaluated on Sentinel-1 and Sentinel-2 MSMT data to achieve a 6-class classification including two major crops grown in the region. One of the proposed models namely Perm-3D-CRNN-v1 showed a 97.77 % overall accuracy on test samples and reflected satisfactory on quantitative analysis. The localized spectral-spatial convolutions created prominent class-specific features whereas the bidirectional information flow in the recurrent layer improved the exploitation of crop-phenology type features making the model perform efficiently.

Automatic Crop Classification Based on Optimized Spectral and Textural Indexes Considering Spatial Heterogeneity

Crop recognition with high accuracy at a large scale is hampered by the spatial heterogeneity of crop growth characteristics under the complex influence of environmental conditions. With the aim to automatically realize large-scale crop classification with high accuracy, this study proposes an automatic crop classification strategy considering spatial heterogeneity (ACCSH) by combining the geographic detector technique, random forest average accuracy model, and random forest classification model. In ACCSH, spectral and textural indexes that can quantify crop growth characteristics and environmental variables with potential driving effects are first calculated. Next, an adaptive spatial heterogeneity mining method based on the geographic detector technique is proposed to mine spatial homogeneous zones adaptively with significant differentiation of crop growth characteristics. Subsequently, in view of the differences in crop growth characteristics and key classification indexes between spatial homogeneous zones, correlation analysis, and random forest average accuracy are combined to optimize classification indexes independently within each zone. Finally, random forest is used to classify the target crop in each spatial homogeneous zone separately. The proposed ACCSH is applied to automatically recognize crop types, specifically wheat and corn, in northern France. Results show that kappa coefficients of wheat and corn using ACCSH are 15% and 26% higher than those of classifications at the global scale, respectively. In addition, the index optimization strategy in ACCSH shows apparent superiority. Kappa coefficients of wheat and corn are 5–18% and 9–42% higher than those of classifications based on non-optimized indexes, respectively. In general, ACCSH can automatically realize crop classification with a high precision that suggests its reliability.

Mapping Main Grain Crops and Change Analysis in the West Liaohe River Basin with Limited Samples Based on Google Earth Engine

It is an important issue to explore achieving high accuracy long-term crop classification with limited historical samples. The West Liaohe River Basin (WLRB) serves as a vital agro-pastoral ecotone of Northern China, which experiences significant changes in crop planting structure due to a range of policy. Taking WLRB as a case study, this study constructed multidimensional features for crop classification suitable for Google Earth Engine cloud platform and proposed a method to extract main grain crops using sample augmentation and model migration in case of limited samples. With limited samples in 2017, the method was employed to train and classify crops (maize, soybean, and rice) in other years, and the spatiotemporal changes in the crop planting structure in WLRB from 2014 to 2020 were analyzed. The following conclusions were drawn: (1) Integrating multidimensional features could discriminate subtle differences, and feature optimization could ensure the accuracy and efficiency of classification. (2) By augmenting the original sample size by calculating the similarity of the time series NDVI (normalized difference vegetation index) curves, migrating the random forest model, and reselecting the samples for other years based on the model accuracy scores, it was possible to achieve a high crop classification accuracy with limited samples. (3) The main grain crops in the WLRB were primarily distributed in the northeastern and southern plains with lower elevations. Maize was the most predominant crop type with a wide distribution. The planting area of main grain crops in the WLRB exhibited an increasing trend, and national policies primarily influenced the variations of planting structure in maize and soybean. This study provides a scheme for extracting crop types from limited samples with high accuracy and can be applied for long-term crop monitoring and change analysis to support crop structure adjustment and food security.