Crop Monitoring Research Articles

Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning

Cover cropping between cash crop growing seasons is a multifunctional conservation practice. Timely and accurate monitoring of cover crop traits, notably aboveground biomass and nutrient content, is beneficial to agricultural stakeholders to improve management and understand outcomes. Currently, there is a scarcity of spatially and temporally resolved information for assessing cover crop growth. Remote sensing has a high potential to fill this need, but conventional empirical regression operated with coarse-resolution multispectral data has large uncertainties. Therefore, this study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring. Specifically, we deployed an airborne hyperspectral system covering visible to shortwave-infrared wavelengths (400–2400 nm) to acquire high spatial (0.5 m) and spectral (3–5 nm) resolution reflectance over 23 cover crop fields across Central Illinois in March and April of 2021. Airborne hyperspectral surface reflectance with high spectral and spatial resolution can be well matched with field data to quantify cover crop traits. Furthermore, the PGML models were pre-trained by synthetic data from soil-vegetation radiative transfer modeling (one million records), and then fine-tuned with field data of cover crop biomass and nutrient content. Results show that airborne hyperspectral data with PGML can achieve high accuracy to predict cover crop aboveground biomass (R2 = 0.72, relative RMSE = 15.16%) and nitrogen content (R2 = 0.69, relative RMSE = 16.59%) through leave-one-field-out cross-validation. Unlike the pure data-driven approach (e.g., partial least-squares regression), PGML incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. Meanwhile, with field data for model fine-tuning, PGML predicted biomass more accurately than the inversion of radiative transfer models. We also found that the red edge has a high contribution in quantifying aboveground biomass and nitrogen content, followed by green and shortwave spectra. This study demonstrated the first attempt of utilizing hyperspectral remote sensing to accurately quantify cover crop traits. We highlight the strength of PGML in exploiting sensing data to quantify ecosystem variables to advance agroecosystem monitoring for sustainable agricultural management.

Plant Disease Detection Using Advanced Convolutional Neural Networks with Region of Interest Awareness

The advent of deep learning has paved way for more efficient computer vision applications. In agricultural crop monitoring with technology driven approaches, it is indispensable to have plant disease identification. Recent research reveals that Convolutional Neural Network (CNN) is most suitable deep learning method to process leaf images for detecting diseases. As symptoms of leaf disease appear in specific area, considering entire leaf for processing incurs more computational cost and time besides deteriorating performance due to inadequate quality of training. To overcome this problem, we proposed a framework that considers extraction of ROI using deep CNN prior to prediction of pre-trained deep learning models such as VGG13, ResNet34, DenseNet19, AlexNet, Sqeezenet1_1 and Inception_v3. An algorithm named ROI Feature Map Creation (ROI-FMC) is defined to extract ROI for given input image. This will be given as input to another algorithm proposed namely ROI based Deep CNN with Transfer Learning for Leaf Disease Prediction (ROIDCNN-LDP). The latter is used to predict leaf diseases. PlantVillage dataset is used for empirical study. The experimental results revealed that with ROI awareness, all models could perform well. However, Inception_v3 is the deep CNN model that outperforms other models.

Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring on Remote Sensing Images

Abstract Crop monitoring is a process that involves regular field visits that seem to be difficult since it needs a huge amount of time and manpower. Thus, in modern agriculture, with an extensive range of satellite data such as Landsat, Sentinel-2, Modis, and Palsar, data are readily available. Sugarcane is a tall perennial grass belonging to the genus Saccharum, utilized for producing sugar. These plants were generally 2–6 m tall with fibrous, stout, jointed stalks, rich in sucrose, that will be accumulated in the stalk internodes. Sugarcanes have a different growth pattern and phenology than many other crops; thus, the spectral and temporal features of satellite data are examined by utilizing statistical and machine learning (ML) techniques for optimal discrimination of sugarcane fields with other crops. In this study, we propose an Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring (ODLD-SSCM) model on Remote Sensing Images. The presented ODLD-SSCM model mainly intends to estimate the crop yield of sugarcanes using RSIs. In the presented ODLD-SSCM technique, the sugarcane yield mapping can be derived by the use of the self-attentive deep learning (SADL) model. Besides, an oppositional spider colony optimization (OSCO) algorithm is used for the hyperparameter tuning of the ODLD-SSCM model. A detailed set of experimentations were performed to demonstrate the enhanced outcomes of the ODLDSSCM model. A comprehensive comparison study pointed out the enhancements of the ODLD-SSCM model over other recent approaches.

TomFusioNet: A tomato crop analysis framework for mobile applications using the multi-objective optimization based late fusion of deep models and background elimination

Crop disease is a critical concern for the farmers which needs to be addressed to mitigate production loss. Multifarious frameworks have been proposed for the rapid recognition of tomato crop diseases, however, their practical deployment is a daunting task. The existing models suffer from performance issues such as overfitting and gradient vanishing. To alleviate these issues, this research proposes an end-to-end tomato crop analysis framework, TomFusioNet, for mobile applications. For feature extraction, the late-fusion technique is leveraged by aggregating the results of modified cross-domain transfer learning models using the maximum likelihood prediction strategy. Multi-layer Perceptron models are utilized as meta-learners. TomFusioNet’s pipeline comprises two modules, namely, DeepRec and DeepPred. DeepRec provides preliminary disease recognition results while DeepPred further identifies the type of illness in crop. Logic gate mapping is employed to reduce unnecessary wastage of mobile computation. The hyperparameters of DeepPred are tuned using the multi-objective optimization based non-dominated sorting genetic algorithm II for performance enhancement. We highlighted the significance of feature relevancy; therefore, a hue, saturation and value (HSV) color model-based background elimination technique is also proposed. TomFusioNet can be incorporated in a smartphone app, conceptualized for remote crop monitoring. The proposed DeepRec and DeepPred models achieved an average accuracy of 99.93% and 98.32%, respectively. According to experimental results, TomFusioNet, which is built on NSGA-II, beats state-of-the-art models with a convergence loss of 0.021 and an AUC value of 99.10%. The proposed app framework’s functioning does not require any human in loop. Furthermore, the latency of framework in real-time is less than 2 s, hence, it is effective for rapid tomato crop analysis.

How does the digital transformation of agriculture affect the implementation of Integrated Pest Management?

Integrated pest management (IPM) has greatly influenced farming in the past decades. Even though it has been effective, its adoption has not been as large as anticipated. Operational issues regarding crop monitoring are among the reasons for the lack of adoption of the IPM philosophy because control decisions cannot be made unless the crop is effectively and constantly monitored. In this way, recent technologies can provide unique information about plants affected by insects. Such information can be very precise and timely, especially with the use of real-time data to allow decision-making for pest control that can prevent local infestation of insects from spreading to the whole field. Some of the digital tools that are commercially available for growers include drones, automated traps, and satellites. In the future, a variety of other technologies, such as autonomous robots, could be widely available. While the traditional IPM approach is generally carried out with control solutions being delivered throughout the whole field, new approaches involving digital technologies will need to consider adaptations in the concepts of economic thresholds, sampling, population forecast, injury identification, and ultimately the localized use of control tactics. Therefore, in this paper, we reviewed how the traditional IPM concepts could be adapted, considering this ongoing digital transformation in agriculture.

AI/ML Drone-based Arecanut Monitoring System (ADAMS)

The Indian economy relies heavily on agriculture with 72% of farmers in the states of Karnataka and Kerala producing 330 million kilograms of arecanuts. However, a rapid increase in young adults leaving the agriculture field brings hardships for older farmers like my grandparents since physical labor is increasingly expensive, dangerous, and scarce. The purpose of this research is to automate arecanut crop monitoring by providing a safe, affordable, user-friendly, and cutting-edge system that utilizes six major elements to achieve the goal: arecanut farm, drone, dataset collection, data annotations, AI/ML Mask R-CNN prediction model, and app. Research about various drones were conducted to ensure the drone abides by Indian UAV regulations. Since there were no pre-existing arecanut datasets, a DJI Mini 2 drone and Canon DSLR Camera were utilized to capture over 2000 arecanut images in India. Creating my own dataset from scratch was challenging, however it was a significant contribution in advancing computer vision. This dataset was stored on Amazon S3 and classified into ripe, unripe, and dry arecanuts. Each image was manually annotated using VGG Image Annotator and passed into a Convolutional Neural Network called Mask R-CNN to create a prediction model. This was trained on Amazon Web Services (AWS) for 100 epochs. Overall, epoch 85 gave accurate predictions. Additionally, instead of the DJI Mini 2 drone, the Skydio drone would be the best option for real world implementations. My research is a practical and innovative solution to alleviate farmers’ financial and physical hardships.

Establishment of Crop Water Stress Index for Sustainable Wheat Production under Climate Change in a Semi-Arid Region of Pakistan

The Crop Water Stress Index (CWSI) is a useful tool for evaluating irrigation scheduling and achieving water conservation and crop yield goals. This study examined the CWSI under different water stress conditions for the scheduling of wheat crop irrigation and developed indices using the leaf canopy temperature in Faisalabad, Pakistan. The experiments were conducted using a randomized, complete block design and four irrigation treatments with deficit levels of D0%, D20%, and D40% from the field capacity (FC) and D100% (100% deficit level). The CWSI was determined at pre-heading and post-heading stages through the lower baseline (fully watered crop) and upper limit (maximum stress). These baselines were computed using the air temperature and canopy temperature of plant leaves and the vapor pressure deficit (VPD). The CWSI for each irrigation treatment was calculated and the average seasonal CWSI value for the whole season was used to develop the empirical relationships for scheduling irrigation. The relationships between the air canopy temperatures and the VPD resulted in slope (x) = −0.735 and interception (c) = −0.8731 as well as x = −0.5143 and c = −1.273 at the pre- and post-heading stages, respectively. The values of the CWSI for the treatment at deficit levels of D0%, D20%, D40%, and D100% were found to be 0.08, 0.61, 0.20, and 0.64, respectively. The CWSI values developed in this study can be effectively used to promote better the monitoring of irrigated wheat crops in the region.

METHODOLOGY FOR REMOTE SENSING MONITORING OF ORGANIC WHEAT CROPS

In the presented methodology for aerospace monitoring of autumn wheat crops, grown under the conditions of organic farming, the ways of applying ground and aerospace methods are discussed in detail. This includes field experiments, phenological observations, GIS and remote sensing methods and data (data from Sentinel-2 satellite and WingtraOne unmanned aerial vehicle with MicaSense RedEdge-MX multispectral camera and RGB camera) and statistical analyses. In order to achieve the aim and objectives of the study, an experiment was conducted on a organically certified production field sown with einkorn (Triticum monococum) in the period 2020-2021. The field is part of the holding of ET "Borislav Slavchev" in the village of Byala Reka, Parvomai Municipality, South-Central Bulgaria on the soil type of leached chernozem clays, with a size of 136 da.

METHODOLOGY FOR REMOTE SENSING MONITORING OF ORGANIC WHEAT CROPS

In the presented methodology for aerospace monitoring of autumn wheat crops, grown under the conditions of organic farming, the ways of applying ground and aerospace methods are discussed in detail. This includes field experiments, phenological observations, GIS and remote sensing methods and data (data from Sentinel-2 satellite and WingtraOne unmanned aerial vehicle with MicaSense RedEdge-MX multispectral camera and RGB camera) and statistical analyses. In order to achieve the aim and objectives of the study, an experiment was conducted on a organically certified production field sown with einkorn (Triticum monococum) in the period 2020-2021. The field is part of the holding of ET "Borislav Slavchev" in the village of Byala Reka, Parvomai Municipality, South-Central Bulgaria on the soil type of leached chernozem clays, with a size of 136 da.

Estimating probability of visual detection of exotic pests and diseases in the grains industry—An expert elicitation approach

Participants in the grains industry undertake general surveillance monitoring of grain crops for early detection of pests and diseases. Evaluating the adequacy of monitoring to ensure successful early detection relies on understanding the probability of detection of the relevant exotic crop pests and diseases. Empirical data on probability of detection is often not available. Our aim was to both gain a better understanding of how agronomists undertake visual crop surveillance, and use this insight to help inform structured expert judgments about the probability of early detection of various exotic grain pests and diseases. In our study we surveyed agronomists under a state funded program to identify survey methods used to undertake visual inspection of grain crops, and their confidence in detecting pests and diseases using the associated methods. We then elicited expert judgments on the probabilities of visual detection by agronomists of key exotic pests and diseases, and compared these estimates with the self-assessments of confidence made by agronomists. Results showed that agronomists used a systematic approach to visual crop inspection but that they were not confident in detecting exotic pests and diseases, with the exception of pest and diseases that affect leaves. They were most confident in visually detecting Barley stripe rust and Russian wheat aphid; however, confidence in detecting the latter was influenced by recent training. Expert judgments on the ability of agronomists to visually detect exotic pests and diseases early was in accordance with agronomists’ self-rated confidence of detection but highlighted uncertainty around the ability of agronomists in detecting non-leaf pests and diseases. The outcomes of the study demonstrated the utility of structured expert elicitation as a cost-effective tool for reducing knowledge gaps around the sensitivity of general surveillance for early detection, which in turn improves area freedom estimates.

Features of growing red beet and carrot in the conditions of the south-western part of the Central region of the Russian Federation

Relevance and methodology. The article presents the results of the study of economically valuable signs of red beet and carrot when grown in the conditions of the south-western part of the Central region of the Russian Federation. The aim of scientific researches was to conduct phytosanitary monitoring of crops, study the species composition of harmful organisms and the relative resistance of varieties and hybrids of vegetable root crops to harmful organisms in the Bryansk region. The estimation on level of ecotoxicant accumulation during cultivation was carried out. The researches were carried out in 2018–2020 in the stationary field of the Bryansk State Agrarian University.Results. As a result of the researches, the species composition of harmful organisms on the variety samples of red beet and garden carrot was established. In the conditions of the Bryansk region, according to the signs of "root crop mass" and "commercial yield" on average over the years of research varieties and hybrids of garden carrots Mars F1, Kupar F1, Nadezhda F1, Shantene korolevskaya, Nante, Minor were identified. Insignificant population of willow-carrot aphid — Cavariella aegopodii (Skop.) —was established on plants of varieties Nante, Nantskaya 4, Marlinka, Minor and hybrid Nadezhda F1. The Nante variety with a high content of carotene (185.1 mg/kg), dry matter (13.3%), low accumulation of nitrates (11.0 mg/kg) and relative resistance to pests was selected according to the results of biochemical analysis. The development of red beet cercosporosis (R) was found to be at a level of 1.6 to 11.3%, while the prevalence of the disease ranged from 22.7 to 76.9%. The variety samples Mulatka, Lyubava and Nezhnost(R 2–2.4%) were slightly affected. A high degree of the disease prevalence was noted in the varieties Nesravnennaya, Gospadynya, Bordo 237. The samples Mulatka, Kreolka, Gospadynya, Lyubava, Nesravnennaya were selected according to the mass of the root crop and resistance to harmful organisms.

Farmers’ Perceptions on an Irrigation Advisory Service: Evidence from Tunisia

Decisions on irrigation water management are usually made at different levels, including farms, water user associations (WUAs), and regional water planning agencies. The latter generally have good access to information and decision tools regarding water resources management. However, these remain out of reach to the final water users, namely the farmers. The study, conducted in the irrigated district of Cherfech, north Tunisia, had the main objective of investigating farmer’s perceptions of, and acceptance for, the use of an irrigation advisory service (IAS) to be implemented by their WUA. The suggested IAS provides the following information: (1) reference evapotranspiration (ETo) and rainfall; (2) crop water requirement (CWR) of the most cultivated crops; (3) irrigation water requirement (IWR) of the farmer’s crop; and (4) crop monitoring and real-time estimation of IWR of crops settled, using soil moisture sensors. Such services and information would be available at the WUA level and provided in a timely manner to farmers for more effective decision making at the plot level. Prior to the acceptance study, we launched a technical study to determine the required tools and equipment required for the implementation of the IAS, followed by a farmer survey to assess their respective perceptions and acceptance towards this IAS. Results showed that only 54% of the farmers are satisfied by WUAs work, but that 77% of them accepted using the suggested IAS. Farmers are also willing to pay for most of the IAS packages suggested. The financial profitability of investing in the IAS at the WUA level shows the venture is financially viable, with a benefit cost ratio (BCR) of 1.018. The project will be even more profitable if we add the social benefits, which may result in water savings at the WUA level.

Remote Sensing Monitoring of Rice and Wheat Canopy Nitrogen: A Review

Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality.

A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization

Climate change and population growth risk the world’s food supply. Annual crop yield production is one of the most crucial components of the global food supply. Moreover, the COVID-19 pandemic has stressed global food security, production, and supply chains. Using biomass estimation as a reliable yield indicator, space-based monitoring of crops can assist in mitigating these stresses by providing reliable product information. Research has been conducted to estimate crop biophysical parameters by destructive and non-destructive approaches. In particular, researchers have investigated the potential of various analytical methods to determine a range of crop parameters using remote sensing data and methods. To this end, they have investigated diverse sources of Earth observations, including radar and optical images with various spatial, spectral, and temporal resolutions. This paper reviews and analyzes publications from the past 30 years to identify trends in crop monitoring research using remote sensing data and tools. This analysis is accomplished through a systematic review of 277 papers and documents the methods, challenges, and opportunities frequently cited in the scientific literature. The results revealed that research in this field had increased dramatically over this study period. In addition, the analyses confirmed that the normalized difference vegetation index (NDVI) had been the most studied vegetation index to estimate crop parameters. Moreover, this analysis showed that wheat and corn were the most studied crops, globally.

Assessing the contribution of the Adhoc crop health surveillance tool on the food security and livelihoods of smallholder farmers in Uganda

AbstractOne of the perennial causes of food insecurity in Sub‐Saharan African countries is crop pests and diseases that affect agricultural production. To address this concern in Uganda, a mobile phone crowdsourcing Adhoc surveillance tool, Adsurv, was deployed and used by smallholder farmers. Adsurv is a crop health surveillance and monitoring mobile application that facilitates diagnosing and managing crop pests and diseases. Existing literature has examined the impacts of mobile technologies on food security or livelihoods in isolation, but has not conducted a holistic assessment of both. This study on the other hand aimed at assessing the contribution Adsurv made towards improved food security and livelihood assets of the smallholder farmers, which were not fully understood. The study contributes to applying the sustainable livelihood framework (SLF) as theory in evaluating Mobile for Development (M4D) initiatives. The Analytic Hierarchy Process (AHP) was further adopted to facilitate the development of a hierarchical assessment model of the multidimensional food security and livelihood aspects using data collected from 56 out of the targeted 70 participants. Results show that Adsurv mainly contributed towards food availability rather than utilization or access. These gains mostly enhanced the human assets of the smallholder farmers, empowering them with skills that positively impacted all other assets to support the attainment of sustainable livelihoods. The implication of these results in both research and practice is the need for further research trials, which will motivate farmers to recognize the importance of the nutritional value of food in their agronomic practices.

Analysis of NDVI and NDRE Indices Using Satellite Images for Crop Identification at Kelantan

In Malaysia, the expansion of rubber trees, oil palm, and paddy plantations has become widely increased because of economic demands. Crop identification or monitoring is one of these applications since remote sensing provides precise, up-to-date, and cost-effective data on a range of crop kinds at various temporal and geographic resolutions. This study recommended integrating multispectral images, a tree canopy segmentation algorithm, and vegetable indicators to measure the attributes and spectral properties of crops and plants in a Kelantan region. Normalised difference vegetation index (NDVI) and normalised difference red edge (NDRE) were obtained from five spectral band images (red, green, blue, infrared (NIR), and red edge 2 (REDGE) that were processed by software into a full image map. NDVI is based on a plant’s characteristic reflection of a mix of visible red and near-infrared (NIR) light. NDVI function is to calculate the percentages of canopy cover, whereas the NDRE index is to assess leaf chlorophyll concentration or nitrogen content. The Red Edge sensors detected fluctuations in chlorophyll content within the leaf and throughout the plant canopy. Based on Landsat 8 and Sentinel 2A data, this study determined the NDVI and NDRE values for the crops in paddy fields, oil palm plantations and rubber tree plantations from different locations in Kelantan. The data was then analysed, the bar graphs and an emerging analysis of the differences between NDVI and NDRE maps for crop identification were used to present the results, respectively.

Using the SCOPE model for potato growth, productivity and yield monitoring under different levels of nitrogen fertilization

Most applications of remote sensing in agricultural crop monitoring use multispectral imaging techniques, but with upcoming hyperspectral missions, the opportunity arises to better estimate pigment absorption and crop structure by exploiting the full solar reflective spectrum. In this study, we demonstrate how hyperspectral time series can be used with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model to estimate crop yield variability among fields, crop varieties and nitrogen treatments generically, i.e. without a calibration with in situ , data. Field experiments were conducted in two potato fields in the Netherlands between May and September 2019. The fields were planted with five varieties of potato, under three nitrogen fertilization treatments. By fitting the model to the full VNIR-SWIR spectrum of measured hyperspectral reflectance, we retrieved the model input parameters of Leaf Area Index (LAI), leaf chlorophyll content (Cab) and leaf water content (Cw) and simulated the photosynthesis throughout the season using data of local Automatic Weather Stations (AWS). Statistical analysis of measured and retrieved traits of LAI, Cab and canopy water content showed that two fields responded differently to the treatments, exhibiting fewer classes than were expected based on the experimental design. Potato yield, which was estimated as the sum of photosynthesis flux multiplied by the harvest index of 0.64, correlated with the measured tuber dry weight with R 2 0.36 and RMSE 2.5 t ha −1 . This study demonstrates that even in the absence of crop or variety specific information, hyperspectral reflectance and local weather data ingested into SCOPE can explain a substantial part of the observed variability in yield among fields. • Time series of hyperspectral canopy reflectance were recorded in two potato fields. • The variation in traits was created by nitrogen fertilization treatments. • SCOPE model retrieved plant traits contained information about treatments. • SCOPE-simulated accumulated photosynthesis correlated with the final dry yield. • SCOPE model is more generic than dimensionality reduction methods.

An RGB-D multi-view perspective for autonomous agricultural robots

Automated in-field data gathering is essential for crop monitoring and management and for precision farming treatments. To this end, consumer-grade digital cameras have been shown to offer a flexible and affordable sensing solution. This paper describes the integration and development of a cost-effective multi-view RGB-D device for sensing and modeling of agricultural environments. The system features three RGB-D sensors, arranged to cover a horizontal field of view of about 130 deg in front of the vehicle, and a suite of localization sensors consisting of a tracking camera, an RTK-GPS sensor and an IMU device. The system is intended to be mounted on-board an agricultural vehicle to provide multi-channel information of the surveyed scene including color, infrared and depth images, which are then combined with localization data to build a multi-view 3D geo-referenced map of the traversed crop. The experimental demonstrator of the multi-sensor system is presented along with the steps for the integration of the different sensor data into a unique multi-view map. Results of field experiments conducted in a commercial vineyard are included, as well, showing the effectiveness of the proposed system. The resulting map could be useful for precision agriculture applications, including crop health monitoring, and to support autonomous driving.

An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring

Continuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a consistent scale. With the increased availability of high spatial resolution images, it is necessary to develop a new STIF model that can effectively reflect the properties of high spatial resolution satellite images for small-scale crop field monitoring. This paper proposes an advanced STIF model using a single image pair, called high spatial resolution image fusion using object-based weighting (HIFOW), for blending high spatial resolution satellite images. The four-step weighted-function approach of HIFOW includes (1) temporal relationship modeling, (2) object extraction using image segmentation, (3) weighting based on object information, and (4) residual correction to quantify temporal variability between the base and prediction dates and also represent both spectral patterns at the prediction date and spatial details of fine-scale images. The specific procedures tailored for blending fine-scale images are the extraction of object-based change and structural information and their application to weight determination. The potential of HIFOW was evaluated from the experiments on agricultural sites using Sentinel-2 and RapidEye images. HIFOW was compared with three existing STIF models, including the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. Experimental results revealed that the HIFOW prediction could restore detailed spatial patterns within crop fields and clear crop boundaries with less spectral distortion, which was not represented in the prediction results of the other three models. Consequently, HIFOW achieved the best prediction performance in terms of accuracy and structural similarity for all the spectral bands. Other than the reflectance prediction, HIFOW also yielded superior prediction performance for blending normalized difference vegetation index images. These findings indicate that HIFOW could be a potential solution for constructing high spatial resolution time-series images in small-scale croplands.

The Application of Remote Sensing Techniques and Spectral Analyzes to Assess the Content of Heavy Metals in Soil – A Case Study of Barania Góra Reserve, Poland

The understanding of the spatial and temporal dynamics of farmland processes is essential to ensure the proper crop monitoring and early decision making needed to support efficient resource management in agriculture. By creating appropriate crop management strategies, one can increase harvest efficiency while reducing costs, waste, chemical spraying, and inhibiting the impact of biotic and abiotic factors on crop stress. Only reliable spatial information makes it possible to comprehend the influence of various factors on the environment. The main objective of the research presented in the paper was to assess the possibility of using maps of vegetation and soil indices, such as NDVI, SAVI, IRECI, CIred-edge, PSRI and HMSSI, calculated on the basis of images from the Sentinel-2 satellite, to qualitatively determine the increased amount of heavy metals in the soil in the areas of small agricultural plots around the Barania Góra nature reserve in Poland.The conducted pilot project shows that the spectral indices: NDVI, SAVI, IRECI, CIred-edge, PSRI, and HMSSI, calculated on the basis of images from Sentinel-2, have the potential to assess the content of nickel zinc, chromium and cobalt in the soil on agricultural plots. However, the confirmation of the obtained results requires continuation of the research.