Agricultural Crop Monitoring Research Articles

Near Real Time Crop Health Status Monitoring and Mapping using Sentinel Satellite Image in Menjar Shenkora District, Ethiopia

Agricultural monitoring systems must provide timely and standardized information on crop production, status, and yield, from sub-regional to national scales. Accurate monitoring and mapping of vegetation condition and health are vital for managing crops, assessing damage, and predicting yields. Crop health monitoring is one of the important items for tracking the general health status of any crop. In this regard, remote sensing and GIS play a crucial role for monitoring crop health, providing current information that traditional methods like field surveys and sampling questionnaires struggle to obtain. Effective cropland mapping techniques are essential for regular crop monitoring. This type of monitoring demands frequents continuous data with high time and space resolution. Near real-time crop monitoring uses technologies like the Sentinel-2 satellite mission, offering a consistent 5-day revisit cycle and freely accessible data. This opens new doors for delivering timely updates and monitoring parcel-based crop health and conditions in real-time. Therefore, this study used satellite images, Global Positioning System (GPS) collected data, and parcel-based socioeconomic data. GPS and socioeconomic data were employed to validate the satellite-based near real-time crop monitoring results. Vegetation Condition Index (VCI) and the Normalized Difference Vegetation Index (NDVI) were used to evaluate crop health at different stages of the growing season and to generate time series data for crop phenology respectively. NDVI time series data was used to generate crop phenology information for four main crops: Teff, wheat, onion, and sorghum. The crop type maps for these crops at the study sites were validated with an overall accuracy of 79.26% and a Kappa value of 0.737. Additionally, the results from the current research and the field-collected data were consistent in providing information about the onset, greening, maturity, and senescence dates of each crop. These findings highlight the effectiveness of the satellite-based system for real-time agricultural crop monitoring using Sentinel-2 observations across various sites and time frames. Moreover, it helps to fill the gaps of traditional crop monitoring methods with those based on satellite technology. This system is particularly valuable for early warning purpose in areas like the current study site, where conventional crop monitoring methods and inputs are limited.

Systematic analysis of hyperspectral imaging and intelligent sensor systems in multiscale agriculture

This article delves into the systematic analysis of Hyperspectral Imaging Technology (HSI) and Intelligent Sensor Systems in the context of multiscale agriculture, showcasing their critical role in enhancing efficiency and sustainability in agricultural production amid the challenges posed by global population growth. It highlights the pressing need to optimize agricultural processes through advanced technologies to tackle issues such as resource scarcity and environmental pollution. By presenting case studies, the article illustrates the effective integration of HSI and intelligent sensors in key agricultural processessoil analysis, crop monitoring, and pest detectionunderscoring their significance in advancing precision agriculture. The discussion extends to the potentials of data fusion and decision support systems in elevating crop yield and quality. Concluding, the paper emphasizes that despite facing hurdles like technical barriers and maintenance costs, the application of these technologies not only boosts production efficiency and precision but also contributes to agricultural sustainability, underlining the importance of continued research and innovation for a more sustainable and efficient agricultural future.

Using SAR Data for Monitoring of Agricultural Crops in the South of the Russian Far East

The use of SAR data to monitoring agricultural crops is a promising area of research designed to complement existing methods and technologies based on the analysis of multispectral images. The main advantages of vegetation indices calculated from SAR data include their sensitivity to the polarimetric properties of the backscatter intensity, its scattering characteristics, and independence from cloud cover. This is especially important for the territory of the south of the Russian Far East, whose monsoon climate provides humid and cloudy weather during the period when crops gain maximum biomass. For arable lands in the Khabarovsk Territory and the Amur Region, a total of 64 Sentinel-1 SAR images were obtained from May to October 2021. For each date, the values of the DpRVI, RVI, VH/VV indices were calculated and time series were constructed for the entire observation period for individual fields (342 fields in total). NDVI time series were constructed from Sentinel-2 multispectral images using a cloud mask. The characteristics of time series extremes were calculated for different types of arable land: soybeans, oats, and fallows. It was shown that for each crop the seasonal curves DpRVI, RVI, VH/VV had a characteristic appearance. It was found that the DpRVI demonstrated the highest stability – the coefficients of variation of the seasonal variation of DpRVI were significantly lower than those for RVI and VH/VV. It was also revealed that the similarity between the curves of these indices remained for regions quite distant from each other - the Khabarovsk Territory and the Amur Region. The main characteristics of the seasonal variation of time series of radar indices were calculated in comparison with NDVI - the magnitude of the maximum, the date of the maximum and the values of the coefficient of variation for these indicators. It was found, firstly, that the values of these indicators in different regions are similar to each other; secondly, the variability of the maximum and the day of the maximum for DpRVI is lower than for RVI and VH/VV; thirdly, the variability of the maximum and the day of the maximum for DpRVI is comparable to NDVI. Thus, time series of radar indices DpRVI, RVI, VH/VV for the main types of agricultural lands in the south of the Far East have distinctive features and can be used in classification problems, yield modeling and crop rotation control.

Crop monitoring using remote sensing land use and land change data: Comparative analysis of deep learning methods using pre-trained CNN models

In the context of the rapidly evolving climate dynamics of the early twenty-first century, the interplay between climate change and biospheric integrity is becoming increasingly critical. The pervasive impact of climate change on ecosystems is manifested not only through alterations in average environmental conditions and their variability but also through ancillary shifts such as escalated oceanic acidification and heightened atmospheric CO2 levels. These climatic transformations are further compounded by concurrent ecological stressors, including habitat degradation, defaunation, and fragmentation. Against this backdrop, this study delves into the efficacy of advanced deep learning methodologies for the classification of land cover from satellite imagery, with a particular emphasis on agricultural crop monitoring. The study leverages state-of-the-art pre-trained Convolutional Neural Network (CNN) architectures, namely VGG16, MobileNetV2, DenseNet121, and ResNet50, selected for their architectural sophistication and proven competence in image recognition domains. The research framework encompasses a comprehensive data preparation phase incorporating augmentation techniques, a thorough exploratory data analysis to pinpoint and address class imbalances through the computation of class weights, and the strategic fine-tuning of CNN architectures with tailored classification layers to suit the specificities of land cover classification challenges. The models' performance was rigorously evaluated against benchmarks of accuracy and loss, both during the training phase and on validation datasets, with preventative strategies against overfitting, such as early stopping and adaptive learning rate modifications, being integral to the methodology. The findings illuminate the considerable potential of leveraging pre-trained deep learning models for remote sensing in agriculture, demonstrating that advanced CNN architectures, particularly DenseNet121 and ResNet50, are notably effective in enhancing crop type classification accuracy from satellite imagery. This study contributes valuable insights to the field of precision agriculture, advocating for the integration of sophisticated image recognition technologies to bolster crop monitoring efficacy, thereby enabling more nuanced agricultural decision-making and resource allocation.

АКТУАЛЬНОСТЬ ПРОВЕДЕНИЯ МОНИТОРИНГА СЕЛЬСКОХОЗЯЙСТВЕННЫХ КУЛЬТУР И ХВОЙНОГО ПОДРОСТА В ВЕГЕТАТИВНЫЙ ПЕРИОД

Modern agriculture faces a number of challenges, including climate change, resource constraints and the need to increase productivity. In this context, the assessment of quantitative parameters of crop growth by technical means during the growing season becomes particularly relevant. The scientific approach to this problem is becoming a key tool for optimizing production and ensuring food security, sustainable development of forest ecosystems. (Research purpose) To substantiate the relevance of monitoring crops and undergrowth during artificial reforestation during the vegetative period. (Materials and methods) Video monitoring of agricultural crops, automated segment recognition, geospatial data, literature review, statistical analysis were used. (Results and discussion) The statistics and problems of existing measures for monitoring coniferous undergrowth after reforestation measures were analyzed. The methods of using video monitoring of agricultural crops, in particular, for analyzing the yield of corn shoots, were considered. The use of a video monitoring system to control the care of coniferous undergrowth in the first five years after reforestation was substantiated. Key limitations and difficulties were cited, including high labor costs, insufficient monitoring, lack of systematic approach, limited human labor and low speed and accuracy of data processing. It has been shown that these factors emphasize the need for innovations in methods of controlling the growth of crops and caring for coniferous young. (Conclusions) It was revealed that the video monitoring system of coniferous undergrowth is one of the solutions to the problems of monitoring the condition of forest areas. It was determined that it allows assessing the quality of planting work, determining the growth and health indicators of seedlings, monitoring the environment by indirect signs and, due to a high level of digitalization and automation, responding quickly to violations. It was stated that the assessment of the parameters of crop growth in real time with the use of technical solutions makes it possible to identify problems such as diseases and parasites at an early stage, which will improve the possibilities for their prevention.

Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review

The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, slow adoption of sustainable farming, a need for climate-resilient food systems, resource inequity, and the protection of small-scale farmers’ practices. These issues are integral to food security and environmental health. Remote sensing technologies can assist precision agriculture in effectively addressing these complex problems by providing farmers with high-resolution lenses. The use of vegetation indices (VIs) is an essential component of remote sensing, which combines the variability of spectral reflectance value (derived from remote sensing data) with the growth stage of crops. A wide array of VIs can be used to classify the crops and evaluate their state and health. However, precisely this high number leads to difficulty in selecting the best VI and their combination for specific objectives. Without thorough documentation and analysis of appropriate VIs, users might find it difficult to use remote sensing data or obtain results with very low accuracy. Thus, the objective of this review is to conduct a critical analysis of the existing state of the art on the effective use of VIs for the discrimination and monitoring of several important agricultural crops (wheat, corn, sunflower, soybean, rape, potatoes, and forage crops), grasslands and meadows. This data could be highly useful for all the stakeholders involved in agricultural activities. The current review has shown that VIs appear to be suitable for mapping and monitoring agricultural crops, forage crops, meadows and pastures. Sentinel-1 and Sentinel-2 data were the most utilized sources, while some of the frequently used VIs were EVI, LAI, NDVI, GNDVI, PSRI, and SAVI. In most of the studies, an array of VIs needed to be employed to achieve a good discrimination of crops or prediction of yields. The main challenges in using VIs are related to the variation of the spectral characteristics during the vegetation period and to the similarities of the spectral signatures of various crops and semi-natural meadows. Thus, further studies are needed to establish appropriate models for the use of satellite data that would prove to have greater accuracy and provide more relevant information for the efficient monitoring of agricultural crops.

Implementation of a weather station to monitor agricultural crops

Abstract Various studies on local crop monitoring are presented in the literature, however there is insufficient research on low-cost IoT weather stations that can obtain weather information over a long period of time. To overcome the difficulties described above, this paper proposes the development of an IoT weather station for low-cost, sustainable and multi-functional agricultural crop monitoring. The objective of the work is to develop a local weather observation device for agricultural crops, to set up a server for storing and providing meteorological information and to perform an analysis of the acquired weather data. The weather station architecture developed and proposed in this project consists of four main blocks: sensor and controller module, power module, local data storage module and data communication module (gateway).

Crop type recognition of VGI road-side images via hierarchy structure based on semantic segmentation model Deeplabv3+

The application of artificial intelligence in the field of agricultural crop type recognition is of great significance. There are currently two methods for collecting crop information: remote sensing and volunteered geographic information (VGI). As one pixel in a remote sensed satellite image may correspond to multiple crop types due to its low resolution, more refined VGI images can be a supplementary data source for agricultural crop monitoring. A hierarchal semantic segmentation structure based on Deeplabv3+ is proposed in this paper. Inspired by causality theories, a binary classification is adopted in the first stage of the model to segment crops from background. The second stage aims to recognize the crop types including rape, corn, fallow and bare land, wheat and rice. As wheat and rice have many similar characteristics, they are considered as one category in this stage and will be further processed in the downstream. In the third stage, a priori knowledge such as the location and time of image acquisition is utilized for fine recognition between wheat and rice. We have carried out experiments on the proposed Deeplabv3+ model and other different semantic segmentation models are validated on a VGI dataset with 20% rough labels and 80% accurate labels, including thousands of road-side images collected from different image sensors, under different weather and from different geographic locations. Experiments results show that Deeplabv3+ achieves the best performance. The recognition of five categories of crops has reached a precision of 87%, a recall of 91%, and IoU of 81%, showing good potential for use in the crop type recognition for VGI images.

Обеспечение оперативного мониторинга сельскохозяйственных посевов спутниковыми данными

Abstract. Purpose. The study is aimed at providing operational monitoring of agricultural crops using satellite data. Data on the spectral-reflective properties of agricultural crops and climatic characteristics that reflect the state of agricultural crops are considered on the example of Kuraginskoye Agricultural Experiment Production Facility (AEPF), located in the south of the Krasnoyarsk krai. Methodology and methods. Based on satellite data, the values of the average daily temperature of the land surface, the sum of daily precipitation, thematic maps and the average values of the NDVI, ClGreen, and MSAVI2 vegetation indices were obtained. Satellite data from Sentinel-2, Terra MODIS and IMERG GPM Precipitation were used as sources of Earth remote sensing data. The initial satellite information was processed using the QGIS software. As a result, satellite data were selected from open sources, methods for their processing were compiled, and an archive of data for the growing seasons from 2015 to 2022 was formed. Operational monitoring using satellite data makes it possible to identify the heterogeneity of the spatial distribution of vegetation cover, track the dynamics of temperatures and the amount of precipitation during the growing season, thereby contributing to a comprehensive assessment of the state of all agricultural crops in general and individually for each field. Scientific novelty. The technology of processing Sentinel-2, Terra MODIS (MOD11), GPM (IMERG) data and obtaining their derived products based on the QGIS geoinformation system in automated mode for agricultural monitoring tasks has been developed. Practical significance. Data sets on vegetation indices, temperature and precipitation have been formed, which are of great practical importance for making effective management decisions on the territory of the Kuraginskoye AEPF.

High-precision estimation of grass quality and quantity using UAS-based VNIR and SWIR hyperspectral cameras and machine learning

Miniaturised hyperspectral cameras are becoming more easily accessible and smaller, enabling efficient monitoring of agricultural crops using unoccupied aerial systems (UAS). This study’s objectives were to develop and assess the performance of UAS-based hyperspectral cameras in the estimation of quantity and quality parameters of grass sward, including the fresh and dry matter yield, the nitrogen concentration (Ncont) in dry matter (DM), the digestibility of organic matter in DM (the D-value), neutral detergent fibre (NDF), and water-soluble carbohydrates (WSC). Next-generation hyperspectral cameras in visible-near-infrared (VNIR, 400–1000 nm; 224 bands) and shortwave-infrared (SWIR; 900–1700 nm; 224 bands) spectral ranges were used, and they were compared with commonly used RGB and VNIR multispectral cameras. The implemented machine-learning framework identified the most informative predictors of various parameters, and estimation models were then built using a random forest (RF) algorithm for each camera and its combinations. The results indicated accurate estimations; the best normalised root-mean-square errors (NRMSE) were 8.40% for the quantity parameters, and the best NRMSEs for the quality parameters were 7.44% for Ncont, 1% for D-value, 1.24% for NDF, and 12.02% for WSC. The hyperspectral datasets provided the best results, whereas the worst accuracies were obtained using the crop height model and RGB data. The integration of the VNIR and SWIR hyperspectral cameras generally provided the highest accuracies. This study showed for the first time the performance of novel SWIR range hyperspectral UAS cameras in agricultural application.

Spatio-Temporal Knowledge Graph-Based Research on Agro-Meteorological Disaster Monitoring

Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative manner. This paper proposes a method for monitoring agro-meteorological disasters based on a spatio-temporal knowledge graph. It employs a semantic ontology framework to achieve the organic fusion of multi-source heterogeneous data, including remote sensing data, meteorological data, farmland data, crop information, etc. And it formalizes expert knowledge and computational models into knowledge inference rules, thereby enabling monitoring, early warning, and disaster analysis of agricultural crops within the observed area. The experimental area for this research is the wheat planting region in three counties in Henan Province. The method is tested using simulation monitoring, early warning, and impact calculation of the past two occurrences of dry hot wind disasters. The experimental results demonstrate that the proposed method can provide more specific and accurate warning information and post-disaster analysis results compared to raw records. The statistical results of NDVI decline also validate the correlation between the severity of wheat damage caused by dry hot winds and the intensity and duration of their occurrences. Regarding remote sensing data, this paper proposes a method that directly incorporates remote sensing data into spatio-temporal knowledge inference calculations. By integrating remote sensing data into the regular monitoring process, the advantages of remote sensing data granted by continuous observation are utilized. This approach represents a beneficial attempt to organically integrate remote sensing and meteorological data for monitoring, early warning, and evaluation analysis of agro-meteorological disasters.

IoT Applied to Real-Time Monitoring of Agricultural Crops

Los notorios cambios en el comportamiento climático que van desde oscilaciones hacia valores extremos de temperatura hasta la ausencia de lluvias en largos periodos de tiempo, disminuyendo las fuentes naturales de agua como ríos y arroyos e incluso los depósitos subterráneos de agua, con los consiguientes daños que esto produce en todo lo concerniente a la producción agrícola trae como consecuencia, la disminución en la producción de los mismos con el consiguiente impacto en la economía por el aumento de los precios y en un caso más extremo la escases de los mismos, impactando en la calidad de vida de la población. Por ello es importante realizar un monitoreo en tiempo real de las condiciones en que se encuentran los cultivos agrícolas a fin de optimizar el uso de los recursos disponibles. A fin de llegar a estos objetivos el Internet de las Cosas (IoT) se ha demostrados como una herramienta muy útil debido, entre otras cosas, a la disminución del tamaño y precio de los circuitos integrados, aparición de circuitos empotrados o motas que permiten el diseño con una funcionalidad específica que incluyen sensores, reducción del consumo de potencia permitiendo la utilización de baterías, los cuales van apareciendo con mayor duración, la simplificación de las comunicación wifi que permite en la actualidad el acceso ilimitado a amplias zonas geográficas con dispositivos muy simples que no solo permite la comunicación Wifi sino también tienen integrada la posibilidad de medir variables mediante sensores. En este trabajo presentamos nuestro sistema mediante el cual se puede realizar el monitoreo de las condiciones de suelo en tiempo real utilizando tecnología IoT.

LANDSAT 8 LST Pan sharpening using novel principal component based downscaling model

The panchromatic (PAN) band is accessible at a much higher spatial resolution than the thermal infrared (TIR) band in remote sensing satellites. To mitigate this issue, current research proposes a fusion method based on principal components of PAN and TIR band pairs to enhance the spatial resolution of Land surface temperature (LST) images. In this research, LANDSAT-8 LST images of 100 m resolution have been downscaled to 15 m resolution by the Novel principal component (NPC) based downscaling model. The downscaled LST image's thermal values have been cross-verified by in situ thermal points collected by the thermal data logger and image quality indices. The results have shown the root mean square error (RMSE) of 0.51–0.92 for downscaled LST image from 100m to 15m resolution. The correlation between downscaled LST and ground truth points is between 0.54 and 0.83. The proposed NPC-based LST-PAN combination technique collects spatial details of the Earth's surface from PAN data as well as indulges thermal information of TIR image. The proposed fusion technique has upgraded the spatial goal and peculiarity of TIR images data set. The higher spatial resolution of thermal images can increase the level of precision in various remote sensing applications. These improved data sets can be used in a variety of applications, including urban heat island mapping, agricultural crop monitoring, forest fire detection, and hydrological modeling, to name a few. The increased spatial resolution provided by NPC fusion results in more accurate and consistent temperature readings, leading to improved decision-making and management in these and other applications.

Harnessing heterogeneity in space with statistically guided meta-learning.

Spatial data are ubiquitous, massively collected, and widely used to support critical decision-making in many societal domains, including public health (e.g., COVID-19 pandemic control), agricultural crop monitoring, transportation, etc. While recent advances in machine learning and deep learning offer new promising ways to mine such rich datasets (e.g., satellite imagery, COVID statistics), spatial heterogeneity-an intrinsic characteristic embedded in spatial data-poses a major challenge as data distributions or generative processes often vary across space at different scales, with their spatial extents unknown. Recent studies (e.g., SVANN, spatial ensemble) targeting this difficult problem either require a known space-partitioning as the input, or can only support very limited number of partitions or classes (e.g., two) due to the decrease in training data size and the complexity of analysis. To address these limitations, we propose a model-agnostic framework to automatically transform a deep learning model into a spatial-heterogeneity-aware architecture, where the learning of arbitrary space partitionings is guided by a learning-engaged generalization of multivariate scan statistic and parameters are shared based on spatial relationships. Moreover, we propose a spatial moderator to generalize learned space partitionings to new test regions. Finally, we extend the framework by integrating meta-learning-based training strategies into both spatial transformation and moderation to enhance knowledge sharing and adaptation among different processes. Experiment results on real-world datasets show that the framework can effectively capture flexibly shaped heterogeneous footprints and substantially improve prediction performances.

Development of agricultural crop monitoring software

The operation of Bradley's algorithm is simple. After that, we divide the image into several areas alongside d, take the average value of the sum of the values of Im pixels in this area, add the value, and compare the value of each pixel with the obtained result. 1/8 of the image width and 15% of the average pixel brightness across the area. In general, both of these parameters can and should be changed according to the specific situation. Thus, if the size of the object is larger than the area of a square with a side equal to d, then the center of this object can be taken as the background by the algorithm, and the problem is solved by reducing the value of d, but fine details of the image may be lost. This article provides an overview of some research and image processing analysis on the problem of crop and vegetation image recognition. Remote identification of crops and plants and their cultivation, assessment of horticulture status startup projects are presented. A computer vision system for obtaining images from a drone and assessing the condition of a garden is provided. Digitization methods for data processing are considered.

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, Res Net34, Dense Net19, Alex Net, 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. Plant Village 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

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.

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.

Monitoring Directional Dynamics of Growing Wheat Crop Canopy Using Ground based Time Series Remote Sensing Radiative Measurements

Agricultural crop monitoring is an issue of extreme importance under global climate change, increased natural disasters and population explosion threatening global food security. In this paper, dynamic behaviour of spectro-directional reflectance properties of wheat canopy has been studied using radiometric measurements performed over a wheat field during the entire crop life cycle under varying viewing geometries. The study reveals that biological growth rhythm of wheat crop associated with continuous alteration in canopy condition particularly in terms of optical and morphological properties during the life cycle results in a distinct and systematic changing pattern of bidirectional responses in red and near Infrared (NIR). Analysis shows appreciable sensitivity of radiometric measurements both in the red and NIR regions to crop phenological transformation and changes. Soil background influenced the overall angular anisotropy pattern and manifested relatively high surface reflectance specifically at the early growth stage. At this stage, changes in viewing direction give rise to additional variability in directional reflectance due to varying proportion of soil-vegetation and enhanced contrast between wheat crop and its soil background. Both amplitude and angular pattern of directional response of the canopy undergo appreciable changes with time. Asymmetry in directional reflectance has been noticed in both the spectral regions on either side of nadir in the principal plane. In this connection, time varying angular characteristics of normalized difference vegetation index (NDVI) has also been studied in relation to crop growth.

Features of digital phytosanitary monitoring of agricultural crops

The subject of the study is digital phytosanitary monitoring of agricultural crops. The object of the study is agricultural crops of the Russian Federation. The following main research methods are used: monographic method; method of analysis and synthesis; method of observation; method of comparison; method of expert assessments. As a result of the scientific research, the following result was obtained. The concept and essence of digital phytosanitary monitoring of agricultural crops are considered. We have determined that digital phytosanitary monitoring of agricultural crops is understood as a high-tech process that includes monitoring the state of growth and development of plants in order to protect them from harmful organisms and the influence of environmental factors. Three options for conducting phytosanitary monitoring of agricultural crops in the Russian Federation are considered: the use of satellite systems; the use of aviation equipment; the use of sensors and devices of agricultural machinery. The differences between digital phytosanitary monitoring of crops and the traditional method are determined by the following five characteristics: the time of monitoring; access to information; the possibility of physical damage to the studied plants; spatial and temporal analysis of vast, remote territories and interaction with each other; obtaining final results.