Crop Monitoring System Research Articles

Monitoring Plant Status and Fertilization Strategy through Multispectral Images.

A crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550 nm), red (660 nm), red edge (735 nm), near infrared (790 nm), RGB, and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 mL, T1: 6.25 mL, T2: 12.5 mL and T3: 25 mL) and the vermicomposting was added in Weeks 1 and 5. The trial was conducted in a greenhouse and 192 images were taken with each lens. A plant segmentation algorithm was developed and several vegetation indices were calculated. On top of calculating indices, multiple morphological features were obtained through image processing techniques. The morphological features were revealed to be more feasible to distinguish between the control and the organic fertilized plants than the vegetation indices. The system was developed in order to be assembled in a precision organic fertilization robotic platform.

Adaptive Crop Monitoring System Based on Wireless Sensor Networks

Through incorporation of wireless sensor networks (WSNs) into many domains like health care, smart city and various industrial applications, it has become a broad area of research to many experts and scholars worldwide. This paper mainly concentrates on efficient development of agriculture by various modern procedures followed by farmers especially in southern part of India. This includes combination of various climatic parameters like temperature, light, rain and other environmental factors. The existing applications lack location specific data based on their system structure and other key technologies. Therefore there is a need for a systematic architecture based on various locations as environment differs from location to location. This paper presents an adaptive approach for crop management and an easy way to monitor systems which helps the farmer to get a better crop yield. The advantages and challenges of the existing systems are discussed here and various novel and innovative ideas are discussed along with future scope.

Crop Disease Detection and Monitoring System

Plants are liable to diseases that affect the growth of the plant, which successively affects the economy of the farmer. The symptoms of plant diseases are discernible in parts of a plant such as leaves, stem. Using an automatic disease detection system helps in early-stage detection and allows to treat the plant, thus preventing loss of crop. So, in our proposed system, we have introduced a crop disease detection and monitoring system. Disease detection is done using K-means clustering. Other subsystems include periodic monitoring of the temperature, humidity and soil moisture content. Based on the input of the soil moisture sensor, the motor is switched on and off for watering the plants. The actions taken are recorded and sent as a message to the farmer using the Wi-Fi Module.

Mapping cotton fields using data mining and MODIS time-series

ABSTRACTCotton is the most important fibre culture in the world. In Brazil, cotton cultivation is concentrated in the Cerrado biome, the Brazilian savanna, and is one of the most important commodities in the country. As an annual crop, the updating frequency of the spatial distribution data of cotton fields is extremely important for crop monitoring systems. In order to provide fast and accurate information for crop monitoring, time series of remote- sensing data has been used in the development of several applications in agriculture, since the high temporal resolution of some orbital sensor allows monitoring targets with high spectral-temporal variations in the land surface. However, there are still some challenges to systematize the processing of such a large amount of data available by long time series of remote-sensing imagery. Thus, this study contributes to the construction of models to identify and separate specific crop types with similar spectral behaviour to other crops practised in the same period. The objective of this study was to develop a systematic methodology based on data mining of time series of vegetation indices (VI) to map cotton fields at the regional scale. Field reference data and time series of NDVI and EVI images, obtained from MODIS sensor products during four cropping seasons (from 2012–2013 to 2015–2016), were used to construct mapping models based on decision tree algorithms. Phenological metrics were calculated from the VI time series and used to build classification rules for mapping cotton fields. Our results demonstrate that the proposed method to map cotton fields achieve high accuracy when field data and visual interpretation of NDVI temporal profiles were used for validation (accuracy higher than 95% and 93%, respectively). Comparisons with the official statistics indicated an optimal fit, with linear correlation (r) and coefficient of determination (R2) above 0.93. Therefore, the proposed method was efficient to distinguish cotton fields from other crop types with similar spectral behaviour. In addition, this method can also be applied to other cotton-producing regions and other production seasons, by reusing the models generated through machine learning approaches.

A Crop Group-Specific Pure Pixel Time Series for Europe

Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough crop signals. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop groups (i.e., winter and spring crops and summer crops) based on a MODIS–NDVI timeseries. We applied this method to the European Union at a 250 m spatial resolution. Pre-processed and smoothed, daily normalized difference vegetation index (NDVI) data (2001–2017) were used to first extract the phenological data. To account for regional characteristics (varying climate, agro-management, etc.), these data were clustered by administrative units and by year using a Gaussian mixture model. The number of clusters was pre-defined using data from regional agricultural acreage statistics. After automatic labelling, clusters were filtered based on agronomic knowledge and phenological information extracted from the same timeseries. The resulting pure pixels were validated with two different datasets, one based on high-resolution Sentinel-2 data (5 sites, 2 years) and one based on a regional crop map (1 site, 7 years). For the winter and spring crop class, pixel purity amounted to 93% using the first validation dataset and to 73% using the second one, averaged over the different years. For summer crops, the respective values were 61% (91% without one critical validation site) and 72%. The phenological analyses revealed a clear trend towards an earlier NDVI peak (approximately −0.28 days/year) for winter and spring crops across Europe. We expect that this dataset will be useful for various applications, from crop model calibration to operational crop monitoring and yield forecasting.

Exploration of Crop Production Improvement through Various Agricultural Monitoring Systems

Most wireless sensor network (WSN) are widely used to adopt various crop monitoring systems which overcome many issues caused due to lack of an efficient crop monitoring system. As there is an increasing need of various decision support systems at different field locations which constantly monitor the crops for good yield. The main focus of this paper is to review the latest available crop monitoring system designs and their implementations using wireless sensor network which results in increasing the crop yielding and quality of farming without manual observation of the farmer. Many physical events such as relative humidity, soil moisture, water levels etc. are most important for the crop productivity, growth, and quality of plants in different agricultural fields. Sensors have to transmit the collected information through a gateway to the data server. The experts can observe the crop growth by calculating various natural factors from the cloud server simultaneously. The analysis of crop growth at various optimal environmental conditions is analyzed and achieved for maximum crop productiveness through this type of continuous monitoring.

English

Agriculture plays a significant role in overall GDP of any country. So, there are several efforts those are being made to develop and increase the crop yield. In any of crop, weed i.e. (unwanted crops) is the major concern that may leads to poor production of crop. Therefore, an automatic crop monitoring system is required to monitor the weed. This system will help the farmers to monitor the crops, in gradual fashion, once it has been cultivated. Then specific Vegetation Index (VI) would be applied to locate all green portions in the image that would be part of early wheat crop or weed patches. We used Object Based Image Analysis (OBIA) algorithm to detect the weed patches in RGB and Multispectral imagery captured by UAV at 30-60 m altitude is used to acquire the images of wheat fields. Once the weed patches successfully identified from between the crop rows and within the crop rows then connected component-based classification technique is used that successfully classify the detected object either the object is related to weed patches or actual crop. The core objective of this work is to lessen the human involvement and to introduce the latest techniques and computation technology, peculiarly to identify the weed patches within the crop rows as well as between the crop rows in wheat field. Moreover, exploitation of UAV technology is also the core objective that will significantly provide the site specifically herbicides spraying

A New Approach to Statistical Process Control: Identification of Outliers in Yield Maps

The tools of precision agriculture are of utmost importance in the Brazilian agribusiness, enabling increases in yields and reducing production costs. The use of harvest monitoring systems makes it possible due the possibility to identify pontual problems in an area, however, it becomes necessary to be working properly so it does not acquire incorrect information. Therefore, the purpose with this study was to propose a new approach to identify discrepant points in harvesting maps using statistical process control, as well as to define the best multiple of the standard deviation to identificate these points. The work was conducted during the soybean harvesting at São Geronimo farm in an area of 38 hectares in the municipality of Candido Mota, located in the the state of São Paulo. For gathering information, it was used a Stara crop monitoring system (model Topper Maps) set to record information during harvest in each three second. The productivity data were used to generate an individual control chart to identify points that were out of control so they could be removed. Two standard deviation multiples, that presented an average productivity closer to the average real productivity of the area, were selected. The multiples of the deviations that came closest were the 2σ and 3σ. Two multiples of standard deviation presented an average yield closer to the average real yield of the area. Individual control charts can be used to set control limits and identify possible discrepancies. The multiple of standard deviation 3σ presented information with greater reliability.

Use and relevance of European Union crop monitoring and yield forecasts

Since 1993, the JRC has put in operation a crop monitoring and yield forecasting system for Europe, the results of which are published in the JRC MARS Bulletin (currently every month). This paper outlines how the agro-meteorological analyses, country-specific overviews of crop conditions, and crop yield forecasts reported in the Bulletin are used and how these respond to the diverse needs of different types of stakeholders. Stakeholders from more than 32 countries download the JRC MARS Bulletin, in peak-season up to 1500 downloads occur in the first days after publication. The readership of the Bulletin is diverse, coming from governments (e.g. Ministries of Agriculture), private companies (e.g. commodity traders, banking), media, and research and academia. On the list of stakeholders that want to be notified of the release of the Bulletin, roughly 37% originate from business, 35% from research and development, 22% from government, and 6% from the media. The primary user is the Directorate General for Agriculture and Rural Development (DG-AGRI) of the European Commission, which uses the forecasts to quantify the production estimates for crop supply balance sheets and to identify regions with exceptional (mostly weather related) challenges that might require a policy response. The information for wheat, maize and rice is shared through the Agricultural Market Information System (AMIS) thus contributing to increased global market transparency and better governance of agriculture and food policies. The largest business use is in market information, financial, and consultancy services, followed by commodity trading. Examples of use in media reports as well as online feedback to those, e.g. by farmer's organizations, are also presented. Downloads of the Bulletin peak in the month before harvest at the time when the forecasts can be of most value for stakeholder decision-making.

A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetation and agricultural land monitoring and modelling studies. This paper is aimed at comparing, validating and discussing different LAI satellite products from operational services and customized solution based on innovative Earth Observation (EO) data such as Landsat-7/8 and Sentinel-2A. The comparison was performed to assess overall quality of LAI estimates for rice, as a fundamental input of different scale (regional to local) operational crop monitoring systems such as the ones developed during the “An Earth obseRvation Model based RicE information Service” (ERMES) project. We adopted a multiscale approach following international recognized protocols of the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) guidelines in different steps: (1) acquisition of representative field sample measurements, (2) validation of decametric satellite product (10–30 m spatial resolution), and (3) exploitation of such data to assess quality of medium-resolution operational products (~1000 m). The study areas were located in the main European rice areas in Spain, Italy and Greece. Field campaigns were conducted during three entire rice seasons (2014, 2015 and 2016—from sowing to full-flowering) to acquire multi-temporal ground LAI measurements and to assess Landsat-7/8 LAI estimates. Results highlighted good correspondence between Landsat-7/8 LAI estimates and ground measurements revealing high correlations (R2 ≥ 0.89) and low root mean squared errors (RMSE ≤ 0.75) in all seasons. Landsat-7/8 as well as Sentinel-2A high-resolution LAI retrievals, were compared with satellite LAI products operationally derived from MODIS (MOD15A2), Copernicus PROBA-V (GEOV1), and the recent EUMETSAT Polar System (EPS) LAI product. Good agreement was observed between high- and medium-resolution LAI estimates. In particular, the EPS LAI product was the most correlated product with both Landsat/7-8 and Sentinel-2A estimates, revealing R2 ≥ 0.93 and RMSE ≤ 0.53 m2/m2. In addition, a comparison exercise of EPS, GEOV1 and MODIS revealed high correlations (R2 ≥ 0.90) and RMSE ≤ 0.80 m2/m2 in all cases and years. The temporal assessment shows that the three satellite products capture well the seasonality during the crop phenological cycle. Discrepancies are observed mainly in absolute values retrieved for the peak of rice season. This is the first study that provides a quantitative assessment on the quality of available operational LAI product for rice monitoring to both the scientific community and users of agro-monitoring operational services.

An evaluation framework to build a cost-efficient crop monitoring system. Experiences from the extension of the European crop monitoring system

This paper presents an evaluation framework followed to identify cost-efficient alternatives to extend the MARS Crop Yield Forecasting System (MCYFS), run by the European Commission Joint Research Centre since 1992, to other main producing areas of the world: Eastern European Neighbourhood, Asia, Australia, South America and North America. These new systems would follow the principles and components of the MCYFS Europe: a meteorological data infrastructure, a remote sensing data infrastructure, a crop modelling platform, statistical tools, a team of analysts and a crop area estimation component. The framework designed evaluates the performance of the possible MCYFS-like system realizations against six defined objectives and their costs. Possible monitoring systems are based on a combination of different technical solutions for each of the MCYFS components, and are evaluated through an automatic algorithm that calculates the expected system performance –relying on a priori expert judgement–, the costs, and possible risks to construct some technical solutions, to finally identify the cost-efficient ones. A baseline system, achieving the minimum required performance, was identified as the most efficient starting point for the MCYFS extension in all the geographical areas. Such system would be built upon: (i) near real-time reanalysis meteorological products; (ii) remote sensing data from low-resolution (~1 km) platforms with a long-term product archive; (iii) crop models based on crop-specific model calibration from experimental data published in scientific literature; (iv) statistical methods based on trend and regression analysis applied to national level; (v) a team of analysts with specific technical profiles (on meteorology, remote sensing, and agronomy); and (vi) digital classification of very high resolution imagery supported by non-expensive ground surveys for area estimation. In countries where accessibility to local data and resources is high the baseline system can be upgraded enhancing some of the components: sub-national statistical analysis with additional statistical methods like multiple regression or scenario analysis; recruitment of experts on local agricultural conditions in the team of analysts; local calibration of crop models with experimental data; and exploiting high and low resolution biophysical products from remote sensing for crop monitoring.

Monitoring system for agronomic variables based in WSN technology on cassava crops

Agriculture, and natural resources associated to its development like water, soils and forests, have a relevant role in the future of countries and environmental conservation. The optimization of these resources is made with the implementation of technological strategies and tools that make it possible. In this sense, we developed a monitoring prototype for agronomic variables in cassava crops (Manihot Esculenta Crantz) in the Atlántico department (Colombia) based in WSN using Z1 motes as hardware platform and the temperature and soil moisture sensor SHT11. The operating system used was Contiki, and the routing protocol was RPL. The Network Performance Metrics evaluated were packet loss, RSSI (Received Signal Strength Indicator), LQI (Link Quality Indicator) and network convergence time. Then, a deployment model using Schläfli notation to determine the location and number of nodes, also we calculated the coverage range of the nodes to keep network uniformity. With these calculations, we obtained the linkage budgets between specks, and results were validated with RadioMobile software. Then, test fields were made in a cassava crop located in the city of Manati, Atlántico. Finally, with the help of server client architecture XAMPP, all data was stored and visualized through SIMCA (Agricultural Crop Information and Monitoring System), a web application developed by authors.

Price effect and competition coefficients of agricultural exports - the case for Vietnamese export rice

In order to support a previous analysis of the increase in export value in Vietnamese rice market, the paper aims to identify whether or not the increase was caused by quantity or price effects. Along with the monthly data between 02/2009 and 08/2015, this paper uses the double logarit and error correction model (ECM) to estimate the price competition coefficients from Thai and Pakistani rice. Regression results confirmed the price effects of Thai and Pakistani rice price on Vietnamese price in the long run but not vice versa. The Vietnamese price was also altered which was followed by a change of exchange rate and yearly seasons. To reduce the risk of fluctuation in price, the Vietnamese Government and producers need to concern about momentous solutions such as increasing the quality of rice in proportion to consumer safety, establishing crop monitoring systems effectively to warn weather risks. Rice producers should apply information technology to correctly get an accurate reflection of the current market, manage the production process as well as take care of macroeconomic variables such as the exchange rate.

A Low-Cost Approach to Automatically Obtain Accurate 3D Models of Woody Crops.

Crop monitoring is an essential practice within the field of precision agriculture since it is based on observing, measuring and properly responding to inter- and intra-field variability. In particular, “on ground crop inspection” potentially allows early detection of certain crop problems or precision treatment to be carried out simultaneously with pest detection. “On ground monitoring” is also of great interest for woody crops. This paper explores the development of a low-cost crop monitoring system that can automatically create accurate 3D models (clouds of coloured points) of woody crop rows. The system consists of a mobile platform that allows the easy acquisition of information in the field at an average speed of 3 km/h. The platform, among others, integrates an RGB-D sensor that provides RGB information as well as an array with the distances to the objects closest to the sensor. The RGB-D information plus the geographical positions of relevant points, such as the starting and the ending points of the row, allow the generation of a 3D reconstruction of a woody crop row in which all the points of the cloud have a geographical location as well as the RGB colour values. The proposed approach for the automatic 3D reconstruction is not limited by the size of the sampled space and includes a method for the removal of the drift that appears in the reconstruction of large crop rows.

IoT based Animal Health Monitoring and Crop Monitoring System

IoT based Animal Health Monitoring and Crop Monitoring System

High resolution strawberry field monitoring using the compact hyperspectral imaging solution COSI

In strawberry production, a balanced and accurate irrigation schedule is essential, because of the high sensitivity of strawberry plants to water deficits and waterlogging. The optimal irrigation management strategy can, however, only be obtained by an accurate crop monitoring system. To replace the current visual inspection methods, which are subjective, time consuming and labour-intensive, the performance of the COmpact hyperSpectral Imaging system (COSI) mounted on an RPAS (Remotely Piloted Aircraft System) was evaluated. The study, focusing on different irrigation treatments in strawberry cultivation, unraveled the potential of the COSI system, to monitor field variations, even at small scale. Growth inhibition and differences in plant physiology due to water deficit could be detected. As such, the COSI system has shown potential for steering irrigation management decisions in strawberry cultivation.

Developed using Quadcopter Crop Protection and Monitoring System from Wild Animals

Developed using Quadcopter Crop Protection and Monitoring System from Wild Animals

CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture.

CropWatch agroclimatic indicators (CWAIs) are a monitoring tool developed by the CropWatch global crop monitoring system in the Chinese Academy of Sciences (CAS; www.cropwatch.com.cn , Wu et al Int J Digital Earth 7(2):113-137, 2014, Wu et al Remote Sens 7:3907-3933, 2015). Contrary to most other environmental and agroclimatic indicators, they are "agronomic value-added", i.e. they are spatial values averaged over agricultural areas only and they include a weighting that enhances the contribution of the areas with the largest production potential. CWAIs can be computed for any time interval (starting from dekads) and yield one synthetic value per variable over a specific area and time interval, for instance a national annual value. Therefore, they are very compatible with socio-economic and other variables that are usually reported at regular time intervals over administrative units, such as national environmental or agricultural statistics. Two of the CWAIs are satellite-based (RAIN and Photosynthetically Active radiation, PAR) while the third is ground based (TEMP, air temperature); capitals are used when specifically referring to CWAIs rather than the climate variables in general. The paper first provides an overview of some common agroclimatic indicators, describing their procedural, systemic and normative features in subsequent sections, following the terminology of Binder et al Environ Impact Assess Rev 30:71-81 (2010). The discussion focuses on the systemic and normative aspects: the CWAIs are assessed in terms of their coherent description of the agroclimatic crop environment, at different spatial scales (systemic). The final section shows that the CWAIs retain key statistical properties of the underlying climate variables and that they can be compared to a reference value and used as monitoring and early warning variables (normative).

Cloud Cover Assessment for Operational Crop Monitoring Systems in Tropical Areas

The potential of optical remote sensing data to identify, map and monitor croplands is well recognized. However, clouds strongly limit the usefulness of optical imagery for these applications. This paper aims at assessing cloud cover conditions over four states in the tropical and sub-tropical Center-South region of Brazil to guide the development of an appropriate agricultural monitoring system based on Landsat-like imagery. Cloudiness was assessed during overlapping four months periods to match the typical length of crop cycles in the study area. The percentage of clear sky occurrence was computed from the 1 km resolution MODIS Cloud Mask product (MOD35) considering 14 years of data between July 2000 and June 2014. Results showed high seasonality of cloud occurrence within the crop year with strong variations across the study area. The maximum seasonality was observed for the two states in the northern part of the study area (i.e., the ones closer to the Equator line), which also presented the lowest averaged values (15%) of clear sky occurrence during the main (summer) cropping period (November to February). In these locations, optical data faces severe constraints for mapping summer crops. On the other hand, relatively favorable conditions were found in the southern part of the study region. In the South, clear sky values of around 45% were found and no significant clear sky seasonality was observed. Results underpin the challenges to implement an operational crop monitoring system based solely on optical remote sensing imagery in tropical and sub-tropical regions, in particular if short-cycle crops have to be monitored during the cloudy summer months. To cope with cloudiness issues, we recommend the use of new systems with higher repetition rates such as Sentinel-2. For local studies, Unmanned Aircraft Vehicles (UAVs) might be used to augment the observing capability. Multi-sensor approaches combining optical and microwave data can be another option. In cases where wall-to-wall maps are not mandatory, statistical sampling approaches might also be a suitable alternative for obtaining useful crop area information.

Identifying indicators for extreme wheat and maize yield losses

Yield forecasts are generally based on a combination of expert knowledge, survey data, statistical analyses and model simulations. These forecasts, when public, influence crop prices and can be used to estimate end-of-season stocks. Thus, the skills and limitations of such products are important because they inform trade policies. In Europe, yield forecasts are made available to stakeholders throughout the growing season via the monthly MARS (Monitoring Agricultural ResourceS) Bulletin. The MARS Crop Yield Forecasting System relies on an in-depth analysis of past climate, short-term weather forecasts and crop growth simulations. In this paper, we focus on the occurrence of abnormally low yields and evaluate how accurately agro-climatic indicators and model outputs anticipate their occurrences for two crop species in two European countries of contrasted agroclimatic conditions. Importantly, the indicators considered here encompass a large range of complexity levels and several are used to inform the yield forecasts presented in the MARS Bulletin. Each indicator is independently used to predict the onset occurrence of an abnormal yield loss (henceforth named extreme) in France and in Spain for both winter wheat and non-irrigated grain maize for a period covering the 1976–2013 growing seasons. Indicators are ranked based on a score quantifying their ability to accurately separate extreme from non-extreme yield loss events. We provide and in-depth analysis of the robustness of our ranking to alternative definitions of extreme yield loss. No single indicator performs systematically well (e.g., whatever the country or crop species) but several show acceptable scores (e.g., averaged temperatures, vapor pressure deficit, precipitation, potential yield). We find no obvious relationship between the level of complexity of indicators and their accuracy. Single climate variables such as temperature or precipitation often perform as well as a crop model integrating temperatures and precipitation effects on crop growth. Namely, monthly averaged maximum temperatures and precipitation rank highest for both crop species in France. Drought indices perform well in Spain for wheat and for maize. We argue that our transparent framework can be useful to evaluate and improve crop-monitoring systems worldwide.