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It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.

The common Soil in Egypt is clay soil so common irrigation system is tradition surface irrigation with 60% irrigation efficiency. Agricultural sector consumes more than 80% of water resources under surface irrigation (tradition methods). In arid and semi-arid regions consumptive use is the best index for irrigation requirements. A large part of the irrigation water applied to farm land is consumed by Evapotranspiration (ET). Irrigation water consumption under each of the physical and climatic conditions for large scale will be easier with remote sensing techniques. In Egypt, Agricultural cycle is often tow agricultural seasons yearly; summer and winter. Common summer crops are Maize, Rice and Cotton while common winter crops are Clover and Wheat. Landsat8 bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate Normalized Deference Vegetation Index (NDVI) and monitoring cultivated areas. The cultivated land area was 3,277,311 ha in August 2013. In this paper Kc = 2 * NDVI ? 0.2 represents the relation between crop coefficient (Kc) and NDVI. Kc and Reference evapotranspiration (ETo) used to estimate ETc in Egypt. The main objective of this paper is studying the potential crop Evapotranspiration in Egypt using remote sensing techniques.

Variable rate technology (VRT) in nutrient management has been developed in order to apply crop inputs according to the required amount of fertilizers. Meteorological conditions rarely differ within one field; however, differences in soil conditions responding to precipitation or evaporation results within field variations. These variations in soil properties such as moisture content, evapotranspiration ability, etc. requires site-specific treatments for the produced crops. There is an ongoing debate among experts on how to define management zones as well as how to define the required amount of fertilizers for phosphorus and nitrogen replenishment for winter wheat (Triticum aestivum L.) production. For management zone delineation, vegetation based or soil based data collection is applied, where various sensor technology or remote sensing is in help for the farmers. The objective of the study reported in this paper was to investigate the effect of soil moisture data derived from Sentinel-2 satellite images moisture index and variable rate phosphorus and nitrogen fertilizer by means of variable rate application (VRA) in winter wheat in Mezőföld, Hungary. Satellite based moisture index variance at the time of sowing has been derived, calculated and later used for data comparison. Data for selected points showed strong correlation (R2 = 0.8056; n = 6) between moisture index and yield, however generally for the whole field correlation does not appear. Vegetation monitoring has been carried out by means of NDVI data calculation. On the field level, as indicated earlier neither moisture index values at sowing nor vegetation index data was sufficient to determine yield. Winter wheat production based on VRA treatment resulted significant increase in harvested crop: 5.07 t/h in 2013 compared to 8.9 t/ha in 2018. Uniformly managed (control) areas provided similar yield as VRA treated areas (8.82 and 8.9 t/ha, respectively); however, the input fertilizer was reduced by 108 kg/ha N and increased by 37 kg/ha P.

Sorghum is known as a major potential feedstock for biofuel production. Being able to efficiently discover genetic control of many traits over a large number of genotypes, genome-wide association study (GWAS) has become a powerful tool for studying sorghum biomass yield components. However, automated high-throughput field-based plant phenotyping is now the bottleneck for scaling up such experiments. This paper presents an auto-guidance enabled utility tractor which navigates itself between crop rows with a predefined path while collecting stereo images of sorghum samples from both sides of the vehicle. Three levels of stereo camera heads were instrumented to capture images of plants up to 3 meters tall. The stereo images were processed offline to reconstruct 3D point clouds using Semi-Global Block Matching. A semi-automated software interface was developed to measure stem diameter due to the strict sampling strategy and the complexity of high-density crop canopy. An automated hedge-based feature extraction pipeline was proposed to quantify other variations in plant architecture traits such as plant height, leaf area index (LAI) and vegetation volume index (VVI). The stem diameter measured using the semiautomatic method showed high correlation (0.958) to hand measurement.

Remote sensing is often used for yield prediction as well as for crop monitoring. This paper describes how Landsat satellite data can be used to derive a growth model calculated from normalised difference vegetation index that can predict winter wheat (Triticum aestivum) and winter oilseed rape (Brassica napus) phenological state using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie scale. Time series of Landsat images were chosen from the years 2004, 2008 and 2012, when winter oilseed rape was grown, and 2005, 2009, 2011 and 2013, when winter wheat was grown in the same experimental field. The images were selected from the whole growing season of both crops. An advantage of this method is the easy availability of the remote sensing and its easy application for deriving a prediction model from vegetation indices. Our results showed that Landsat images, after correct pre-processing, can be used for winter wheat and winter oilseed rape growth model prediction.

With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis. As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging (LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional (3D) data accurately, and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China, we developed a high-throughput crop phenotyping platform, named Crop 3D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs, functions and testing results of the Crop 3D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.

Crop phenology is essential for evaluating crop production in the food insecure regions of West Africa. The aim of the paper is to study whether satellite observation of plant phenology are consistent with ground knowledge of crop cycles as expressed in agro-simulations. We used phenological variables from a MODIS Land Cover Dynamics (MCD12Q2) product and examined whether they reproduced the spatio-temporal variability of crop phenological stages in Southern Mali. Furthermore, a validated cereal crop growth model for this region, SARRA-H (System for Regional Analysis of Agro-Climatic Risks), provided precise agronomic information. Remotely-sensed green-up, maturity, senescence and dormancy MODIS dates were extracted for areas previously identified as crops and were compared with simulated leaf area indices (LAI) temporal profiles generated using the SARRA-H crop model, which considered the main cropping practices. We studied both spatial (eight sites throughout South Mali during 2007) and temporal (two sites from 2002 to 2008) differences between simulated crop cycles and determined how the differences were indicated in satellite-derived phenometrics. The spatial comparison of the phenological indicator observations and simulations showed mainly that (i) the satellite-derived start-of-season (SOS) was detected approximately 30 days before the model-derived SOS; and (ii) the satellite-derived end-of-season (EOS) was typically detected 40 days after the model-derived EOS. Studying the inter-annual difference, we verified that the mean bias was globally consistent for different climatic conditions. Therefore, the land cover dynamics derived from the MODIS time series can reproduce the spatial and temporal variability of different start-of-season and end-of-season crop species. In particular, we recommend simultaneously using start-of-season phenometrics with crop models for yield forecasting to complement commonly used climate data and provide a better estimate of vegetation phenological changes that integrate rainfall variability, land cover diversity, and the main farmer practices.

The use of depth cameras in precision agriculture is increasing day by day. This type of sensor has been used for the plant structure characterization of several crops. However, the discrimination of small plants, such as weeds, is still a challenge within agricultural fields. Improvements in the new Microsoft Kinect v2 sensor can capture the details of plants. The use of a dual methodology using height selection and RGB (Red, Green, Blue) segmentation can separate crops, weeds, and soil. This paper explores the possibilities of this sensor by using Kinect Fusion algorithms to reconstruct 3D point clouds of weed-infested maize crops under real field conditions. The processed models showed good consistency among the 3D depth images and soil measurements obtained from the actual structural parameters. Maize plants were identified in the samples by height selection of the connected faces and showed a correlation of 0.77 with maize biomass. The lower height of the weeds made RGB recognition necessary to separate them from the soil microrelief of the samples, achieving a good correlation of 0.83 with weed biomass. In addition, weed density showed good correlation with volumetric measurements. The canonical discriminant analysis showed promising results for classification into monocots and dictos. These results suggest that estimating volume using the Kinect methodology can be a highly accurate method for crop status determination and weed detection. It offers several possibilities for the automation of agricultural processes by the construction of a new system integrating these sensors and the development of algorithms to properly process the information provided by them., The Spanish Ministry of Economy and Competitiveness has provided support for this research via projects AGL2014-52465-C4-3-R and AGL2014-52465-C4-1-R, and Bosch Foundation. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).

The dry subtropics are subject to a rapid expansion of crops and pastures over vast areas of natural woodlands and savannas. In this paper, we explored the effect of this transformation on vegetation productivity (magnitude, and seasonal and long-term variability) along aridity gradients which span from semiarid to subhumid conditions, considering exclusively those areas with summer rains (>66%). Vegetation productivity was characterized with the proxy metric "Enhanced Vegetation Index" (EVI) (2000 to 2012 period), on 6186 natural and cultivated sampling points on five continents, and combined with a global climatology database by means of additive models for quantile regressions. Globally and regionally, cultivation amplified the seasonal and inter-annual variability of EVI without affecting its magnitude. Natural and cultivated systems maintained a similar and continuous increase of EVI with increasing water availability, yet achieved through contrasting ways. In natural systems, the productivity peak and the growing season length displayed concurrent steady increases with water availability, while in cultivated systems the productivity peak increased from semiarid to dry-subhumid conditions, and stabilized thereafter giving place to an increase in the growing season length towards wetter conditions. Our results help to understand and predict the ecological impacts of deforestation on vegetation productivity, a key ecosystem process linked to a broad range of services. Fil: Baldi, Germán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina Fil: Texeira González, Marcos Alexis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía; Argentina Fil: Murray, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina

The paper presents a detailed understanding of nitrogenous fertilizer use in Indian agriculture and esti-mation of seasonal nitrogen loosses from rice crop in Indo-Gangetic plain region, the ‘food bowl’oftheIndian sub-continent. An integrated methodology was developed for quantification of different forms ofnitrogen losses from rice crop using remote sensing derived inputs, field data of fertilizer application,collateral data of soil and rainfall and nitrogen loss coefficients derived from published nitrogen dynam-ics studies. The spatial patterns of nitrogen losses in autumn or ‘kharif’ and spring or ‘rabi’ season rice at1×1km grid were generated using image processing and GIS. The nitrogen losses through leaching informofurea-N,ammonium-N(NH 4 -N)andnitrate-N(NO 3 -N)aredominantoverammoniavolatilizationloss. The study results indicate that nitrogen loss through leaching in kharif and rabi rice is of the orderof 34.9% and 39.8% of the applied nitrogenous fertilizer in the Indo-Gangetic plain region. This studyprovides a significant insight to the role of nitrogenous fertilizer as a major non-point source pollutantfrom agriculture.© 2010 Elsevier B.V. All rights reserved.

In this paper we report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging. We focus attention specifically on the detailed characteristic of canopy height distribution―canopy shoot area as a function of height―which can provide an elaborate picture of canopy growth and health under a given set of conditions. We apply the method to a wheat field trial involving ten Australian wheat varieties that were subjected to two different fertilizer treatments. A novel camera self-calibration approach is proposed which allows the determination of quantitative plant canopy height data (as well as other valuable phenotypic information) by stereo matching. Utilizing the canopy height distribution to provide a measure of canopy height, the results compare favourably with manual measurements of canopy height (resulting in an ᴿ² value of 0.92),and are indeed shown to be more consistent. By comparing canopy height distributions of different varieties and different treatments, the methodology shows that different varieties subjected to the same treatment, and the same variety subjected to different treatments can respond in much more distinctive and quantifiable ways within their respective canopiesthan can be captured by a simple trait measure such as overall canopy height. Refereed/Peer-reviewed

In recent years, within the European Union several legislative, monitoring and coordinating actions have been undertaken to encourage sustainable use of resources, reduction in the use of chemicals and improvement of the urban environment. In this respect, two concepts that are strictly related to most of the aspects above are: “precision agriculture” and “precision conservation” and more specifically “precision turfgrass management.” Optical sensing has become a crucial part of precision turfgrass management and spectral reflectance in particular has been an active area of research for many years. However, while turfgrass status evaluation by proximity-sensed spectral reflectance appears to be an established and reliable practice, much more could be achieved in terms of monitoring of large turfgrass areas through remote sensing, and in particular through satellite imagery. This paper reports the results of a trial attempting to evaluate the spectral signatures of several turfgrass species and cultivars, for future use in turfgrass satellite monitoring. Our experimental study focused on 20 turfgrass species/varieties including perennial ryegrasses, tall fescues, kentucky bluegrasses, bermudagrass ecotypes, seeded commercial bermudagrasses, vegetatively propagated bermudagrasses, Zoysia japonica and non-japonica zoysiagrasses. Various biological and agronomical parameters were studied and turfgrass spectral reflectance for all entries was gathered. Vegetation indices were calculated by simulating the available wavelengths deriving from World View 2 satellite imagery. Results showed that within the same species selected vegetation indices are often able to discriminate between different varieties that have been established and maintained with identical agronomical practices.

This paper summarizes the authors' research in imaging- and optical weed sensors based on studies of plant spectral and morphological characteristics. Location, color, texture, and shape features of weeds and crops were identified as the major criteria for sensor structure and algorithm design. Imaging-based weed sensors were compared with optical weed sensors. Factors affecting detection accuracy were studied through experiments. Laboratory experiments were also conducted to test the accuracy of statistical and neural-network classification models. Extensive field experiments were conducted to test the effectiveness of the weed sensors.

We have developed an image analysis system for mapping white clover pastures. The information from digital colour photographs is processed by software (Trifolium.exe) specially designed for the purpose. The software estimates the coverage of clover, weeds and bare ground, and the unidentified remainder of the total area is regarded as covered by grass. To evaluate the reliability of the estimates of clover content, the clover on paper printouts of non-processed images were marked manually and analysed by a photo scanner and commercially available software. The outputs from Trifolium.exe and the estimates from scanned manual markings were highly correlated (r 2=0.81). A sensitivity test was conducted to quantify the impact of changes of six user-adjustable parameters of Trifolium.exe. The software output of clover coverage was sensitive for changes in three, soil coverage for changes in one, and weed coverage for changes in all parameters. The fact that the digital image acquisition and analysis produce ne...

In the Sahel, poor soil quality and rainfall levels have a great influence on pasture production and hence on secondary output. In areas where rainfall is the limiting factor for primary production, recovery of primary and secondary production after the dry season depends on soil type. On sandy soils a large fraction of rainfall infiltrates and becomes available for plant growth, stimulating fast herbage growth, while on clayey and loamy soils low infiltration rates generate runoff, leading to slower herbage growth rates. The very different moisture retention characteristic of sands and clays is another possible cause for the observed differences in growth rates. lit this paper we investigate the herbage growth rate from the onset of the rainy season. We hypothesise that, in areas where rainfall is the limiting factor for primary production. the vegetation growth rate on clayey soils is lower than that on sandy soils. We will test this hypothesis using long-term rainfall, soil types and satellite derived normalised difference vegetation index data. This research shows that the growth rates on sandy soil are significantly greater than that on clayey soils during the early part of the rainy season. We also show that these differences can be detected at large scales using satellite imagery. We also conclude that, at this scale, movement strategies of pastoralists would be intrinsically linked to not only rainfall patterns and distribution. but also to the underlying soil types in the region as this affects the quality and quantity of fodder available. (C) 2002 Elsevier Science B.V. All rights reserved.

This paper compares of pixel- and object-based techniques for mapping wild oat weed patches in wheatfields using multi-spectral QuickBird satellite imagery for site-specific weed management. The researchwas conducted at two levels: (1) at the field level, on 11 and 15 individual infested wheat fields in 2006 and2008, respectively, and (2) on a broader level, by analysing the entire 2006 and 2008 images. To evaluatethe wild oat patches mapping at the field level, both pixel- and object-based image analyses were testedwith six classification algorithms: Parallelepipeds (P), Mahalanobis Distance (MD), Maximum Likelihood(ML), Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Decision Tree (DT). The resultsshowed that weed patches could be accurately detected with both analyses obtaining global accuraciesbetween 80% and 99% for most of the fields. The MD and SVM classifiers were the most accurate forboth the pixel- and object-based images from 2006 and 2008, respectively. In the broad-scale analysis,all of the wheat fields were identified in the imagery using a multiresolution hierarchical segmentationbased on two scales. The first segmentation scale was classified using the MD and ML algorithms todiscriminate wheat fields from other land uses. Accuracies greater than 85% were obtained for MD and88% for ML for both imagery. A hierarchical analysis was then performed with the second segmentationscale, increasing the accuracies to 93% and 91% for 2006 and 2008 imagery, respectively. Finally, based onthe most accurate results obtained in the field-level study, pixel-based classifications using the MD, MLand SVM algorithms were applied to the wheat fields identified. The results of these broad-level analysesshowed that wild oat patches were accurately discriminated in all the wheat fields present in the entireimages with accuracies greater than 91% for all the classifiers tested., This work was partially financed by the Spanish Ministry of Science and Innovation through projects AGL2008-04670-CO3-03 and AGL2011-30442-CO2-01 (FEDER). JAE-Doc CSIC FEDER Program financed the work of J.M. Pena.

It is necessary that the weed area is discriminated from lawn area if selective spraying is applied to maintain a lawn field. However, both weed and lawn usually have similar green color in summer. In this paper, a color independent method using gray-scale uniformity in image was proposed for detecting the weed area. This method works based on different levels of uniformity for weed and lawn surfaces and could precisely detect only the weed leaves. The analysis results showed that a weed could be detected if its leaves are at least 5 pixels wide in image, while its length is enough to generate a minimum size blob. It was also found that the image had to be acquired at a short camera distance so that the soil among the lawn was visible and the lawn area had less uniformity. After weed leaf detection, a morphological image analysis was performed on a group the adjacent blobs as result of leaf detection. It was observed that the morphological image analysis was useful in locating a representative center-point for grouped blobs in correspondence to main root location of the weeds.

Information on forest biomass is required for several purposes, including estimation of forest bioenergy resources and forest carbon stocks. Airborne laser scanning is today considered the most accurate remote sensing method for forest inventory. The three-dimensional nature of laser scanning data enables estimation of the volumes of the tree canopies. The dimensions of the tree canopies show high correlation with the amount of forest biomass. Optical aerial photographs are often used to complement laser data, for improved distinction between tree species. The paper reports on a study testing the estimation of forest biomass variables in two study areas in Southern Finland. The biomass variables were derived on the basis of tree-level field measurements, with biomass models used for pine, spruce, and birch. The sample-plot-level biomass components were derived on the basis of tree-level data and used as reference data for airborne-laser- and aerial‑photograph-based estimation. Results were slightly better for total biomass (RMSE 22.5% and 23.6% for the two study areas) than total volume (RMSE: 23.4% and 26.1%). Species-specific estimation errors were large in general but varied between the study areas, because of differences in their forest structures.

The present study aims to determine spectral bands that are best suited for characterizing rice agronomic variables. The data for this study came from ground-level hyperspectral reflectance measurements of rice at different stage. Reflectance was measured in discrete narrow bands between 350 and 2500 nm. Observed rice agronomic variables included leaf area index (LAI), wet biomass (WBM including aboveground wet biomass-AGWBM, leaf wet biomass-LWBM, stem wet biomass-SWBM), and dry biomass(DBM: including aboveground dry biomass-AGDBM, leaf dry biomass-LDBM, stem dry biomass.) Firstly, narrow band ratio vegetation index (NBRVI) involving all possible two bands combinations of discrete channels were tested. The second part of the paper describes a rigorous search procedure to identify the best NBRVI predictors of rice agronomic variables. Special narrow band lambda (λ1) versus lambda (λ2) plots of R2 values illustrates the most effective wavelength combinations (λ1 and λ2) and band-widths (Δλ1 and Δλ2) for predicting rice agronomic variables at different development stages. The best of the NBRVI models explained 58% to 83% variability rice agronomic variables at different development stage. A strong relationship with rice agronomic variables is located in red-edge, 700 nm to 750 nm, the longer portion of red (650nm to 700nm), the shorter portion of green (500nm to 550nm), a particular portion of NIR (800nm to 850nm). They are followed by moisture-sensitive NIR(1150nm to 1200nm), and two portions of SWIR (1600nm to 1650nm).

Rotylenchulus reniformis nematodes present in the soil are one of the major nematode parasite species significantly affecting the growth and development of cotton plants. Recent studies have shown that the nematode numbers in the plant's rhizosphere has direct impact on the reflectance of the plants. In this paper, authors utilize this correlation in developing a field worthy methodology for predicting nematode population number extant in the plant's rhizosphere from variable plant's reflectance. To accomplish this task, a supervised Self-organized map (SOM) was trained using the hyperspectral data signatures of cotton plants affected by different known nematode numbers. The hyperspectral signatures used for training were collected from the cotton plants grown in controlled environment. Twelve field samples (uncontrolled environment) with known nematode numbers obtained from lab analysis of the soil were presented to the supervised trained Self-Organized Map. The location of the sample on the labeled supervised-SOM was used to determine the estimated nematode population of the field sample. In addition to the map grid, the locations of the samples were also visualized using U-matrix, to determine whether the samples were not corrupt or located in the junk part of the map. In addition to the primary goal, hyperspectral signatures of both training and testing data were divided into three sub-regions: Visible region, NIR region and Mid-IR region to observe whether any particular region was the most effective in predicting nematode population.

Bidens pilosa L. (commonly known as cobbler's peg) is an annual broad leaf weed widely distributed in tropical and subtropical regions of the world and is reported to be a weed of 31 crops, including wheat. Automatic detection of Bidens in wheat farms is a non-trivial problem due to their similarity in colour and presence of occlusions. This paper proposes a methodology which could be used to discriminate Bidens from wheat to be used in operations such as autonomous weed destruction. A spectrometer is used to analyse the optical properties of Bidens and wheat leaves while achieving high classification results. However, due to the practical constraints of using spectrometers, a colour camera-based technique is proposed. It is shown that the colour-based segmentation followed by shape-based validation algorithm gives rise to high detection rates with lower false detections. We have experimentally evaluated the algorithm with Bidens detection rate of 80% and a false alarm rate of 10%.

The emergence of precision agriculture technologies and an increasing demand for higher quality grape products has led to a growing interest in Precision Viticulture. Actually, cultural monitoring is the most important application in PV systems: it requires specific technologies able to investigate the cultural conditions. To this aim, typically remote sensing surveys are adopted. These, anyhow, involve technical, economical and organisational barriers hampering a wide diffusion of their application. In order to overcome these problems, it would be necessary to substitute and/or integrate remote sensing information with alternative ground sensing technologies, to be employed directly inside the vineyard. This paper considers a commercial optical sensor, the GreenSeeker, useful in ground sensing surveys, and it compares its performances in monitoring vine with results obtained by a multispectral digital camera used as a tester. The experimentation was carried out in a greenhouse, on an artificial row including 15 grapevines (Cabernet Sauvignon variety). In front of the row, it was fixed a metallic rail gauge in order to permit a longitudinal movement of the Greenseeker sensor. Each plant was investigated at three different heights with a 5 s data time acquisition. Simultaneously, photos of the same grapevine were took by a multispectral digital camera, in order to obtain NDVI values through image analysis. The multispectral digital camera, normally used for remote sensing survey in agriculture, was considered as a test. Results demonstrate a strength correlation (R2 = 0.97) between the NDVI values measured through the two methods. This shows the same behaviour of the two tools, according to crop vigour and stress conditions induced into the plants. Consequently the GreenSeeker can be considered as a suitable solution for cultural monitoring in viticulture.