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Vine Signal Extraction – an Application of Remote Sensing in Precision Viticulture
摘要： This paper presents a study of precision agriculture in the wine industry. While precision viticulture mostly aims to maximise yields by delivering the right inputs to appropriate places on a farm in the correct doses and at the right time, the objective of this study was rather to assess vine biomass differences. The solution proposed in this paper uses aerial imagery as the primary source of data for vine analysis. The first objective to be achieved by the solution is to automatically identify vineyards blocks, vine rows, and individual vines within rows. This is made possible through a series of enhancements and hierarchical segmentations of the aerial images. The second objective is to determine the correlation of image data with the biophysical data (yield and pruning mass) of each vine. A multispectral aerial image is used to compute vegetation indices, which serve as indicators of biophysical measures. The results of the automatic detection are compared against a test field, to verify both vine location and vegetation index correlation with relevant vine parameters. The advantage of this technique is that it functions in environments where active cover crop growth between vines is evident and where variable vine canopy conditions are present within a vineyard block.
关键词： precision viticulture,remote sensing,segmentation,GIS,Precision agriculture,classification
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE Conference on Information and Communication Technology (CICT) - Allahabad, India (2019.12.6-2019.12.8)] 2019 IEEE Conference on Information and Communication Technology - LEDCOM: A Novel and Efficient LED Based Communication for Precision Agriculture
摘要： Wireless Sensor Networks and Satellite Remote Sensing are some of the existing techniques that are used to collect, analyze and interpret data from the agricultural crop sites. However, there are certain limitations common to both of these techniques that are concerned with the latency and the resolution of the data collected. UAVs (Unmanned Aerial Vehicles) are becoming another alternative that has become integral nowadays due to its affordable and scalable nature while offering user friendly requirements and customizations. This proposes a novel and cost-effective technique (LEDCOM) that harnesses the capabilities of ground sensors and unmanned UAV while using computer vision methods to produce a qualitative data analysis system that describes the crop site under supervision. An UAV is assumed to collect the ground based sensor node data in the form of binary patterns on LED Arrays that is encoded in the image taken by a camera of a drone. Image processing techniques are used to identify and decode the LED sequences from the arrays. The performance of the proposed system is evaluated under different features and image resolutions within the same lighting conditions. A promising performance is observed for LED pattern identi?cation from the challenging images taken from a height.
关键词： Computer Vision,LED Pattern Identi?cation,UAVs,Wireless Sensor Networks,Precision Agriculture,Remote Sensing
更新于2025-09-23 15:21:01
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Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems
摘要： Background: Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, automatic and consistent early detection and quantification of disease symptoms which are important in breeding programs for the development of improved cultivars and in crop production for the implementation of disease control measures for yield protection. Current methods of plant disease phenotyping are predominantly visual and hence are slow and prone to human error and variation. There has been increasing interest in hyperspectral imaging applications for early detection of disease symptoms. However, the high dimensionality of hyperspectral data makes it very important to have an efficient analysis pipeline in place for the identification of disease so that effective crop management decisions can be made. The focus of this work is to determine the minimal number of most effective hyperspectral wavebands that can distinguish between healthy and diseased soybean stem specimens early on in the growing season for proper management of the disease. 111 hyperspectral data cubes representing healthy and infected stems were captured at 3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and control specimens from 4 different genotypes. Each hyperspectral image was captured at 240 different wavelengths in the range of 383–1032 nm. We formulated the identification of best waveband combination from 240 wavebands as an optimization problem. We used a combination of genetic algorithm as an optimizer and support vector machines as a classifier for the identification of maximally-effective waveband combination. Results: A binary classification between healthy and infected soybean stem samples using the selected six waveband combination (475.56, 548.91, 652.14, 516.31, 720.05, 915.64 nm) obtained a classification accuracy of 97% for the infected class. Furthermore, we achieved a classification accuracy of 90.91% for test samples from 3 days after inoculation using the selected six waveband combination. Conclusions: The results demonstrated that these carefully-chosen wavebands are more informative than RGB images alone and enable early identification of charcoal rot infection in soybean. The selected wavebands could be used in a multispectral camera for remote identification of charcoal rot infection in soybean.
关键词： Band selection,Soybean disease,Precision agriculture,Hyperspectral,Support vector machines,Genetic algorithm,Charcoal rot
更新于2025-09-23 15:21:01
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Use of principal components of UAV-acquired narrow-band multispectral imagery to map the diverse low stature vegetation fAPAR
摘要： The fraction of absorbed photosynthetically active radiation (fAPAR) is an important plant physiological index that is used to assess the ability of vegetation to absorb PAR, which is utilized to sequester carbon in the atmosphere. This index is also important for monitoring plant health and productivity, which has been widely used to monitor low stature crops and is a crucial metric for food security assessment. The fAPAR has been commonly correlated with a greenness index derived from spaceborne optical imagery, but the relatively coarse spatial or temporal resolution may prohibit its application on complex land surfaces. In addition, the relationships between fAPAR and remotely sensed greenness data may be influenced by the heterogeneity of canopies. Multispectral and hyperspectral unmanned aerial vehicle (UAV) imaging systems, conversely, can provide several spectral bands at sub-meter resolutions, permitting precise estimation of fAPAR using chemometrics. However, the data pre-processing procedures are cumbersome, which makes large-scale mapping challenging. In this study, we applied a set of well-verified image processing protocols and a chemometric model to a lightweight, frame-based and narrow-band (10 nm) UAV imaging system to estimate the fAPAR over a relatively large cultivated land area with a variety of low stature vegetation of tropical crops along with native and non-native grasses. A principal component regression was applied to 12 bands of spectral reflectance data to minimize the collinearity issue and compress the data variation. Stepwise regression was employed to reduce the data dimensionality, and the first, third and fifth components were selected to estimate the fAPAR. Our results indicate that 77% of the fAPAR variation was explained by the model. All bands that are sensitive to foliar pigment concentrations, canopy structure and/or leaf water content may contribute to the estimation, especially those located close to (720 nm) or within (750 nm and 780 nm) the near-infrared spectral region. This study demonstrates that this narrow-band frame-based UAV system would be useful for vegetation monitoring. With proper pre-flight planning and hardware improvement, the mapping of a narrow-band multispectral UAV system could be comparable to that of a manned aircraft system.
关键词： MiniMCA,precision agriculture,leaf area index,productivity,Chemometrics
更新于2025-09-23 15:21:01
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Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks
摘要： Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.
关键词： UAS,tree identification,citrus,precision agriculture,CNN,feature extraction,deep learning,superpixels
更新于2025-09-23 15:21:01
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Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems
摘要： The 3D digital characterization of vegetation is a growing practice in the agronomy sector. Precision agriculture is sustained, among other methods, by variables that remote sensing techniques can digitize. At present, laser scanners make it possible to digitize three-dimensional crop geometry in the form of point clouds. In this work, we developed several methods for calculating the volume of vine wood, with the ?nal intention of using these values as indicators of vegetative vigor on a thematic map. For this, we used a static terrestrial laser scanner (TLS), a mobile scanning system (MMS), and six algorithms that were implemented and adapted to the data captured and to the proposed objective. The results show that, with TLS equipment and the algorithm called convex hull cluster, the volumes of a vine trunk can be obtained with a relative error lower than 7%. Although the accuracy and detail of the cloud obtained with TLS are very high, the cost per unit for the scanned area limits the application of this system for large areas. In contrast to the inoperability of the TLS in large areas of terrain, the MMS and the algorithm based on the L1-medial skeleton and the modelling of cylinders of a certain height and diameter have solved the estimation of volumes with a relative error better than 3%. To conclude, the vigor map elaborated represents the estimated volume of each vine by this method.
关键词： vine size,mobile mapping,plant vigor,terrestrial laser scanning,precision agriculture,Vitis vinifera
更新于2025-09-23 15:19:57
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Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach
摘要： Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications.
关键词： image classifiers,machine learning system,precision agriculture,recognition system,mutual subspace method
更新于2025-09-19 17:15:36
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[IEEE 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) - Vancouver, BC, Canada (2018.11.1-2018.11.3)] 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) - Solar-Powered Smart Agricultural Monitoring System Using Internet of Things Devices
摘要： Advances in wireless communication technologies for monitoring and control systems have paved the way for a new method of farming known as smart farming. Smart farming can be achieved through the use of Precision Agriculture (PA) which involves using novel technology along with inch-scale devices to monitor crops and provide precise treatments when required. Smart PA is able to take traditional farming practices and apply technological advances from areas such as Wireless Sensor Networks (WSN) and the Internet of Things (IoT) to assist in increasing the output yield of a crop while improving efficiency and reducing the amount of stress placed on a farmer. In this paper, a solar-powered smart agricultural monitoring system with IoT devices is presented. Solar-powered prototype nodes were designed to measure environmental conditions in a field and report to a base station for data storage and further processing. Two prototypes were compared in identifying the advantages gained when using energy harvesting in a device. Using an experimental testbed, a proof of concept of how the system would function is presented. According to experimental results, using an energy harvesting device can provide an increased lifetime for a device by supplying power and recharging its battery.
关键词： Internet of Things,Precision Agriculture,Smart Monitoring,Wireless Sensor Network
更新于2025-09-19 17:15:36
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Do optical sensor readings change throughout the day? An evaluation of two sensor systems
摘要： Optical sensors are useful tools for rapid and periodic assessment of plant nutritional status. However, the utility and potential of these sensors may be reduced if their readings change throughout the day. Therefore, this study aimed to assess the effects of time of day on measurements of the SPAD-502 chlorophyll meter and the GreenSeeker throughout the bean crop cycle. The treatments consisted of time of day for sensor measurements (8:00, 12:00, and 16:00 h) throughout six dates over the crop cycle. The results showed that measurements from the SPAD-502 and the GreenSeeker significantly changed according to time of day. The SPAD index at 16:00 h showed the lowest coefficient of variation (CV, 2.5%) and was on average, 3.05 ± 0.43, and 1.12 ± 0.25 SPAD units higher when compared to readings obtained at 8:00 and 12:00 h. Differently, the NDVI from the GreenSeeker showed the lowest CV (4.34%) at 8:00 h and was, on average, 0.06 ± 0.028 and 0.03 ± 0.01 units higher than measurements taken at 16:00 h and 12:00 h, respectively. Furthermore, the different times of sensor measurements present variations in air temperature and solar radiation, which directly influence the leaf water content and paraheliotropic movements. Thus, the indices from both sensors tend to show a high variability during different times of the day. Therefore, it is essential to create a consistent sampling protocol to reduce the variability of sensor measurements during the crop cycle.
关键词： Phaseolus vulgaris L,solar radiation,chlorophyll meter,precision agriculture,canopy reflectance sensor
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Terahertz Spectroscopy of Different Phenotypes of Arabidopsis Thaliana
摘要： The recent surge in ‘Precision agriculture’ is fueled by the acute need of food security felt worldwide to feed the global burgeoning population. To this effect, various optical techniques have recently been employed to characterize the biochemical processes in various parts of the plants [1]. Terahertz (THz) region (0.3 – 10 THz) has gathered momentum in this field as it is biologically safe, can penetrate food packaging and most importantly, has fingerprinting ability of various organic and inorganic molecules due to active rotational and vibrational modes [2]. Moreover, unlike other optical spectroscopic techniques, THz time domain spectroscopic (THz-TDS) technique records the information in form of THz electric field; which once converted to frequency domain provides both amplitude and phase information [3]. Additionally, THz waves are highly absorbed by water which in turn can be utilized for quantitative water status monitoring in plants [4]. In this work, we have used THz-TDS with a bandwidth over 5 THz for the detection of biochemicals present in two different T-DNA insertion mutants in two independent genes of Arabidopsis thaliana (Col-0 Columbia ecotype). The plants were grown from mutant seeds in controlled environment. For easier optical access to all parts of the plants, the seedlings were grown parallel to the surface in a petri dish of plant growth medium. Liu et al. has already reported successful discrimination of transgenic Soybean seeds using THz spectroscopy [5]. We have recorded spectroscopic data of several parts of all the mutant plants which were 5, 7, 12 and 13 days old kept under identical conditions. The time domain spectrum obtained from the experiment was converted to frequency domain. Figure 1 shows the frequency domain spectrum of one such set of spectroscopic data collected from the stem part of the two different mutants compared to wild type control Col-0 plants. As it is evident from the spectrum, there are many absorption peaks corresponding to water and other biochemical molecules. For example, the peaks at 1.7 THz and 2.6 THz corresponds to sucrose present in the plants and in the nutri-solution which has been taken as the reference [2]. Our ongoing study involves careful analysis of the variations of the molecular absorption peaks for different mutants which would not only enable a label-free genetic identification, but also would help in our understanding of the underlying shift in biochemical processes contributed by the specific gene.
关键词： Terahertz spectroscopy,Arabidopsis thaliana,THz-TDS,Precision agriculture,biochemical processes
更新于2025-09-16 10:30:52