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1. Maturity Grape Indicators Obtained By Means Of Earth Observation TechniquesWine producers often need to buy grapes from growers. A good selection of grapes allows obtaining the desired wine quality. This paper presents a procedure to obtain by means of earth observation techniques indices and parameters used in the Spanish vineyards to monitor the state of the grapes. In this way is possible to monitor the ripeness of the grapes or the best time to harvest in such a way that growers can get the highest quality grapes, while producers of wine can select the most appr... J. Sanz, A. Romo, J.L. Casanova, S. Fraile |
2. Spectral Models for Estimation of Chlorophyll Content, Nitrogen, Moisture Stress and Growth of Wheat CropField experiments were conducted during 2009-10 and 2010-11 at research farm of the department of Farm Machinery and Power Engineering, Punjab Agricultural university, Ludhiana. Three w... B.S. Sekhon, J. Mukherjee, A. Sharma, S.K. Thind, R. Kaur, M.S. Makkar |
3. Exploiting the Dmc Satellite Constellation for Applications in Precision AgricultureThis paper presents the unique capabilities of the DMC constellation of optical sensors, and examples of how a number of organisations around the world are exploiting this powerful data source for applications in precision farming. The DMC consists of five satellites built in the UK by Surrey Satellite Technology Ltd, each carrying a wide swath (650km) optical sensor. It is an international programme of satellite ownership and groundstations, with joint campaigns being coordinated c... P. Stephens, S. Mackin, G. Holmes |
4. Potential of Visible and Near Infrared Spectroscopy for Prediction of Paddy Soil Physical PropertiesA fast and convenient soil analytical technique is needed for soil quality assessment and precision soil management. The main objective of this study was to evaluate the ability of Visible (Vis) and Near-infrared Reflectance Spectroscopy (NIRS) to predict paddy soil physical properties in a typical Malaysian paddy field. To assess the utility of spectroscopy for soil physical characteristics prediction, we used 118 soil samples for laboratory analysis and optical measurement in the Vis-NIR re... A. Gholizadeh, M. Saberioon, M. Mohd Soom |
5. Can Active Sensor Based NDVI Consistently Classify Wheat Genotypes?ABSTRACT ... M.A. Naser, R. Khosla, S. Haley, R. Reich, L. Longchamps, M. Moragues, G.W. Buchleiter, G.S. Mcmaster |
6. Variation in Nitrogen Use Efficiency for Multiple Wheat Genotypes across Dryland and Irrigated Cropping SystemsABSTRACT ... M.A. Naser, R. Khosla, R. Reich, S. Haley, L. Longchamps, M. Moragues, G.W. Buchleiter, G.S. Mcmaster |
7. Automatic Remote Image Processing For Agriculture Uses Through Specific SoftwareAbstract ... D. Gómez-candón, J.J. Caballero-novella, J.M. Peña-barragán, M. Jurado-expósito, F. López-granados, L. Garcia-torres, A.I. Decastro |
8. Position Error of Input Prescription Map Delineated From Remote ImagesThe spatial variability of biotic fact... D. Gómez-candón, J.J. Caballero-novella, J.M. Peña-barragán, M. Jurado-expósito, L. Garcia-torres, F. López-granados, A.I. Decastro |
9. Comparing Sensing Platforms for Crop Remote SensingRemote sensing offers the possibility to obtain a rapid and non-destructive diagnosis of crop health status. This gives the opportunity to apply variable rates of fertilizers to meet the actual crop needs at every locations of the field. However, the commonly used normalized difference vegetation index (ND... R. Khosla, L. Longchamps |
10. Estimation of Soil Moisture from RADARSAT-2 Multi-Polarized SAR Data over Wheat FieldsGuijun Yanga... G. Yang |
11. Estimation of Rice Yield from MODIS Data in West Java, IndonesiaChiharu Hongo1*, Takaaki Furukawa1, Gunardi Sigit2, Masayasu Maki3, Koki Honma3,... C. Hongo, T. Furukawa, G. Sigit, M. Maki, K. Honma, K. Yoshida, K. Oki, H. Shirakawa |
12. Ground Level Hyperspectral Imagery For Weeds Detection In Wheat FieldsWeeds are a severe pest in agriculture resulting in extensive yield loss. Applying precise weed control has economical as well as environmental benefits. Combining remote sensing tools and techniques with the concept of precision agriculture has the potential to automatically locate and identify weeds in order to allow precise control. The objective of the current work is to detect ... D.J. Bonfil, U. Shapira, A. Karnieli, I. Herrmann, S. Kinast |
13. Estimation of Leaf Nitrogen Concentration in Barley with In Situ Hyperspectral MeasurementsLeaf nitrogen concentration (LNC), a good indicator of nitrogen status in crop, is of special significance to diagnose nutrient stress and guide nitrogen fertilization in fields. Due to its non-destructive and quick advantages, hyperspectral remote sensing plays a unique r... J.M. Wang, C.M. Li, X.M. Yang, W.M. Huang, H.M. Yang, X.M. Xu |
14. Applications for Precision Agriculture: the Italian Experience of SIRIUS ProjectThis paper reports the results of the project SIRIUS (Sustainable Irrigation water management and River-ba... P. Nino, S. Vanino, F. Lupia, F. Altobelli, F. Vuolo, I. Namdarian, C. De Michele |
15. Appropriate Wavelengths for Winter Wheat Growth Status Based On Multi-Spectral Crop Reflectance DataOne of the applications of remote sensing in agriculture is to obtain crop status for estimation and management of variable rate of inputs in the crop production. In order to select the appropriate wavelengths relat... I. Han-ya, K. Ishii, N. Noguchi, V. Rasooli Sharabian |
16. Assessment of Land Use Changes in Dirab Region of Saudi Arabia Using Remotely Sensed ImageriesA thorough knowledge of land use changes is important for planning and management activities of land resources. Moreover, it is considered ... K.A. Al-gaadi |
17. Remote NIR-Sensor Fusion with Weather Data for Improved Prediction of Wheat Yield ModelsPrediction models for grain yield based on remote sensing data are commonly shown to perform reasonably well for one single cropping season. The model performances often drop, however, when data from more years is included. This may be caused by biased data, resulting from diverging growth conditions from year to year, which a... T. Isaksson, A. Korsaeth, S. Øvergaard |
18. Soil Resource Appraisal towards Land use Planning Using Satellite Remote Sensing and GIS – A Case Study in Medak Nala Watershed in Northern Karnataka, IndiaIn precision farming, knowledge of spatial variability in soil properties is important. The soil map shows soil series and phases like stoniness, gravelliness, salinity, sodicity... V.C. Patil, H.H. Gowda, K.A. Reddy, U.K. Shanwad |
19. Remote Sensing Imagery Based Agricultural Land Pattern Extraction around Miyajimanuma WetlandThis research aimed to extract agricultural land use pattern around the Miyajimanuma wetland, Hokkaido, Japan. By combining the image segmentation technology - watershed transform and image classification technology- particle swarm optimization (PSO)-k-means based minimum distance classifier, a new method for extracting the agricultural land use information ... R. Mochizuki, I. Han-ya, N. Noguchi, B. Su, K. Ishii |
20. Estimating Crop Leaf Area Index from Remotely Sensed Data: Scale Effects and Scaling MethodsLeaf area index (LAI) of crop canopies is significant for growth condition monitoring and crop yield estimation, and estimating LAI based on remote sensing observations is the normal way to assess regional crop growth. However, the scale effects of LAI make multi-scale observations harder to be fully and effectively utilized for LAI estimation. A systematical statistical str... Y. Dong , J. Wang , C. Li , G. Yang, X. Song, W. Huang |
21. Monitoring Drought Stress Index in Wheat Field of Mongolia Using Remote SensingWater stress during crop growth, even during short periods of a couple of weeks, is a major cause of yield reduction. The complexity in defining the magnitude of such water stress is due to diversity of crops grown in a given location, variability in soil type and conditions, spatial variability of rainfall, delay in timely of agriculture, and diversity in crop management practices. The problem associated with drought ... B.M. Banzragch, N.M. Damdinpurev, E.M. Batzorig |
22. Hyperspectral Imagery for the Detection of Nitrogen Stress in Potato for In-season Management... T.J. Nigon, C. Rosen, D. Mulla, Y. Cohen, V. Alchanatis, R. Rud |
23. Evaluating Water Status in Potato Fields Using Combined Information from RGB and Thermal Aerial ImagesPotato yield and quality are highly dependent on an adequate supply of water. In this study the combined information from RGB and thermal aerial images to ev... Y. Cohen, V. Alchanatis, B. Heuer, H. Lemcoff, M. Sprintsin, C. Rosen, D. Mulla, T. Nigon, Z. Dar, A. Cohen, A. Levi, R. Brikman, T. Markovits, R. Rud |
24. Understanding Spatial and Temporal Variability of Wheat Yield: An Integrated System ApproachSpatial variation in soil water and nitrogen are often the causes of crop yield spatial variability due to their influence on the uniformity of plant stand at emergence and for in-season stresses. Natural and acquired variability in production capacity or potential within a field causes uniform agronomic management practices for the field to be correct in some parts and inappropriate in others. To ... B. Basso, C. Fiorentino, D. Cammarano, A. D'errico |
25. Spectral Characterization to Discriminate Grass Weeds from Wheat Crop Using Remote Sensing and GIS for Precision Agriculture and Environmental SustainabilityKaur, Ramanjit, Mahey RK, Mahal JS, Kingra PK and Kaur Pukhraj ... R. Randhawa |
26. A Case Study Approach for Teaching and Applying Precision AgricultureStudents often struggle understanding precision agriculture principles and how these principles can be applied to farming operations. A case-study approach that requires students to own a recreational global positioning system (GPS) for collecting on-farm data could be a method for helping students understand and apply precision agriculture. This paper describes a case-study approach to teaching precision agriculture using student owned GPS units and geographical information systems (GIS) sof... J.D. Williams |
27. Map@Syst – Geospatial Solutions for Rural and Community SustainabilityMap@Syst is a part of the USDA Cooperative State Research, Education and Extension Service (CSREES) eXtension online Web information service. eXtension is an educational partnership of more than 70 universities to provide online access to objective, research-based information and educational opportunities. Map@Syst is a Wiki-based Web site assembled and maintained cooperatively by geospatial technology educational specialists and practitioners. Map@Syst is a primary source of geospatial infor... P. Rasmussen, J. Nowatzki |
28. Teaching Critical Thinking Skills Using Geospatial Technology As Instructional ToolsTechniques in data collection and analysis of data are important concepts for students of precision farming. Also needed in conjunction with these concepts are critical thinking and problem solving skills. Employers often list critical thinking skills as one of the most important characteristics for new employees. Helping students experience and acquire critical thinking skills can be difficult. Geospatial technologies are not only useful precision farming tools, they are also educational too... T.A. Brase |
29. Spotweeds: a Multiclass UASs Acquired Weed Image Dataset to Facilitate Site-specific Aerial Spraying Application Using Deep LearningUnmanned aerial systems (UASs)-based spot spraying application is considered a boon in Precision Agriculture (PA). Because of spot spraying, the amount of herbicide usage has reduced significantly resulting in less water contamination or crop plant injury. In the last demi-decade, Deep Learning (DL) has displayed tremendous potential to accomplish the task of identifying weeds for spot spraying application. Also, most of the ground-based weed management technologies have relied on DL techniqu... N. Rai, Y. Zhang, J. Quanbeck, A. Christensen, X. Sun |
30. A Generative Adversarial Network-based Method for High Fidelity Synthetic Data AugmentationDigital Agriculture has led to new phenotyping methods that use artificial intelligence and machine learning solutions on image and video data collected from lab, greenhouse, and field environments. The availability of accurately annotated image and video data remains a bottleneck for developing most machine learning and deep learning models. Typically, deep learning models require thousands of unique samples to accurately learn a given task. However, manual annotation of a large dataset will... S. Sridharan, S. Sornapudi, Q. Hu, S. Kumpatla, J. Bier |
31. Meta Deep Learning Using Minimal Training Images for Weed Classification in Wild BlueberryDeep learning convolutional neural networks (CNNs) have gained popularity in recent years for their ability to classify images with high levels of accuracy. In agriculture, they have been applied for disease identification, crop growth monitoring, animal behaviour tracking, and weed classification. Datasets traditionally consisting of thousands of images of each desired target are required to train CNNs. A recent survey of Nova Scotia wild blueberry (Vaccinium angustifolium Ait.) fie... P.J. Hennessy, T.J. Esau, A.W. Schumann, A.A. Farooque, Q.U. Zaman, S.N. White |
32. Generation of Site-specific Nitrogen Response Curves for Winter Wheat Using Deep LearningNitrogen response (N-response) curves are tools used to support farm management decisions. Conventionally, the N-response curve is modeled as an exponential function that aims to identify an important threshold for a given field: the economic optimum point. This is useful to determine the nitrogen rate beyond which there is no actual profit for the farmers. In this work, we show that N-response curves are not only field-specific but also site-specific and, as such, economic optimum points sho... G. Morales, J.W. Sheppard, A. Peerlinck, P. Hegedus, B. Maxwell |
33. Real-time Detection of Picking Region of Ridge Planted Strawberries Based on YOLOv5s with a Modified NeckRobotic strawberry harvesting requires machine vision system to have the ability to detect the presence, maturity, and location of strawberries. Strawberries, however, can easily be bruised, injured, and even damaged during robotic harvest if not picked properly because of their soft surfaces. Therefore, it is important to cut or pick the strawberry stems instead of picking the fruit directly. Additionally, real-time detection is critical for robotic strawberry harvesting to adapt to the chan... Z. He, K. Manoj, Q. Zhang, S. Kshetri |
34. Predicting Below and Above Ground Peanut Biomass and Maturity Using Multi-target RegressionPeanut growth and maturity prediction can help farmers and breeding programs improving crop management. Remote sensing images collected by satellites and drones make possible and accurate crop monitoring. Today, empirical relations between crop biomass and spectral reflectance could be used for prediction of single variables such as aboveground crop biomass, pod weight (PW), or peanut maturity. Robust algorithms such as multioutput regression (MTR) implemented through multioutput random fores... M.F. Oliveira, F.M. Carneiro, M. Thurmond, M.D. Del Val, L.P. Oliveira, B. Ortiz, A. Sanz-saez, D. Tedesco |
35. From Fragmented Data to Unified Insights: Leveraging Data Standardization Tools for Better Collaboration and Agronomic Big Data AnalysisThe quantity and scope of agronomic data available for researchers in both industry and academia is increasing rapidly. Data sources include a myriad of different streams, such as field experiments, sensors, climatic data, socioeconomic data or remote sensing. The lack of standards and workflows frequently leads agronomic data to be fragmented and siloed, hampering collaboration efforts within research labs, university departments, or research institutes. Researchers and businesses therefore ... S. Sela |
36. Coupling Machine Learning Algorithms and GIS for Crop Yield Predictions Based on Remote Sensing Imagery and Topographic IndicesIn-season yield prediction can support crop management decisions helping farmers achieve their yield goals. The use of remote sensing to predict yield it is an alternative for non-destructive yield assessment but coupling auxiliary data such as topography features could help increase the accuracy of yield estimation. Predictive algorithms that can effectively identify, process and predict yield at field scale base on remote sensing and topography still needed. Machine learning could be an alt... M.F. Oliveira, G.T. Morata, B. Ortiz, R.P. Silva, A. Jimenez |
37. A Framework for Imputation of Missing Parts in UAV Orthomosaics Using Planetscope and Sentinel-2 DataIn recent years, the emergence of Unmanned Aerial Vehicles (UAV), also known as drones, with high spatial resolution, has broadened the application of remote sensing in agriculture. However, UAV images commonly have specific problems with missing areas due to drone flight restrictions. Data mining techniques for imputing missing data is an activity often demanded in several fields of science. In this context, this research used the same approach to predict missing parts on orthomosaics obtain... F.R. Pereira, A.A. Dos Reis, R.G. Freitas, S.R. Oliveira, L.R. Amaral, G.K. Figueiredo, J.F. Antunes, R.A. Lamparelli, E. Moro, N.D. Pereira, P.S. Magalhães |
38. Identifying Key Factors Influencing Yield Spatial Pattern and Temporal Stability for Management Zone DelineationManagement zone delineation is a practical strategy for site-specific management. Numerous approaches have been used to identify these homogenous areas in the field, including approaches using multiple years of historical yield maps. However, there are still knowledge gaps in identifying variables influencing spatial and temporal variability of crop yield that should be used for management zone delineation. The objective of this study is to identify key soil and landscape properties affecting... L.N. Lacerda, Y. Miao, K. Mizuta, K. Stueve |
39. Strawberry Pest Detection Using Deep Learning and Automatic Imaging SystemStrawberry growers need to monitor pests to determine the options for pest management to reduce damage to yield and quality. However, manually counting strawberry pests using a hand lens is time-consuming and biased by the observer. Therefore, an automated rapid pest scouting method in the strawberry field can save time and improve counting consistency. This study utilized six cameras to take images of the strawberry leaf. Due to the relatively small size of the strawberry pest, six cam... C. Zhou, W. Lee, A. Pourreza, J.K. Schueller, O.E. Liburd, Y. Ampatzidis, G. Zuniga-ramirez |
40. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical DataBayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri |
41. Automated Lag Phase Detection in Wine GrapesCrop yield estimation, an important managerial tool for vineyard managers, plays a crucial role in planning pre/post-harvest operations to achieve desired yield and improve efficiency of various field operations. Although various technological approaches have been developed in the past for automated yield estimation in wine grapes, challenges such as cost and complexity of the technology, need of higher technical expertise for their operation and insufficient accuracy have caused major concer... P. Upadhyaya, M. Karkee, X. Zhang, S. Kashetri |
42. Supervised Feature Selection and Clustering for Equine Activity RecognitionIn this paper we introduce a novel supervised algorithm for equine activity recognition based on accelerometer data. By combining an approach of calculating a wide variety of time-series features with a supervised feature significance test we can obtain the best suited features using just 5 labeled samples per class and without requiring any expert domain knowledge. By using a simple cluster assignment algorithm with these obtained features, we get a classification algorithm that achieves a m... T. De Waele, D. Peralta, A. Shahid, E. De Poorter |
43. Increasing Precision Irrigation Efficacy for Row Crop Agriculture Through the Use of Artificial IntelligenceThe agricultural sector is the largest consumer of the world’s available fresh water resources. With fresh water scarcity increasing worldwide, more efficient use for irrigation water is necessary. Precision irrigation is described as the application of water to meet crop needs of a specific area, at the right amount and at the time that is optimum for crop health and management objectives. Irrigation becomes increasingly efficient through the use of precision irrigation tools. Howe... E. Bedwell |
44. Symposium Welcome and Introductions... J. Lowenberg-deboer |
45. How Does an Autonomous Tractor See the World... G. Bansal |
46. Transforming Row Crop Agriculture: Harnessing Computer Vision and AI for Automation and Autonomy... A. Sharda |
47. Swarm Farming is the Future... C. Rupp |
48. Evolving Nexus of Academia, Industry, and Government to Advance and Realize the Benefits of Robotics in Crop Production Agriculture... E.M. Barnes, M. Scott, S.A. Shearer |
49. Machine Vision, AI, and Robotics in Specialty Crop Production... M. Karkee |
50. Can AI and Automation Transform Specialty Crop Production?... Y. Ampatzidis |
51. Using AI to Estimate Vineyards and Vegetables Vigour and Yield... S. Fountas |
52. I Call Shotgun: Uncovering Human-System/Robot Gaps in Emerging Technologies... Y. Salzer |
53. Stakeholder Inclusion for Responsible Robotics: Who, How, and Why?... D. Rose |
54. Field Crop Robots - Adoption and Farm Level Economics... M. Gandorfer |
55. Development of a Multispectral Vision-based Automated Sweetpotato Grading SystemQuality evaluation and grading of sweetpotatoes is a manual operation that requires significant labor input. Machine vision technology offers a promising solution for automated sweetpotato grading and sorting. Although color imaging is widely used for quality evaluation of various horticultural commodities, a multispectral vision technique that acquires color and near-infrared (NIR) images simultaneously is a potentially more effective modality for fruit grading, especially for defects, while... J. Xu, Y. Lu |