More Web Proxy on the site http://driver.im/

research-article

Smallholder Agriculture in the Information Age: Limits and Opportunities

Authors:

Mariya Zheleva,

Petko Bogdanov,

Daphney-Stravoula Zois,

Ranveer Chandra,

Mark KimballAuthors Info & Claims

LIMITS '17: Proceedings of the 2017 Workshop on Computing Within Limits

Pages 59 - 70

https://doi.org/10.1145/3080556.3080563

Published: 22 June 2017 Publication History

Abstract

Recent projections by the United Nations show that the food production needs to double by 2050 in order to meet the nutrition demand of the world's growing population. A key enabler of this growth are smallholder family farms, that form the backbone of agricultural (AG) production worldwide. To meet this increasing demand, smallholder farms need to implement critical advances in task management and coordination, crop and livestock monitoring and efficient farming practices. Information and Communication Technology (ICT) will play a critical role in these advances by providing integrated and affordable cyber-physical systems (CPS) that can longitudinally measure, analyze and control AG operations. In this paper we make headway towards the design and integration of such AG-CPS. We begin by characterizing the information and communication technology demand of smallholder agriculture based on traffic analysis of farm Internet use. Our findings inform the design and integration of an end-to-end AG-CPS called FarmNET that provides (i) robust control mechanisms for multi-sensor AG data collection and fusion, (ii) wide-area, heterogeneous wireless networks for ubiquitous farm connectivity, (iii) algorithms and models for farm data analytics that produce actionable information from the collected agricultural data, and (iv) control mechanisms for autonomous, proactive farming.

References

[1]

360 Yield Center. http://www.360yieldcenter.com/Nitrogen.

[2]

AgEagle. http://ageagle.com/.

[3]

AgGateway. http://www.aggateway.org/.

[4]

Aglytix. http://www.aglytix.com/.

[5]

Agribotix. http://www.agribotix.com/.

[6]

Apache spark http://spark.apache.org/.

[7]

AquaSpy. http://www.aquaspy.com/.

[8]

aWhere. http://agfundernews.com/awhere-closes-7m-campaign-agfunder.html/.

[9]

Climate Corporation (A Monsanto company). https://climate.com/company/.

[10]

CropMetrics. http://www.virtualoptimizer.com/.

[11]

FarmLogs. http://agfundernews.com/farmlogs-lands-10m-series-b-help-manage-farms.html/.

[12]

Farmobile. http://www.farmobile.com/.

[13]

Granular. http://www.granular.ag/.

[14]

gThrive. http://www.gthrive.com/.

[15]

iCropTrak. http://www.icroptrak.com/.

[16]

John Deere. http://www.deere.com/.

[17]

MyAgCentral. https://www.myagcentral.com/.

[18]

OnFarm. http://www.onfarm.com/tag/saas/.

[19]

PowWow Energy. https://www.powwowenergy.com/.

[20]

SoilSCAPE: Soil Moisture Sensing Controller And oPtimal Estimator. http://soilscape.usc.edu/bootstrap/index.html.

[21]

SST Software. http://www.sstsoftware.com/.

[22]

A. A. Abbasi and M. Younis. A survey on clustering algorithms for wireless sensor networks. Computer communications, 30(14):2826--2841, 2007.

Digital Library

[23]

I. F. Akyildiz, T. Melodia, and K. R. Chowdhury. A survey on wireless multimedia sensor networks. Computer networks, 51(4):921--960, 2007.

Digital Library

[24]

I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: A survey. Comput. Netw., 38(4):393--422, Mar. 2002.

Digital Library

[25]

J. N. Al-Karaki and A. E. Kamal. Routing techniques in wireless sensor networks: a survey. IEEE wireless communications, 11(6):6--28, 2004.

Digital Library

[26]

T. A. Alhmiedat and S.-H. Yang. A survey: localization and tracking mobile targets through wireless sensors network. 2007.

[27]

A. O. Allen. Probability, statistics, and queueing theory. Academic Press, 2014.

[28]

C. M. Alves-Serodio, J. Monteiro, and C. Couto. An integrated network for agricultural management applications. In Industrial Electronics, 1998. Proceedings. ISIE'98. IEEE International Symposium on, volume 2, pages 679--683. IEEE, 1998.

[29]

G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella. Energy conservation in wireless sensor networks: A survey. Ad hoc networks, 7(3):537--568, 2009.

Digital Library

[30]

D. Anurag, S. Roy, and S. Bandyopadhyay. Agro-sense: Precision agriculture using sensor-based wireless mesh networks. In Innovations in NGN: Future Network and Services, 2008. K-INGN 2008. First ITU-T Kaleidoscope Academic Conference, pages 383--388. IEEE, 2008.

[31]

T. Arampatzis, J. Lygeros, and S. Manesis. A survey of applications of wireless sensors and wireless sensor networks. In Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation, pages 719--724. IEEE, 2005.

[32]

G. K. Atia, V. V. Veeravalli, and J. A. Fuemmeler. Sensor Scheduling for Energy-Efficient Target Tracking in Sensor Networks. IEEE Trans. Signal Process., 59(10):4923--4937, Oct. 2011.

Digital Library

[33]

A. Baggio. Wireless sensor networks in precision agriculture. In ACM Workshop on Real-World Wireless Sensor Networks (REALWSN 2005), Stockholm, Sweden. Citeseer, 2005.

[34]

G. Bareth, A. Bolten, J. Hollberg, H. Aasen, A. Burkart, and J. Schellberg. Feasibility study of using non-calibrated uav-based rgb imagery for grassland monitoring: case study at the rengen long-term grassland experiment (rge), germany. DGPF Tagungsband, 24(2015):1--7, 2015.

[35]

E. Ben-Dor and A. Banin. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal, 59(2):364--372, 1995.

[36]

L. Bencini, F. Chiti, G. Collodi, D. Di Palma, R. Fantacci, A. Manes, and G. Manes. Agricultural monitoring based on wireless sensor network technology: Real long life deployments for physiology and pathogens control. In Sensor Technologies and Applications, 2009. SENSORCOMM'09. third International Conference on, pages 372--377. IEEE, 2009.

Digital Library

[37]

P. Bhagwat, B. Raman, and D. Sanghi. Turning 802.11 inside-out. SIGCOMM Comput. Commun. Rev., 34(1):33--38, Jan. 2004.

Digital Library

[38]

S. Bhaumik, G. Narlikar, S. Chattopadhyay, and S. Kanugovi. Breathe to stay cool: Adjusting cell sizes to reduce energy consumption. In Proceedings of the First ACM SIGCOMM Workshop on Green Networking, Green Networking '10, pages 41--46, New York, NY, USA, 2010. ACM.

Digital Library

[39]

G. Bishop-Hurley, D. Swain, D. Anderson, and P. Corke. Animal Control-What constitutes a reliable cue to stop animal movement? 2006.

[40]

G. Bishop-Hurley, D. L. Swain, D. Anderson, P. Sikka, C. Crossman, and P. Corke. Virtual fencing applications: Implementing and testing an automated cattle control system. Computers and Electronics in Agriculture, 56(1):14--22, 2007.

Digital Library

[41]

P. Bogdanov, B. Baumer, P. Basu, A. Bar-Noy, and A. K. Singh. As strong as the weakest link: Mining diverse cliques in weighted graphs. In Proceedings of Machine Learning and Knowledge Discovery in Databases - European Conference (ECML/PKDD). Springer, 2013.

[42]

P. Bogdanov, M. Busch, J. Moehlis, A. K. Singh, and B. K. Szymanski. The social media genome: Modeling individual topic-specific behavior in social media. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ACM, 2013.

Digital Library

[43]

P. Bogdanov, M. Busch, J. Moehlis, A. K. Singh, and B. K. Szymanski. The social media genome: Modeling individual topic-specific behavior in social media. In Journal of Social Network Analysis and Mining (SNAM). Springer, 2014.

Digital Library

[44]

P. Bogdanov, M. Mongiovi, and A. K. Singh. Mining heavy subgraphs in time-evolving networks. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2011.

Digital Library

[45]

Z. Butler, P. Corke, R. Peterson, and D. Rus. Virtual fences for controlling cows. In Robotics and Automation, 2004. Proceedings. ICRA'04. 2004 IEEE International Conference on, volume 5, pages 4429--4436. IEEE, 2004.

[46]

Z. Butler, P. Corke, R. Peterson, and D. Rus. From robots to animals: virtual fences for controlling cattle. the International Journal of Robotics Research, 25(5--6):485--508, 2006.

Digital Library

[47]

N. Cao, S. Choi, E. Masazade, and P. K. Varshney. Sensor selection for target tracking in wireless sensor networks with uncertainty. IEEE Transactions on Signal Processing, 64(20):5191--5204, 2016.

Digital Library

[48]

A. Chedad, D. Moshou, J.-M. Aerts, A. Van Hirtum, H. Ramon, and D. Berckmans. A--panimal production technology: recognition system for pig cough based on probabilistic neural networks. Journal of agricultural engineering research, 79(4):449--457, 2001.

[49]

Y. Chen, Q. Zhao, V. Krishnamurthy, and D. Djonin. Transmission scheduling for optimizing sensor network lifetime: A stochastic shortest path approach. IEEE Transactions on Signal Processing, 55(5):2294--2309, 2007.

Digital Library

[50]

E. K. Chong, C. M. Kreucher, and A. O. Hero. Partially observable Markov decision process approximations for adaptive sensing. Discrete Event Dynamic Systems, 19(3):377--422, 2009.

Digital Library

[51]

Y. Chung, S. Oh, J. Lee, D. Park, H.-H. Chang, and S. Kim. Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors, 13(10):12929--12942, 2013.

[52]

U. S. Committee. New Cooperation for Global Food Security. http://www.un.org/press/en/2009/gaef3242.doc.htm.

[53]

S. Cugati, W. Miller, and J. Schueller. Automation concepts for the variable rate fertilizer applicator for tree farming. In the Proceedings of the 4th European Conference in Precision Agriculture, Berlin, Germany, June, pages 14--19, 2003.

[54]

X.-H. Dang, A. K. Singh, P. Bogdanov, H. You, and B. Hsu. Discriminative subnetworks with regularized spectral learning for global-state network data. In Proceedings of the 25th European Conference on Machine Learning / 18th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD). Springer, 2014.

Digital Library

[55]

X.-H. Dang, H. You, P. Bogdanov, and A. Singh. Learning predictive sub-structures with regularization for network data. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2015.

Digital Library

[56]

I. Demirkol, C. Ersoy, and F. Alagoz. Mac protocols for wireless sensor networks: a survey. IEEE Communications Magazine, 44(4):115--121, 2006.

Digital Library

[57]

I. Demirkol, C. Ersoy, and E. Onur. Wake-up receivers for wireless sensor networks: benefits and challenges. IEEE Wireless Communications, 16(4):88--96, Aug 2009.

Digital Library

[58]

S. E. Díaz, J. C. Pérez, A. C. Mateos, M.-C. Marinescu, and B. B. Guerra. A novel methodology for the monitoring of the agricultural production process based on wireless sensor networks. Computers and Electronics in Agriculture, 76(2):252--265, 2011.

Digital Library

[59]

J. A. V. Diosdado, Z. E. Barker, H. R. Hodges, J. R. Amory, D. P. Croft, N. J. Bell, and E. A. Codling. Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system. Animal Biotelemetry, 3(1):15, 2015.

[60]

A. Dobermann and R. Nelson. Opportunities and solutions for sustainable food production. 2013.

[61]

C. Eastwood, S. Kenny, et al. Art or science?: Heuristic versus data driven grazing management on dairy farms. Extension Farming Systems Journal, 5(1):95, 2009.

[62]

D. Estrin, L. Girod, G. Pottie, and M. Srivastava. Instrumenting the world with wireless sensor networks. In Acoustics, Speech, and Signal Processing, 2001. Proceedings.(ICASSP'01). 2001 IEEE International Conference on, volume 4, pages 2033--2036. IEEE, 2001.

[63]

A. B. Flores, R. E. Guerra, E. W. Knightly, P. Ecclesine, and S. Pandey. Ieee 802.11af: a standard for tv white space spectrum sharing. IEEE Communications Magazine, 51(10):92--100, October 2013.

[64]

C. Francone, V. Pagani, M. Foi, G. Cappelli, and R. Confalonieri. Comparison of leaf area index estimates by ceptometer and pocketlai smart app in canopies with different structures. Field Crops Research, 155:38--41, 2014.

[65]

J. A. Fuemmeler, G. K. Atia, and V. V. Veeravalli. Sleep control for tracking in sensor networks. IEEE Transactions on Signal Processing, 59(9):4354--4366, 2011.

Digital Library

[66]

H. C. J. Godfray, J. R. Beddington, I. R. Crute, L. Haddad, D. Lawrence, J. F. Muir, J. Pretty, S. Robinson, S. M. Thomas, and C. Toulmin. Food security: the challenge of feeding 9 billion people. science, 327(5967):812--818, 2010.

[67]

J. C. Groot, G. J. Oomen, and W. A. Rossing. Multi-objective optimization and design of farming systems. Agricultural Systems, 110:63--77, 2012.

[68]

L. Gu and J. A. Stankovic. Radio-triggered wake-up capability for sensor net- works. In Proceedings. RTAS 2004. 10th IEEE Real-Time and Embedded Technology and Applications Symposium, 2004., pages 27--36, May 2004.

Digital Library

[69]

S. Guo, M. H. Falaki, E. A. Oliver, S. U. Rahman, A. Seth, M. A. Zaharia, U. Ismail, and S. Keshav. Design and implementation of the kiosknet system. In 2007 International Conference on Information and Communication Technologies and Development, pages 1--10, Dec 2007.

[70]

D. Hanson and C. Mo. Monitoring cattle motion using 3-axis acceleration and gps data. Journal of Research in Agriculture and Animal Science, 2(10):1--8, 2014.

[71]

K. A. Harris and V. V. Veeravalli. Implementing energy-efficient tracking in a sensor network. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 4608--4612. IEEE, 2013.

[72]

S. Hasan, K. Heimerl, K. Harrison, K. Ali, S. Roberts, A. Sahai, and E. Brewer. Gsm whitespaces: An opportunity for rural cellular service. In Dynamic Spectrum Access Networks (DYSPAN), 2014 IEEE International Symposium on, pages 271-- 282, April 2014.

[73]

Y. He and E. K. Chong. Sensor scheduling for target tracking: A Monte Carlo sampling approach. Digital Signal Processing, 16(5):533--545, 2006.

[74]

K. Heimerl, K. Ali, J. Blumenstock, B. Gawalt, and E. Brewer. Expanding rural cellular networks with virtual coverage. In Proceedings of the 10th USENIX Conference on Networked Systems Design and Implementation, nsdi'13, pages 283--296, Berkeley, CA, USA, 2013. USENIX Association.

Digital Library

[75]

K. Heimerl et al. Local, sustainable, small-scale cellular networks. ICTD13, Cape Town, South Africa, 2013.

Digital Library

[76]

K. Heimerl, S. Hasan, K. Ali, T. Parikh, and E. Brewer. An experiment in reducing cellular base station power draw with virtual coverage. In Proceedings of the 4th Annual Symposium on Computing for Development, ACM DEV-4 '13, pages 6:1--6:9, New York, NY, USA, 2013. ACM.

Digital Library

[77]

J. I. Huircán, C. Muñoz, H. Young, L. Von Dossow, J. Bustos, G. Vivallo, and M. Toneatti. Zigbee-based wireless sensor network localization for cattle monitoring in grazing fields. Computers and Electronics in Agriculture, 74(2):258--264, 2010.

Digital Library

[78]

I. F. P. R. Institute. Smallholder farming. http://www.ifpri.org/topic/smallholder-farming.

[79]

M. S. N. KABIR, S.-O. CHUNG, K. Yong-Joo, L. Geung-Joo, Y. Seung-Hwa, L. Kyeong-Hwan, T. OKAYASU, and E. INOUE. Sensor comparison for grass growth estimation. J. Fac. Agr., Kyushu Univ, 61(2):367--374, 2016.

[80]

R. L. Kallenbach. Describing the dynamic: Measuring and assessing the value of plants in the pasture. Crop Science, 55(6):2531--2539, 2015.

[81]

Y. Kim, R. G. Evans, and W. M. Iversen. Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE transactions on instrumentation and measurement, 57(7):1379--1387, 2008.

[82]

C. Krintz, R. Wolski, N. Golubovic, B. Lampel, V. Kulkarni, B. Roberts, and B. Liu. Smartfarm: Improving agriculture sustainability using modern information technology. ACM SIGKDD DSFEW, 2016.

[83]

V. Krishnamurthy and D. V. Djonin. Optimal threshold policies for multivariate POMDPs in radar resource management. IEEE transactions on Signal Processing, 57(10):3954--3969, 2009.

Digital Library

[84]

N. D. Larusso and A. Singh. Efficient tracking and querying for coordinated uncertain mobile objects. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on, pages 182--193. IEEE, 2013.

Digital Library

[85]

D. Lekomtcev and R. Marsalek. Comparison of 802.11af and 802.22 standards -- physical layer and cognitive functionality. In Electro Revue, VOL. 3, NO. 2, June 2012.

[86]

Y. Li, L. W. Krakow, E. K. Chong, and K. N. Groom. Approximate stochastic dynamic programming for sensor scheduling to track multiple targets. Digital Signal Processing, 19(6):978--989, 2009.

Digital Library

[87]

S. K. Lowder, J. Skoet, and T. Raney. the number, size, and distribution of farms, smallholder farms, and family farms worldwide. World Development, 87:16--29, November, 2016.

[88]

J. M. MacDonald. Family Farming in the United States. https://www.ers.usda.gov/amber-waves/2014/march/family-farming-in-the-united-states/.

[89]

P. Martiskainen, M. Järvinen, J.-P. Skön, J. Tiirikainen, M. Kolehmainen, and J. Mononen. Cow behaviour pattern recognition using a three dimensional accelerometer and support vector machines. Applied animal behaviour science, 119(1):32--38, 2009.

[90]

A. Mason and J. Sneddon. Automated monitoring of foraging behaviour in free ranging sheep grazing a biodiverse pasture. In Sensing Technology (ICST), 2013 Seventh International Conference on, pages 46--51. IEEE, 2013.

[91]

K. W. Matthee, G. Mweemba, A. V. Pais, G. van Stam, and M. Rijken. Bringing internet connectivity to rural zambia using a collaborative approach. In 2007 International Conference on Information and Communication Technologies and Development, pages 1--12, Dec 2007.

[92]

P. McEntee, R. Belford, R. Mandel, J. Harper, and M. Trotter. Sub paddock scale spatial variability between the pasture and cropping phases of mixed farming systems in australia. In Precision agriculture'13, pages 389--394. Springer, 2013.

[93]

N. Mishra, K. Chebrolu, B. Raman, and A. Pathak. Wake-on-wlan. In Proceedings of the 15th International Conference on World Wide Web, WWW '06, pages 761--769, New York, NY, USA, 2006. ACM.

Digital Library

[94]

M. Moghaddam. A Robust Wireless Sensor Network Architecture for the Large-scale Deployment of the Soil Moisture Sensing Controller and Optimal Estimator (SoilSCaPE). In American Geophysical Union Fall Conference, 2011.

[95]

M. Moghaddam, D. Entekhabi, Y. Goykhman, K. Li, M. Liu, A. Mahajan, A. Nayyar, D. Shuman, and D. Teneketzis. A wireless soil moisture smart sensor web using physics-based optimal control: Concept and initial demonstrations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(4):522--535, 2010.

[96]

M. Moghaddam, M. Liu, X. Wu, M. Burgin, Y. Goykhman, Q. Wang, D. Shuman, A. Nayyar, D. Teneketzis, and D. Entekhabi. Soil Moisture Sensing Controller and Optimal Estimator (SoilSCaPE): An in-situ Wireless Sensor Network for Validation of Spaceborne Soil Moisture Estimates. In American Geophysical Union Fall Conference, 2010.

[97]

M. Moghaddam, A. Silva, R. Akbar, D. Clewley, M. Burgin, A. Castillo, and D. Entekhabi. SoilSCAPE In situ Network for Multiscape Validation of SMAP Data Products. In 2013 IEEE International Geoscience and Remote Sensing Symposium, 2013.

[98]

M. Mongiovi, P. Bogdanov, R. Ranca, A. K. Singh, E. E. Papalexakis, and C. Faloutsos. Netspot: Spotting significant anomalous regions on dynamic networks. In Proceedings of SIAM International Conference on Data Mining (SDM), 2013.

[99]

M. Mongiovi, P. Bogdanov, and A. K. Singh. Mining evolving network processes. In Proceedings of the IEEE International Conference on Data Mining (ICDM). IEEE, 2013.

[100]

I. D. Moore, P. Gessler, G. Nielsen, and G. Peterson. Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 57(2):443--452, 1993.

[101]

A. Mucherino, P. J. Papajorgji, and P. M. Pardalos. Data mining in agriculture, volume 34. Springer Science & Business Media, 2009.

Digital Library

[102]

E. S. Nadimi and H. Søgaard. Observer kalman filter identification and multiple-model adaptive estimation technique for classifying animal behaviour using wireless sensor networks. Computers and Electronics in Agriculture, 68(1):9--17, 2009.

Digital Library

[103]

E. S. Nadimi, H. Søgaard, T. Bak, and F. W. Oudshoorn. ZigBee-based wireless sensor networks for monitoring animal presence and pasture time in a strip of new grass. Computers and electronics in agriculture, 61(2):79--87, 2008.

Digital Library

[104]

E. S. Nadimi, H. T. Søgaard, and T. Bak. Zigbee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees. Biosystems engineering, 100(2):167--176, 2008.

[105]

S. Nedevschi, J. Chandrashekar, J. Liu, B. Nordman, S. Ratnasamy, and N. Taft. Skilled in the art of being idle: Reducing energy waste in networked systems. In Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation, NSDI'09, pages 381--394, Berkeley, CA, USA, 2009. USENIX Association.

Digital Library

[106]

S. Nitinawarat, G. K. Atia, and V. V. Veeravalli. Efficient target tracking using mobile sensors. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011 4th IEEE International Workshop on, pages 405--408. IEEE, 2011.

[107]

M. Z. others. Kwiizya: local cellular network services in remote areas. ACM MobiSys13, Taipei, Taiwan, 2013.

[108]

R. Patra, S. Nedevschi, S. Surana, A. Sheth, L. Subramanian, and E. Brewer. Wildnet: Design and implementation of high performancewifi based long distance networks. In Proceedings of the 4th USENIX Conference on Networked Systems Design and Implementation, NSDI'07, pages 7--7, Berkeley, CA, USA, 2007. USENIX Association.

Digital Library

[109]

C. Peng, S.-B. Lee, S. Lu, H. Luo, and H. Li. Traffic-driven power saving in operational 3g cellular networks. In Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, MobiCom '11, pages 121--132, New York, NY, USA, 2011. ACM.

Digital Library

[110]

A. Pentland, R. Fletcher, and A. Hasson. Daknet: rethinking connectivity in developing nations. Computer, 37(1):78--83, Jan 2004.

Digital Library

[111]

F. Pierce and T. Elliott. Regional and on-farm wireless sensor networks for agricultural systems in eastern washington. Computers and electronics in agriculture, 61(1):32--43, 2008.

Digital Library

[112]

F. Qian, Z. Wang, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. Characterizing radio resource allocation for 3g networks. In Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, IMC '10, pages 137--150, New York, NY, USA, 2010. ACM.

Digital Library

[113]

F. Qian, Z. Wang, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. Top: Tail optimization protocol for cellular radio resource allocation. In the 18th IEEE International Conference on Network Protocols, pages 285--294, Oct 2010.

Digital Library

[114]

P. J. Radtke, H. T. Boland, and G. Scaglia. An evaluation of overhead laser scanning to estimate herbage removals in pasture quadrats. Agricultural and forest meteorology, 150(12):1523--1528, 2010.

[115]

M. Rahimi, M. Hansen, W. J. Kaiser, G. S. Sukhatme, and D. Estrin. Adaptive sampling for environmental field estimation using robotic sensors. In Intelligent Robots and Systems, 2005.(IROS 2005). 2005 IEEE/RSJ International Conference on, pages 3692--3698. IEEE, 2005.

[116]

B. Raman and K. Chebrolu. Experiences in using wifi for rural internet in india. IEEE Communications Magazine, 45(1):104--110, Jan 2007.

Digital Library

[117]

Rhizomatica. Rhizomatica -- Mobile Communications for All. rhizomatica.org/. {Online; accessed 4-November-2016}.

[118]

S. Rosen, H. Luo, Q. A. Chen, Z. M. Mao, J. Hui, A. Drake, and K. Lau. Discovering fine-grained rrc state dynamics and performance impacts in cellular networks. In Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, MobiCom '14, pages 177--188, New York, NY, USA, 2014. ACM.

Digital Library

[119]

G. Ruß and A. Brenning. Data mining in precision agriculture: management of spatial information. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 350--359. Springer, 2010.

Digital Library

[120]

J. Schellberg and E. Verbruggen. Frontiers and perspectives on research strategies in grassland technology. Crop and Pasture Science, 65(6):508--523, 2014.

[121]

P. Schmitt, D. Iland, M. Zheleva, and E. Belding. HybridCell: Cellular connectivity on the fringes with demand-driven local cells. In IEEE INFOCOM '16, San Francisco, CA, USA, April 2016.

Digital Library

[122]

E. W. Schuster, S. Kumar, S. E. Sarma, J. L. Willers, and G. A. Milliken. In- frastructure for data-driven agriculture: identifying management zones for cotton using statistical modeling and machine learning techniques. In Emerging Technologies for a Smarter World (CEWIT), 2011 8th International Conference & Expo on, pages 1--6. IEEE, 2011.

[123]

D. K. Shannon, J. Lory, R. Kallenbach, T. Lorenz, J. Harper, G. Schmitz, W. Rapp, B. Carpenter, and D. England. Initial results utilizing a commercially available ultrasonic sensor for forage yield measurements. In Proc. Am. Soc. Agric. Biol. Eng. Am. Soc. Agric. Biol. Eng, St. Joseph, MI, 2013.

[124]

X. Shen, S. Liu, and P. K. Varshney. Sensor selection for nonlinear systems in large sensor networks. IEEE Transactions on Aerospace and Electronic Systems, 50(4):2664--2678, 2014.

[125]

X. Shen and P. K. Varshney. Sensor selection based on generalized information gain for target tracking in large sensor networks. IEEE Transactions on Signal Processing, 62(2):363--375, 2014.

Digital Library

[126]

E. Shih, P. Bahl, and M. J. Sinclair. Wake on wireless: An event driven energy saving strategy for battery operated devices. In Proceedings of the 8th Annual International Conference on Mobile Computing and Networking, MobiCom'02, pages 160--171, New York, NY, USA, 2002. ACM.

Digital Library

[127]

D. I. Shuman. From sleeping to stockpiling: Energy conservation via stochastic scheduling in wireless networks. PhD thesis, The University of Michigan, 2010.

[128]

D. I. Shuman, M. Liu, and O. Q. Wu. Energy-efficient transmission scheduling with strict underflow constraints. IEEE Transactions on Information theory, 57(3):1344--1367, 2011.

Digital Library

[129]

D. I. Shuman, A. Nayyar, A. Mahajan, Y. Goykhman, K. Li, M. Liu, D. Teneketzis, M. Moghaddam, and D. Entekhabi. Measurement scheduling for soil moisture sensing: From physical models to optimal control. Proceedings of the IEEE, 98(11):1918--1933, 2010.

[130]

A. Silva, P. Bogdanov, and A. Singh. Hierarchical in-network attribute com- pression via importance sampling. In Proceedings of the 31st IEEE International Conference on Data Engineering (ICDE), 2015.

[131]

J.-F. Soussana, A.-I. Graux, and F. N. Tubiello. Improving the use of modelling for projections of climate change impacts on crops and pastures. Journal of experimental botany, 61(8):2217--2228, 2010.

[132]

A. Spink, B. Cresswell, A. Kölzsch, F. van Langevelde, M. Neefjes, L. Noldus, H. van Oeveren, H. Prins, T. van der Wal, N. de Weerd, et al. Animal behaviour analysis with gps and 3d accelerometers. In Precision livestock farming, 10--12 September, 2013, Leuven, Belgium, pages 229--239, 2013.

[133]

C. R. Stevenson, G. Chouinard, Z. Lei, W. Hu, S. J. Shellhammer, and W. Caldwell. Ieee 802.22: the first cognitive radio wireless regional area network standard. IEEE Communications Magazine, 47(1):130--138, January 2009.

Digital Library

[134]

L. Subramanian, S. Nedevschi, M. Ho, E. Brewer, and A. Sheth. Rethinking wireless for the developing world. In In Hotnets-V, 2006.

[135]

S. Surana, R. Patra, S. Nedevschi, M. Ramos, L. Subramanian, Y. Ben David, and E. Brewer. Beyond pilots: Keeping rural wireless networks alive. In Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, NSDI'08, pages 119--132, Berkeley, CA, USA, 2008. USENIX Association.

Digital Library

[136]

J. J. Tewa, A. Bah, and S. C. Oukouomi Noutchie. Dynamical models of interactions between herds forage and water resources in sahelian region. In Abstract and Applied Analysis, volume 2014. Hindawi Publishing Corporation, 2014.

[137]

J. Torres-Sánchez, J. Peña, A. De Castro, and F. López-Granados. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from uav. Computers and Electronics in Agriculture, 103:104--113, 2014.

[138]

D. Undersander, B. Albert, D. Cosgrove, D. Johnson, and P. Peterson. Pastures for profit: A guide to rotational grazing. 1997.

[139]

E. R. Vivoni and R. Camilli. Real-time streaming of environmental field data. Computers & Geosciences, 29(4):457--468, 2003.

Digital Library

[140]

N. Wang, N. Zhang, and M. Wang. Wireless sensors in agriculture and food industry-recent development and future perspective. Computers and electronics in agriculture, 50(1):1--14, 2006.

Digital Library

[141]

T. Wark, P. Corke, P. Sikka, L. Klingbeil, Y. Guo, C. Crossman, P. Valencia, D. Swain, and G. Bishop-Hurley. Transforming agriculture through pervasive wireless sensor networks. IEEE Pervasive Computing, 6(2), 2007.

Digital Library

[142]

J. Yick, B. Mukherjee, and D. Ghosal. Wireless sensor network survey. Computer networks, 52(12):2292--2330, 2008.

Digital Library

[143]

Q. Zhang, X.-l. Yang, Y.-m. Zhou, L.-r. Wang, and X.-s. Guo. A wireless solution for greenhouse monitoring and control system based on ZigBee technology. Journal of Zhejiang University-Science A, 8(10):1584--1587, 2007.

[144]

H. Zhou, L. Yin, and C. Liu. Dairy cattle movement detecting technology using support vector machine. In International Conference on Wireless Communications and Applications, pages 23--32. Springer, 2011.

[145]

D. Zois and U. Mitra. Active State Tracking with Sensing Costs: Analysis of Two--States and Methods for n-States. Transactions on Signal Processing, 2017.

Digital Library

[146]

D. S. Zois, U. Demiryurek, and U. Mitra. A POMDP approach for active collision detection via networked sensors. In Asilomar Conference on Signals, Systems, and Computers, Nov. 2016.

[147]

D.-S. Zois, M. Levorato, and U. Mitra. A POMDP Framework for Heterogeneous Sensor Selection in Wireless Body Area Networks. In Proc. 31st IEEE International Conference on Computer Communications (INFOCOM), pages 2611- 2615, March 2012.

[148]

D.-S. Zois, M. Levorato, and U. Mitra. Heterogeneous Time-Resource Allocation in Wireless Body Area Networks for Green, Maximum Likelihood Activity Detection. In Proc. IEEE International Conference on Communications (ICC), pages 3448--3452, June 2012.

[149]

D.-S. Zois, M. Levorato, and U. Mitra. Energy--Efficient, Heterogeneous Sensor Selection for Physical Activity Detection in Wireless Body Area Networks. IEEE Tansactions on Signal Processing, 61(7):1581--1594, April 2013.

Digital Library

[150]

D.-S. Zois, M. Levorato, and U. Mitra. Kalman-like state tracking and control in POMDPs with applications to body sensing networks. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.

[151]

D.-S. Zois, M. Levorato, and U. Mitra. Non-linear smoothers for discrete-time, Finite-state Markov chains. In IEEE International International Symposium on Information theory (ISIT), July 2013.

[152]

D. S. Zois, M. Levorato, and U. Mitra. Active Classification for POMDPs: A Kalman-Like State Estimator. IEEE Transactions on Signal Processing, 62(23):6209--6224, Dec. 2014.

[153]

D.-S. Zois and U. Mitra. On the properties of nonlinear POMDPs for active state tracking. In IEEE Global Conference on Signal and Information Processing (GlobalSIP), pages 193--196, December 2013.

[154]

D.-S. Zois and U. Mitra. A Weiss--Weinstein Lower Bound Based Sensing Strategy for Active State Tracking. In IEEE International International Symposium on Information theory (ISIT), July 2014.

[155]

D.-S. Zois and U. Mitra. Controlled Sensing: A Myopic Fisher Information Sensor Selection Algorithm. In IEEE Globecom, Dec. 2014.

[156]

D. S. Zois and M. Raginsky. Active object detection on graphs via locally informative trees. In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), pages 1--6, 2016.

Cited By

Doggett OBronson KSoden R(2023)HCI Research on Agriculture: Competing Sociotechnical Imaginaries, Definitions, and OpportunitiesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581081(1-24)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581081
Antony ALeith KJolley CLu JSweeney D(2020)A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder AgricultureSustainability10.3390/su1209375012:9(3750)Online publication date: 6-May-2020
https://doi.org/10.3390/su12093750
Reilly CClark M(2020)Moving Recursion Out of the RDBMS for Transactional Graph Workloads2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON51285.2020.9298122(0371-0376)Online publication date: 28-Oct-2020
https://doi.org/10.1109/UEMCON51285.2020.9298122
Show More Cited By

Index Terms

Smallholder Agriculture in the Information Age: Limits and Opportunities

Recommendations

Designing ICT for Agriculture (ICT4A) Innovations for Smallholder Farmers: The Case of Uganda
Interacción '18: Proceedings of the XIX International Conference on Human Computer Interaction

As an increasing number of technology-based systems are developed to aid farmers on crop selection, pest mitigation, and market conditions, there is often little consideration for the actual context of use that farmers in developing countries must face. ...
The use of multi-temporal MODIS images with ground data to distinguish cotton from maize and sorghum fields in smallholder agricultural landscapes of Southern Africa

In this study, we test whether we can significantly <italic>p</italic> < 0.05 distinguish cotton <italic>Gossypium hirsutum</italic> L. fields from maize <italic>Zea mays</italic> L. and sorghum <italic>Sorghum bicolor</italic> fields in smallholder ...
Digital Technologies for Smallholder Agriculture: Tensions and Speculations
DIS '24 Companion: Companion Publication of the 2024 ACM Designing Interactive Systems Conference

Big Tech, Big Agri, and agri-tech start-ups promise us that digital technologies will make agriculture more sustainable, painting high-tech visions of sensors, AI, and autonomous drones and robots running the data-driven farm. However, many such visions ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

LIMITS '17: Proceedings of the 2017 Workshop on Computing Within Limits

June 2017

148 pages

ISBN:9781450349505

DOI:10.1145/3080556

General Chairs:
Bonnie Nardi
University of California, Irvine, USA
,
Bill Tomlinson
University of California, Irvine, USA

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCAS: ACM Special Interest Group on Computers and Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

LIMITS '17

Sponsor:

SIGCAS

LIMITS '17: Workshop on Computing Within Limits

June 22 - 24, 2017

California, Santa Barbara, USA

Acceptance Rates

Overall Acceptance Rate 11 of 17 submissions, 65%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
271
Total Downloads

Downloads (Last 12 months)30
Downloads (Last 6 weeks)2

Reflects downloads up to 05 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Doggett OBronson KSoden R(2023)HCI Research on Agriculture: Competing Sociotechnical Imaginaries, Definitions, and OpportunitiesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581081(1-24)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581081
Antony ALeith KJolley CLu JSweeney D(2020)A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder AgricultureSustainability10.3390/su1209375012:9(3750)Online publication date: 6-May-2020
https://doi.org/10.3390/su12093750
Reilly CClark M(2020)Moving Recursion Out of the RDBMS for Transactional Graph Workloads2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON51285.2020.9298122(0371-0376)Online publication date: 28-Oct-2020
https://doi.org/10.1109/UEMCON51285.2020.9298122
Giua CMateria VCamanzi L(2020)Management information system adoption at the farm level: evidence from the literatureBritish Food Journal10.1108/BFJ-05-2020-0420123:3(884-909)Online publication date: 2-Nov-2020
https://doi.org/10.1108/BFJ-05-2020-0420
Tummers JKassahun ATekinerdogan B(2020)Reference architecture design for farm management information systems: a multi-case study approachPrecision Agriculture10.1007/s11119-020-09728-022:1(22-50)Online publication date: 1-Jun-2020
https://doi.org/10.1007/s11119-020-09728-0
Norton JPenzenstadler BMcDonald SKang EKoirala NKonishi RCarmona GShah JTroncoso STomlinson BEasterbrook SChen JBates O(2019)The SAGE Community CoordinatorProceedings of the Fifth Workshop on Computing within Limits10.1145/3338103.3338108(1-10)Online publication date: 10-Jun-2019
https://dl.acm.org/doi/10.1145/3338103.3338108
Tummers JKassahun ATekinerdogan B(2019)Obstacles and features of Farm Management Information Systems: A systematic literature reviewComputers and Electronics in Agriculture10.1016/j.compag.2018.12.044157(189-204)Online publication date: Feb-2019
https://doi.org/10.1016/j.compag.2018.12.044
Taneja MJalodia NByabazaire JDavy AOlariu C(2019)SmartHerd management: A microservices‐based fog computing–assisted IoT platform towards data‐driven smart dairy farmingSoftware: Practice and Experience10.1002/spe.270449:7(1055-1078)Online publication date: 16-May-2019
https://doi.org/10.1002/spe.2704
Krintz CWolski RGolubovic NBakir FJanowicz KKuhn WCena FHaller AVamvoudakis K(2018)Estimating outdoor temperature from CPU temperature for IoT applications in agricultureProceedings of the 8th International Conference on the Internet of Things10.1145/3277593.3277607(1-8)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3277593.3277607

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents