A deep learning model to predict lower temperatures in agriculture

[EN] Deep learning techniques provide a novel framework for prediction and classification in decision-making procedures that are widely applied in different fields. Precision agriculture is one of these fields where the use of decision-making technologies provides better production with better costs and a greater benefit for farmers. This paper develops an intelligent framework based on a deep learning model for early prediction of crop frost to help farmers activate anti-frost techniques to save the crop. This model is based on a long short-term memory (LSTM) model and it is designed to predict low temperatures. The model is based on information from an IoT infrastructure deployed on two plots in Murcia (Southeast of Spain). Three experiments are performed; a cross validation to validate the model from the most pessimistic point of view, a validation of 24 consecutive hours of temperatures, in order to know 24 hours before the possible temperature drop and a comparison with two traditional time series prediction techniques, namely Auto Regressive Integrated Moving Average and the Gaussian process. The results obtained are satisfactory, being better the results of the LSTM, obtaining an average quadratic error of less than a Celsius degree and a determination coefficient R-2 greater than 0.95.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5.Guillén-Navarro, MA.; Martínez-España, R.; Llanes, A.; Bueno-Crespo, A.; Cecilia-Canales, JM. (2020). A deep learning model to predict lower temperatures in agriculture. Journal of Ambient Intelligence and Smart Environments. 12(1):21-34. https://doi.org/10.3233/AIS-200546S2134121Abdullahi, H. S., Sheriff, R. E., & Mahieddine, F. (2017). Convolution neural network in precision agriculture for plant image recognition and classification. 2017 Seventh International Conference on Innovative Computing Technology (INTECH). doi:10.1109/intech.2017.8102436K.A. Al-Gaadi, A.A. Hassaballa, E. Tola, A.G. Kayad, R. Madugundu, B. Alblewi and F. Assiri, Prediction of potato crop yield using precision agriculture techniques, PLoS ONE 11(9) (2016), e0162219.Barlow, K. M., Christy, B. P., O’Leary, G. J., Riffkin, P. A., & Nuttall, J. G. (2015). Simulating the impact of extreme heat and frost events on wheat crop production: A review. Field Crops Research, 171, 109-119. doi:10.1016/j.fcr.2014.11.010Bendre, M. R., Thool, R. C., & Thool, V. R. (2015). Big data in precision agriculture: Weather forecasting for future farming. 2015 1st International Conference on Next Generation Computing Technologies (NGCT). doi:10.1109/ngct.2015.7375220Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213. doi:10.1016/j.ins.2011.12.028Brahim-Belhouari, S., & Bermak, A. (2004). Gaussian process for nonstationary time series prediction. Computational Statistics & Data Analysis, 47(4), 705-712. doi:10.1016/j.csda.2004.02.006D. Bretreger, J. Quijano, J. Awad et al., Monitoring irrigation volumes using climate data and remote sensing observations, in: Hydrology and Water Resources Symposium (HWRS 2018): Water and Communities, Engineers Australia, 2018, pp. 112–123.Brockwell, P. J., & Davis, R. A. (2016). Introduction to Time Series and Forecasting. Springer Texts in Statistics. doi:10.1007/978-3-319-29854-2Chen, S. W., Shivakumar, S. S., Dcunha, S., Das, J., Okon, E., Qu, C., … Kumar, V. (2017). Counting Apples and Oranges With Deep Learning: A Data-Driven Approach. IEEE Robotics and Automation Letters, 2(2), 781-788. doi:10.1109/lra.2017.2651944I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016.Guillén‐Navarro, M. A., Martínez‐España, R., López, B., & Cecilia, J. M. (2019). A high‐performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience, 33(2). doi:10.1002/cpe.5299Guillen-Navarro, M. A., Pereniguez-Garcia, F., & Martinez-Espana, R. (2017). IoT-based System to Forecast Crop Frost. 2017 International Conference on Intelligent Environments (IE). doi:10.1109/ie.2017.38Haider, S., Naqvi, S., Akram, T., Umar, G., Shahzad, A., Sial, M., … Kamran, M. (2019). LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan. Agronomy, 9(2), 72. doi:10.3390/agronomy9020072Harun, A. N., Kassim, M. R. M., Mat, I., & Ramli, S. S. (2015). Precision irrigation using Wireless Sensor Network. 2015 International Conference on Smart Sensors and Application (ICSSA). doi:10.1109/icssa.2015.7322513Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735S. Hochreiter and J. Schmidhuber, LSTM can solve hard long time lag problems, in: Advances in Neural Information Processing Systems, 1997, pp. 473–479.Huang, R., Zhang, C., Huang, J., Zhu, D., Wang, L., & Liu, J. (2015). Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data. Remote Sensing, 7(7), 8728-8756. doi:10.3390/rs70708728Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. doi:10.1016/j.compag.2018.02.016Kratzert, F., Klotz, D., Brenner, C., Schulz, K., & Herrnegger, M. (2018). Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks. doi:10.5194/hess-2018-247Mahlein, A.-K. (2016). Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. Plant Disease, 100(2), 241-251. doi:10.1094/pdis-03-15-0340-feMilioto, A., Lottes, P., & Stachniss, C. (2017). REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W3, 41-48. doi:10.5194/isprs-annals-iv-2-w3-41-2017Mohammadi, K., Shamshirband, S., Motamedi, S., Petković, D., Hashim, R., & Gocic, M. (2015). Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture, 117, 214-225. doi:10.1016/j.compag.2015.08.008Pantazi, X. E., Moshou, D., Alexandridis, T., Whetton, R. L., & Mouazen, A. M. (2016). Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture, 121, 57-65. doi:10.1016/j.compag.2015.11.018Pierpaoli, E., Carli, G., Pignatti, E., & Canavari, M. (2013). Drivers of Precision Agriculture Technologies Adoption: A Literature Review. Procedia Technology, 8, 61-69. doi:10.1016/j.protcy.2013.11.010Pujari, J. D., Yakkundimath, R., & Byadgi, A. S. (2015). Image Processing Based Detection of Fungal Diseases in Plants. Procedia Computer Science, 46, 1802-1808. doi:10.1016/j.procs.2015.02.137Ray, P. P. (2017). Internet of things for smart agriculture: Technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments, 9(4), 395-420. doi:10.3233/ais-170440Salman, A. G., Heryadi, Y., Abdurahman, E., & Suparta, W. (2018). Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting. Procedia Computer Science, 135, 89-98. doi:10.1016/j.procs.2018.08.153Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117. doi:10.1016/j.neunet.2014.09.003Shareef, Z., & Reddy, S. R. N. (2018). Wireless sensor network for aquaculture: Review, survey, and case study of aquaculture practices in western Godavari region. Journal of Ambient Intelligence and Smart Environments, 10(5), 409-423. doi:10.3233/ais-180498Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience, 2016, 1-11. doi:10.1155/2016/3289801Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69-80. doi:10.1016/j.agsy.2017.01.023Akram, M., & El, C. (2016). Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks. International Journal of Computer Applications, 143(11), 7-11. doi:10.5120/ijca2016910497Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6), 693-712. doi:10.1007/s11119-012-9274-5Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018). Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918-929. doi:10.1016/j.jhydrol.2018.04.065Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and Electronics in Agriculture, 36(2-3), 113-132. doi:10.1016/s0168-1699(02)00096-

A large-scale field assessment of carbon stocks in human-modified tropical forests

Tropical rainforests store enormous amounts of carbon, the protection of which represents a vital component of efforts to mitigate global climate change. Currently, tropical forest conservation, science, policies, and climate mitigation actions focus predominantly on reducing carbon emissions from deforestation alone. However, every year vast areas of the humid tropics are disturbed by selective logging, understory fires, and habitat fragmentation. There is an urgent need to understand the effect of such disturbances on carbon stocks, and how stocks in disturbed forests compare to those found in undisturbed primary forests as well as in regenerating secondary forests. Here, we present the results of the largest field study to date on the impacts of human disturbances on above and belowground carbon stocks in tropical forests. Live vegetation, the largest carbon pool, was extremely sensitive to disturbance: forests that experienced both selective logging and understory fires stored, on average, 40% less aboveground carbon than undisturbed forests and were structurally similar to secondary forests. Edge effects also played an important role in explaining variability in aboveground carbon stocks of disturbed forests. Results indicate a potential rapid recovery of the dead wood and litter carbon pools, while soil stocks (0–30 cm) appeared to be resistant to the effects of logging and fire. Carbon loss and subsequent emissions due to human disturbances remain largely unaccounted for in greenhouse gas inventories, but by comparing our estimates of depleted carbon stocks in disturbed forests with Brazilian government assessments of the total forest area annually disturbed in the Amazon, we show that these emissions could represent up to 40% of the carbon loss from deforestation in the region. We conclude that conservation programs aiming to ensure the long-term permanence of forest carbon stocks, such as REDD+, will remain limited in their success unless they effectively avoid degradation as well as deforestation

The Grizzly, February 22, 1980

Freshmen Survey Results Explored • Songfest Boycott Considered • Career Planning Close-up • Eilts Selected As Graduation Speaker • USGA Notes • Baltz Returns to Union Coffeehouse • 1979 Music Awards • Captain Ray of Light\u27s Pseudo-Science • 1980 Spring Fraternity Pledge Classes • Stapp Enthralls Audience • 1980-81 Roster of Classes • W\u27s Basketball Downs Drexel • Ursinus To Host Grandmaster • Swimming MACs Start Tomorrow • Intramural Hoop Playoffs Open • Albright Downs Hoopsters, 103-82 • Wrestlers Post 6-9-1 Final Tally • Pitt Edges Gymnasts By Onehttps://digitalcommons.ursinus.edu/grizzlynews/1034/thumbnail.jp

An Unbiased Survey of 500 Nearby Stars for Debris Disks: A JCMT Legacy Program

We present the scientific motivation and observing plan for an upcoming detection survey for debris disks using the James Clerk Maxwell Telescope. The SCUBA-2 Unbiased Nearby Stars (SUNS) Survey will observe 500 nearby main sequence and sub-giant stars (100 of each of the A, F, G, K and M spectral classes) to the 850 micron extragalactic confusion limit to search for evidence of submillimeter excess, an indication of circumstellar material. The survey distance boundaries are 8.6, 16.5, 22, 25 and 45 pc for M, K, G, F and A stars, respectively, and all targets lie between the declinations of -40 deg to 80 deg. In this survey, no star will be rejected based on its inherent properties: binarity, presence of planetary companions, spectral type or age. This will be the first unbiased survey for debris disks since IRAS. We expect to detect ~125 debris disks, including ~50 cold disks not detectable in current shorter wavelength surveys. A substantial amount of complementary data will be required to constrain the temperatures and masses of discovered disks. High resolution studies will likely be required to resolve many of the disks. Therefore, these systems will be the focus of future observational studies using a variety of observatories to characterize their physical properties. For non-detected systems, this survey will set constraints (upper limits) on the amount of circumstellar dust, of typically 200 times the Kuiper Belt mass, but as low as 10 times the Kuiper Belt mass for the nearest stars in the sample (approximately 2 pc).Comment: 11 pages, 7 figures (3 color), accepted by the Publications of the Astronomical Society of the Pacifi

Effect of telecare on use of health and social care services: findings from the Whole Systems Demonstrator cluster randomised trial

Objective: to assess the impact of telecare on the use of social and health care. Part of the evaluation of the Whole Systems Demonstrator trial. Participants and setting: a total of 2,600 people with social care needs were recruited from 217 general practices in three areas in England. Design: a cluster randomised trial comparing telecare with usual care, general practice being the unit of randomisation. Participants were followed up for 12 months and analyses were conducted as intention-to-treat. Data sources: trial data were linked at the person level to administrative data sets on care funded at least in part by local authorities or the National Health Service. Main outcome measures: the proportion of people admitted to hospital within 12 months. Secondary endpoints included mortality, rates of secondary care use (seven different metrics), contacts with general practitioners and practice nurses, proportion of people admitted to permanent residential or nursing care, weeks in domiciliary social care and notional costs. Results: 46.8% of intervention participants were admitted to hospital, compared with 49.2% of controls. Unadjusted differences were not statistically significant (odds ratio: 0.90, 95% CI: 0.75–1.07, P = 0.211). They reached statistical significance after adjusting for baseline covariates, but this was not replicated when adjusting for the predictive risk score. Secondary metrics including impacts on social care use were not statistically significant. Conclusions: telecare as implemented in the Whole Systems Demonstrator trial did not lead to significant reductions in service use, at least in terms of results assessed over 12 months

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Evolution of Linear Absorption and Nonlinear Optical Properties in V-Shaped Ruthenium(II)-Based Chromophores

In this article, we describe a series of complexes with electron-rich cis-{Ru^(II)(NH_3)_4}^(2+) centers coordinated to two pyridyl ligands bearing N-methyl/arylpyridinium electron-acceptor groups. These V-shaped dipolar species are new, extended members of a class of chromophores first reported by us (Coe, B. J. et al. J. Am. Chem. Soc. 2005, 127, 4845−4859). They have been isolated as their PF_6− salts and characterized by using various techniques including ^1H NMR and electronic absorption spectroscopies and cyclic voltammetry. Reversible Ru^(III/II) waves show that the new complexes are potentially redox-switchable chromophores. Single crystal X-ray structures have been obtained for four complex salts; three of these crystallize noncentrosymmetrically, but with the individual molecular dipoles aligned largely antiparallel. Very large molecular first hyperpolarizabilities β have been determined by using hyper-Rayleigh scattering (HRS) with an 800 nm laser and also via Stark (electroabsorption) spectroscopic studies on the intense, visible d → π^* metal-to-ligand charge-transfer (MLCT) and π → π^* intraligand charge-transfer (ILCT) bands. The latter measurements afford total nonresonant β_0 responses as high as ca. 600 × 10^(−30) esu. These pseudo-C_(2v) chromophores show two substantial components of the β tensor, β_(zzz) and β_(zyy), although the relative significance of these varies with the physical method applied. According to HRS, β_(zzz) dominates in all cases, whereas the Stark analyses indicate that β_(zyy) is dominant in the shorter chromophores, but β_(zzz) and β_(zyy) are similar for the extended species. In contrast, finite field calculations predict that β_(zyy) is always the major component. Time-dependent density functional theory calculations predict increasing ILCT character for the nominally MLCT transitions and accompanying blue-shifts of the visible absorptions, as the ligand π-systems are extended. Such unusual behavior has also been observed with related 1D complexes (Coe, B. J. et al. J. Am. Chem. Soc. 2004, 126, 3880−3891)