Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on "temporal feature descriptors" that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for Remote Sens. 2014, 6 10775 each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security.

Agricultural monitoring systems require spatio-temporal information on widely cultivated staple crops like rice. More emphasis has been made on area estimation and crop detection than on the temporal aspects of crop cultivation, but seasonal and temporal information such as i) crop duration, ii) date of crop establishment and iii) cropping intensity are as important as area for understanding crop production. Rice cropping systems are diverse because genetic, environmental and management factors (GxExM combinations) influence the spatio-temporal patterns of cultivation. We present a rule based algorithm called PhenoRice for automatic extraction of temporal information on the rice crop using moderate resolution hypertemporal optical imagery from MODIS. Performance of PhenoRice against spatially and temporally explicit reference information was tested in three diverse sites: rice-fallow (Italy), rice-other crop (India) and rice-rice (Philippines) systems. Regional product accuracy assessments showed that PhenoRice made a conservative, spatially representative and robust detection of rice cultivation in all sites (r 2 between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r 2 = 0.98, Mean Error = 4.07 days, Mean Absolute Error = 9.95 days, p < 0.01). Variability in algorithm performance in different conditions in each site (irrigated vs rainfed, direct seeding vs transplanting, fragmented vs clustered rice landscapes and the impact of cloud contamination) was analysed and discussed. Analysis of the maps revealed that cropping intensity and season length per site matched well with local information on agro-practices and cultivated varieties. The results show that PhenoRice is robust for deriving essential temporal descriptions of rice systems in both temperate and tropical regions at a level of spatial and temporal detail that is suitable for regional crop monitoring on a seasonal basis. This work is licensed under a Creative Commons "Attribution-NonCommercial-NoDerivatives 4.0 International" license.

Smart farming is a development that has emphasized information and communication technology used in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies, the Internet of Things (IoT), and cloud computing are anticipated to inspire growth and initiate the use of robots and artificial intelligence in farming. Such ground-breaking deviations are unsettling current agriculture approaches, while also presenting a range of challenges. This paper investigates the tools and equipment used in applications of wireless sensors in IoT agriculture, and the anticipated challenges faced when merging technology with conventional farming activities. Furthermore, this technical knowledge is helpful to growers during crop periods from sowing to harvest; and applications in both packing and transport are also investigated.

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ABSTRACT:Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87-92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85-96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.