Review on IoT Mobile App based on Rural Development in Terms of Agriculture

As India is still a developing country, it has a lot of rural areas wherein the living conditions and standards are below world standards and may even be on the underdeveloped scale of living standards. In order to achieve development in these regions the first and foremost step to initiate is to improve the agriculture standards and methodologies and bring in new technology to improve the methods used in agriculture which is the major source of income to these people. This project is a four staged project which intends on improving the agriculture standards of India. The first stage of the project is an automated humidity and moisture control for the soil, this will help the farmers in automating certain aspects and hence eliminate certain human errors and improve yield. The second stage of the project is an agriculture auction portal wherein the farmers can directly auction their products to the wholesaler without the need of a middle man/broker. The third stage of the project is an android app which conducts various surveys and suggests a new farmer the type of farming/seeds to be planted / soil information and other such relevant data in respect to agriculture which would help increase the yield for a new farmer. The last part of the project is a seed cum financial bank which helps the farmers by providing financial as well as seed aid in times of financial crisis.

Cropping potential or the red-laterite-gravelly belt of West Bengal based on moisture availability index and soil characteristics

Crop potential has been brought out over the red-laterite-gravelly belt of West Bengal using Moisture Availability Index (MAI) and broad soil information. MAI indicates that a crop of 15. 18-20 and 22-24 weeks. duration at 80%, 50% and 30% probability levels respectively maybe raised from this belt. In most of the stations of the belt, rice could be raised in eight out of every ten years without encountering much waterstress period. At lower probability levels. after rice, pulses like gram. tur and lentil and oilseeds like rapeseed and mustard may be raised based on residual soil moisture. In low rainfall years sorghum. groundnut, maize could be introduced in place of rice in the kharif season. Emphasis should also be given on agro-forestry and horticultural crops for increasing and stabilizing agricultural production.

Mapping Soil Properties to Advance the State of Spatial Soil Information for Greater Food Security on US Tribal Lands

Knowledge, data, and understanding of soils is key for advancing agriculture and society. There is currently a critical need for sustainable soil management tools for enhanced food security on Native American Tribal Lands. Tribal Reservations have basic soil information and limited access to conservation programs provided to other U.S producers. The objective of this study was to create first ever high-resolution digital soil property maps of Quapaw Tribal Lands with limited data for sustainable soil resource management. We used a digital soil mapping (DSM) approach based on fuzzy logic to model the spatial distribution of 24 soil properties at 0–15 and 15–30 cm depths. A digital elevation model with 3 m resolution was used to derive terrain variables and a total of 28 samples were collected at 0–30 cm over the 22,880-ha reservation. Additionally, soil property maps were derived from Gridded Soil Survey Geographic Database (gSSURGO) for comparison. When comparing properties modeled by DSM to those derived from gSSURGO, DSM resulted in lower root mean squared error (RMSE) for percent clay and sand at 0–15 cm, and cation exchange capacity, percent clay, and pH at 15–30 cm. Conversely, gSSURGO-derived maps resulted in lower RMSE for cation exchange capacity, pH, and percent silt at the 0–15 cm depth, and percent sand and silt at the 15–30 cm depth. Although, some of the soil properties derived from gSSURGO had lower RMSE, spatial soil property patterns modeled by DSM were in better agreement with the topographic complexity and expected soil-landscape relationships. The proposed DSM approach developed property maps at high-resolution, which sets the baseline for production of new spatial soil information for Quapaw Tribal soils. It is expected that these maps and future versions will be useful for soil, crop, and land-use decisions at the farm and Tribal-level for increased agricultural productivity and economic development. Overall, this study provides a framework for developing DSM on Tribal Lands for improving the accuracy and detail of soil property maps (relative to off the shelf products such as SSURGO) that better represents soil-forming environments and the spatial variability of soil properties on Tribal Lands.

Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library

Traditional laboratory methods for acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modeling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-infrared range includes soil samples from the Biodiversity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally resolved data from the Swiss Soil Monitoring Network (NABO). Given that less than 2 % of the samples in the SSL originate from organic soils, we aimed to develop both an efficient calibration sampling scheme and accurate modeling strategy to estimate the soil carbon (SC) contents of heterogeneous samples between 0 and 2 m depth from 26 locations within two drained peatland regions (School of Agricultural, Forest and Food Sciences (HAFL) data set; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of the SSL. We used partial least square regressions (PLSRs), together with five repetitions of a location-grouped, 10-fold cross-validation, to predict SC ranging from 1 % to 52 % in the local HAFL data set. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL combined with local samples (2), commonly referred to as spiking, and subsets of local and SSL samples optimized for the peatland target sites using the resampling local (RS-LOCAL) algorithm (3). Using local and RS-LOCAL calibrations with at least five local samples, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.93 to 0.97; bias = -0.07 to 1.65; root mean square error (RMSE) = 2.71 % to 3.89 % total carbon; ratio of performance to deviation (RPD) = 3.38 to 4.86; and ratio of performance to interquartile range (RPIQ) = 4.93 to 7.09). However, calibrations using RS-LOCAL only required five or 10 local samples for very accurate models (RMSE = 3.16 % and 2.71 % total carbon, respectively), while purely local calibrations required 50 samples for similarly accurate results (RMSE < 3 % total carbon). Of the three approaches, the entire SSL spiked with local samples for model calibration led to validations with the lowest performance in terms of R2, bias, RMSE, RPD and RPIQ. Hence, we show that a simple and comprehensible modeling approach, using RS-LOCAL together with a SSL, is an efficient and accurate strategy when using infrared spectroscopy. It decreases field and laboratory work, the bias of SSL spiking approaches and the uncertainty of local models. If adequately mined, the information in the SSL is sufficient to predict SC in new and independent study regions, even if the local soil characteristics are very different from the ones in the SSL. This will help to efficiently scale up the acquisition of quantitative soil information over space and time.

Added value of geophysics-based soil mapping in agro-ecosystem simulations

There is an increased demand for quantitative high-resolution soil maps that enable within-field management. Commonly available soil maps are generally not suited for this purpose, but digital soil mapping and geophysical methods in particular allow soil information to be obtained with an unprecedented level of detail. However, it is often difficult to quantify the added value of such high-resolution soil information for agricultural management and agro-ecosystem modelling. In this study, a detailed geophysics-based soil map was compared to two commonly available general-purpose soil maps. In particular, the three maps were used as input for crop growth models to simulate leaf area index (LAI) of five crops for an area of ∼ 1 km2. The simulated development of LAI for the five crops was evaluated using LAI obtained from multispectral satellite images. Overall, it was found that the geophysics-based soil map provided better LAI predictions than the two general-purpose soil maps in terms of correlation coefficient R2, model efficiency (ME), and root mean square error (RMSE). Improved performance was most apparent in the case of prolonged periods of drought and was strongly related to the combination of soil characteristics and crop type.

Semi-supervised learning for increasing transferability of machine learning in digital soil mapping

&lt;p&gt;The most critical aspect of application of digital soil mapping is its limited transferability. Modelling soil properties for regions where no or only sparse soil information is available is highly uncertain, when using the low-cost geo-spatial environmental covariates alone. To overcome this drawback, transfer learning has been introduced in different environmental sciences, including soil science. The general idea behind extrapolation of soil information with transfer learning in soil science is that the target area to transfer to is alike, e.g. in terms of soil-forming factors, and the same machine learning rules can be applied. Supervised machine learning, so far, has been used to transfer the soil information from the reference to the target areas with very similar environmental characteristics between both. Hence, it is unclear how machine learning can perform for other target regions with different environmental characteristics. Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data (reference area) with a large amount of unlabeled data (target area) during training. In this study, we explored if semi-supervised learning could improve the transferability of digital soil mapping relative to supervised learning methods. Soil data for two arid regions and associated environmental covariates were obtained. Semi-supervised learning and supervised learning models were trained based on the data in the reference area and then tested based on the data in the target area. The results of this study indicated the higher power of semi-supervised learning for transferring soil information from one area to another in comparison to the supervised learning method.&amp;#160;&amp;#160;&amp;#160;&lt;/p&gt;