Crop Classification using Biologically-inspired Techniques with High Resolution Satellite Image

Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.

Context-dependency of a complex fruit-frugivore mutualism: temporal variation in crop size and neighborhood effects

The quantity of fruit consumed by dispersers is highly variable among individuals within plant populations. The outcome Of Such selection operated by firugivores has been examined mostly with respect to changing spatial contexts. The influence of varying temporal contexts on frugivore choice, and their possible demographic and evolutionary consequences is poorly understood. We examined if temporal variation in fruit availability across a hierarchy of nested temporal levels (interannual, intraseasonal, 120 h, 24 h) altered frugivore choice for a complex seed dispersal system in dry tropical forests of southern India. The interactions between Phyllanthus emblica and its primary disperser (ruminants) was mediated by another frugivore (a primate),which made large quantities of fruit available on the ground to ruminants. The direction and strength of crop size and neighborhood effects on this interaction varied with changing temporal contexts.Fruit availability was higher in the first of the two study years, and at the start of the season in both years. Fruit persistence on trees,determined by primate foraging, was influenced by crop size andconspecific neighborhood densities only in the high fruit availability year. Fruit removal by ruminants was influenced by crop size in both years and neighborhood densities only in the high availability year. In both years, these effects were stronger at the start of the season.Intraseasonal reduction in fruit availability diminished inequalities in fruit removal by ruminants and the influence of crop size and fruiting neighborhoods. All trees were not equally attractive to frugivores in a P. emblica population at all points of time. Temporal asymmetry in frugivore-mediated selection could reduce potential for co-evolution between firugivores and plants by diluting selective pressures. Inter-dependencies; formed between disparate animal consumers can add additional levels of complexity to plant-frugivore mutualistic networks and have potential reproductive consequences for specific individuals within populations.

Testing the efficacy of a chilli-tobacco rope fence as a deterrent against crop-raiding elephants

Chilli-based repellents have shown promise as deterrents against crop-raiding elephants in Africa. We experimented with ropes coated with chilli-based repellent as a cheap alternative to existing elephant cropraid deterrent methods in India. Three locations (Buxa Tiger Reserve, Wyanad Wildlife Sanctuary and Hosur Forest Division) representing varying rainfall regimes from high to low, and with histories of intense elephant-agriculture conflict, were selected for the experiments that were conducted over 2-3 months during the pre-harvest period of the kharif season in late 2006. Chilli and tobacco powder mixed with waste oil was applied to ropes strung around agricultural fields of 1.4-5.5 km perimeter and elephant approaches were monitored. Elephants breached the rope fences a few times at all three study sites. Female-led herds were far more deterred (practically 100% reduction) than were solitary males (c. 50%) by the chilli-tobacco rope. Efficacy of this method as a deterrent was significantly better in the low-rainfall regime relative to medium and high-rainfall regimes. The initial promising results present a case for more rigorous experimentation; these would help determine if the elephants avoiding the rope are responding physiologically to the chilli-tobacco smell or merely reacting cautiously to a novel substance in their environment.

Ecology of the Asian Elephant in Southern India. II. Feeding Habits and Crop Raiding Patterns

The Asian elephant's foraging strategy in its natural habitat and in cultivation was studied in southern India during 1981-83. Though elephants consumed at least 112 plant species in the study area, about 85% of their diet consisted of only 25 species from the order Malvales and the families Leguminosae, Palmae, Cyperaceae and Gramineae. Alteration between a predominantly browse diet during the dry season with a grass diet during the early wet season was related to the seasonally changing protein content of grasses. Crop raiding, which was sporadic during the dry season, gradually increased with more area being cultivated with the onset of rains. Raiding frequency reached a peak during October-December, with some villages being raided almost every night, when finger millet (Eleusine coracana) was cultivated by most farmers. The monthly frequency of raiding was related to the seasonal movement of elephant herds and to the size of the enclave. Of their total annual food requirement, adult bull elephants derived an estimated 9.3% and family herds 1.7% in quantity from cultivated land. Cultivated cereal and millet crops provided significantly more protein, calcium and sodium than the wild grasses. Ultimately, crop raiding can be thought of as an extension of the elephant's optimal foraging strategy.

Optimal crop planning and conjunctive use of water resources in a coastal river basin

Due to increasing trend of intensive rice cultivation in a coastal river basin, crop planning and groundwater management are imperative for the sustainable agriculture. For effective management, two models have been developed viz. groundwater balance model and optimum cropping and groundwater management model to determine optimum cropping pattern and groundwater allocation from private and government tubewells according to different soil types (saline and non-saline), type of agriculture (rainfed and irrigated) and seasons (monsoon and winter). A groundwater balance model has been developed considering mass balance approach. The components of the groundwater balance considered are recharge from rainfall, irrigated rice and non-rice fields, base flow from rivers and seepage flow from surface drains. In the second phase, a linear programming optimization model is developed for optimal cropping and groundwater management for maximizing the economic returns. The models developed were applied to a portion of coastal river basin in Orissa State, India and optimal cropping pattern for various scenarios of river flow and groundwater availability was obtained.

Climate change and crop yields in the Indian Cardamom Hills, 1978-2007 CE

In this study we analyzed climate and crop yields data from Indian cardamom hills for the period 1978-2007 to investigate whether there were significant changes in weather elements, and if such changes have had significant impact on the production of spices and plantation crops. Spatial and temporal variations in air temperatures (maximum and minimum), rainfall and relative humidity are evident across stations. The mean air temperature increased significantly during the last 30 years; the greatest increase and the largest significant upward trend was observed in the daily temperature. The highest increase in minimum temperature was registered for June (0.37A degrees C/18 years) at the Myladumpara station. December and January showed greater warming across the stations. Rainfall during the main monsoon months (June-September) showed a downward trend. Relative humidity showed increasing and decreasing trends, respectively, at the cardamom and tea growing tracts. The warming trend coupled with frequent wet and dry spells during the summer is likely to have a favorable effect on insect pests and disease causing organisms thereby pesticide consumption can go up both during excess rainfall and drought years. The incidence of many minor pest insects and disease pathogens has increased in the recent years of our study along with warming. Significant and slight increases in the yield of small cardamom (Elettaria cardamomum M.) and coffee (Coffea arabica), respectively, were noticed in the recent years.; however the improvement of yield in tea (Thea sinensis) and black pepper (Piper nigrum L.) has not been seen in our analysis.

Parameter estimation of a two-horizon soil profile by combining crop canopy and surface soil moisture observations using GLUE

Estimation of soil parameters by inverse modeling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise when heterogeneous layered soils are considered. The objective of this study was to explore the potential of combining observations on surface soil moisture and crop variables - leaf area index (LAI) and above-ground biomass for estimating soil parameters (water holding capacity and soil depth) in a two-layered soil system using inversion of the crop model STICS. This was performed using GLUE method on a synthetic data set on varying soil types and on a data set from a field experiment carried out in two maize plots in South India. The main results were (i) combination of surface soil moisture and above-ground biomass provided consistently good estimates with small uncertainity of soil properties for the two soil layers, for a wide range of soil paramater values, both in the synthetic and the field experiment, (ii) above-ground biomass was found to give relatively better estimates and lower uncertainty than LAI when combined with surface soil moisture, especially for estimation of soil depth, (iii) surface soil moisture data, either alone or combined with crop variables, provided a very good estimate of the water holding capacity of the upper soil layer with very small uncertainty whereas using the surface soil moisture alone gave very poor estimates of the soil properties of the deeper layer, and (iv) using crop variables alone (else above-ground biomass or LAI) provided reasonable estimates of the deeper layer properties depending on the soil type but provided poor estimates of the first layer properties. The robustness of combining observations of the surface soil moisture and the above-ground biomass for estimating two layer soil properties, which was demonstrated using both synthetic and field experiments in this study, needs now to be tested for a broader range of climatic conditions and crop types, to assess its potential for spatial applications. (C) 2012 Elsevier B.V. All rights reserved.

Algal biofuel production and mitigation potential in India

Energy and energy services are the backbone of growth and development in India and is increasingly dependent upon the use of fossil based fuels that lead to greenhouse gases (GHG) emissions and related concerns. Algal biofuels are being evolved as carbon (C)-neutral alternative biofuels. Algae are photosynthetic microorganisms that convert sunlight, water and carbon dioxide (CO2) to various sugars and lipids Tri-Acyl-Glycols (TAG) and show promise as an alternative, renewable and green fuel source for India. Compared to land based oilseed crops algae have potentially higher yields (5-12 g/m(2)/d) and can use locations and water resources not suited for agriculture. Within India, there is little additional land area for algal cultivation and therefore needs to be carried out in places that are already used for agriculture, e.g. flooded paddy lands (20 Mha) with village level technologies and on saline wastelands (3 Mha). Cultivating algae under such conditions requires novel multi-tier, multi-cyclic approaches of sharing land area without causing threats to food and water security as well as demand for additional fertilizer resources by adopting multi-tier cropping (algae-paddy) in decentralized open pond systems. A large part of the algal biofuel production is possible in flooded paddy crop land before the crop reaches dense canopies, in wastewaters (40 billion litres per day), in salt affected lands and in nutrient/diversity impoverished shallow coastline fishery. Mitigation will be achieved through avoidance of GHG, C-capture options and substitution of fossil fuels. Estimates made in this paper suggest that nearly half of the current transportation petro-fuels could be produced at such locations without disruption of food security, water security or overall sustainability. This shift can also provide significant mitigation avenues. The major adaptation needs are related to socio-technical acceptance for reuse of various wastelands, wastewaters and waste-derived energy and by-products through policy and attitude change efforts.

Hierarchical artificial immune system for crop stage classification

This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

Crop stage classification of hyperspectral data using unsupervised techniques

The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

Improved modeling of groundwater recharge in agricultural watersheds using a combination of crop model and remote sensing

For improved water management and efficiency of use in agriculture, studies dealing with coupled crop-surface water-groundwater models are needed. Such integrated models of crop and hydrology can provide accurate quantification of spatio-temporal variations of water balance parameters such as soil moisture store, evapotranspiration and recharge in a catchment. Performance of a coupled crop-hydrology model would depend on the availability of a calibrated crop model for various irrigated/rainfed crops and also on an accurate knowledge of soil hydraulic parameters in the catchment at relevant scale. Moreover, such a coupled model should be designed so as to enable the use/assimilation of recent satellite remote sensing products (optical and microwave) in order to model the processes at catchment scales. In this study we present a framework to couple a crop model with a groundwater model for applications to irrigated groundwater agricultural systems. We discuss the calibration of the STICS crop model and present a methodology to estimate the soil hydraulic parameters by inversion of crop model using both ground and satellite based data. Using this methodology we demonstrate the feasibility of estimation of potential recharge due to spatially varying soil/crop matrix.

Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters

The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for para-meters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.