A large gap exists between the potential yield and the yield realized at the agricultural field. Among the factors contributing towards this yield gap are the biotic stresses that affect the crops growth and development. Severity of infestation of the pests and diseases differs between agroclimatic region, individual crops and seasons within a region. Information about the timing of start of infestation of these diseases and pests with their gradual progress in advance could enable plan necessary pesticide schedule for the season, region on the particular crop against the specific menace expected. This could be enabled by development of region, crop and pest-specific prediction models to forewarn these menaces. In India most (70%) of the land-holding size of farmers average 0.39 ha (some even 20 m x 20 m) and only 1% crop growers hold< 10 ha (mean: 17.3 ha). Patchiness of disease and pest incidence could pose problems in its proper assessment and management. Thus, such exercise could be highly time-consuming and labour-intensive for the seventh largest country with difficult terrain, 66% gross cropped area under food crops, lacking in number of skilled manpower and shrinking resources. Remote sensing overcomes such limitations with ability to access all parts of the country and can often achieve a high spatial, temporal and spectral resolution and thus leading to an accurate estimation of area affected. Due to pest and disease stress plants showed different behavior in terms of physiological and morphological changes lead to symptoms such as wilting, curling of leaf, stunned growth, reduction in leaf area due to severe defoliation or chlorosis or necrosis of photosynthetically active parts (Prabhakar et al., 2011; Booteet al., 1983; Aggarwal et al., 2006). Damage evaluation of diseases has been largely done by visual inspections and quantification but visual quantification of plant pest and diseases with accuracy and precision is a tough task. Utilization of remote sensing techniques are based on the assumption that plant pest and disease stresses interfere with physical structure and function of plant and influence the absorption of light energy and therefore changes the reflectance spectrum of plants. Moreover, remote sensing provides better means to objectively quantify crop stress than visual methods and it can be used repeatedly to collect sample measurements non-destructively and non-invasively (Nutteret et al., 1990; Nilson, 1995). Recent advances in the field of spectroscopy and other remote sensing techniques offer much needed technology of hyperspectral remote sensing (Prabhakar et al., 2011). Hyperspectral remote sensing for disease detection helps in monitoring the diseases in plants with the help of different plant spectral properties at the visible, near infrared and shortwave infrared regions ranging from 350 – 2500 nm, which develops specific signatures for a specific stress for a given plant (Yang et al., 2009). It has been effectively used in assessment of disease in agricultural crops like wheat, rice, tomato etc across the world. Cotton (Gissypium hirsutum L.) is one of the major commercial crops grown in India, and supports about 60 million people in the country directly or indirectly through the process of production, processing, marketing and trade (Prabhakar et al., 2011). India ranks first in global acreage, occupying about 33% of world cotton area. With regard to production it is ranked second next to China. In recent years, farmers are facing many challenges because of rising incidents of white flies, jassid, leafhoppers, aphids, mealybugs and stainers. Whiteflies are tiny, sap- sucking insects that may become abundant in vegetable and ornamental plantings, especially during warm weather. They excrete sticky honeydew and cause yellowing or death of leaves. Outbreaks often occur when the natural biological control is disrupted. Management is difficult once populations are high. White flies develop rapidly in warm weather, and populations can build up quickly in situations where natural enemies are ineffective and when weather and host plants favor outbreaks. Large colonies often develop on the undersides of leaves. The most common pest species such as greenhouse white fly (Trialeurodes vaporariorum) and sweet potato white fly (Bemisia tabaci) have a wide host range that includes many weeds and crops. White flies normally lay their tiny oblong eggs on the undersides of leaves. The
eggs hatch, and the young white flies gradually increase in size through four nymphal stages called instars. The first
nymphal stage (crawler) is barely visible even with a hand lens. The crawlers move around for several hours before settling to begin feeding. Later nymphal stages are immobile, oval, and flattened, with greatly reduced legs and
antennae, like small scale insects. The winged adult emerges from the last nymphal stage (sometimes called a pupa,
although whiteflies don’t have a true complete metamorphosis). All stages feed by sucking plant juices from leaves and
excreting excess liquid as drops of honeydew as they feed. White flies use their piercing, needle like mouthparts to suck
sap from phloem, the food-conducting tissues in plant stems and leaves. Large populations can cause leaves to turn
yellow, appear dry, or fall off plants. Like aphids, white flies excrete sugary liquid called honeydew, so leaves may be
sticky or covered with black sooty mold that grows on honeydew. The honeydew attracts ants, which interfere with the
activities of natural enemies that may control white flies and other pests. High white fly infestation was reported at several locations in Punjab during year 2015. The application of non-destructive methods to detect vegetation stress at an
early stage of its development is very important for pest management in commercially important crops. Earlier few studies have been done to characterize reflectance spectra of nutrient stress nitrogen deficiency and irrigation management for cotton but no literature is available regarding characterization of spectral reflectance to study white fly infestation. Therefore, the primary objectives of this study are: (i) to study changes in chlorophyll content and water content due to white fly infestation. (ii) characterization of spectral signature from cotton crop infested by white fly, (iii)
establishment of most sensitive wavebands to white fly infestation.
Hyperspectral remote sensing can aid in discriminating crop residue owing to the ability of narrow bands to capture the
unique absorption feature of soil and residue. The present study was carried out to find out the suitable narrow spectral
bands and hyper-spectral indices for discriminating wheat residue (stubble and burnt). Ground spectra of wheat residue
and the adjoining soil were collected using the ASD fieldspec™ spectroradiometer. The best spectral range was derived
using the Stepwise Discriminating Analysis (SDA). ‘F’ statistics from one-way ANOVA was used to find out the best
index for discriminating wheat residue from soil. EO1-Hyperion data over Anand-Borsad region of Gujarat state in India
was acquired free of cost from USGS earth explorer website (http://eo1.usgs.gov/) to apply the field based result over the
Hyperion scene. Spectral Angle Mapper (SAM) classification scheme was used to generate the wheat residue cover over
the Hyperion scene. Among the hyperspectral indices evaluated for this study the Cellulose Absorption Index (CAI) was
found to be the best and hence CAI was used to classify the Hyperion scene for discriminating crop residue in field and
also the burnt wheat residue. Results indicated that the wave bands at 10 nm width in the SWIR spectral region
specifically from 1500-1700nm and 1900 to 2300nm are most suitable for wheat residue discrimination. The SAM
classification technique is suitable for classifying the wheat residues with an overall accuracy of around 80 % whereas
classification based on CAI could be used successfully to identify both wheat stubble and the burnt residues. This study
concluded that wheat residue can be mapped for a large area with an accuracy of 80% using the space borne
hyperspectral data with.
Banana is one of the major crops of India with increasing export potential. It is important to estimate the production and acreage of the crop. Thus, the present study was carried out to evolve a suitable methodology for estimating banana acreage. Area estimation methodology was devised around the fact that unlike other crops, the time of plantation of banana is different for different farmers as per their local practices or conditions. Thus in order to capture the peak signatures, biowindow of 6 months was considered, its NDVI pattern studied and the optimum two months were considered when banana could be distinguished from other competing crops. The final area of banana for the particular growing cycle was computed by integrating the areas of these two months using LISS III data with spatial resolution of 23m. Estimated banana acreage in the three districts were 11857Ha, 15202ha and 11373Ha for Bharuch, Anand and Vadodara respectively with corresponding accuracy of 91.8%, 90% and 88.16%. Study further compared the use of LISS IV data of 5.8m spatial resolution for estimation of banana using object based as well as per-pixel classification and the results were compared with statistical reports for both the approaches. In the current paper we depict the various methodologies to accurately estimate the banana acreage.
In India, in terms of area under cultivation, citrus is the third most cultivated fruit crop after Banana and Mango. Among citrus group, lime is one of the most important horticultural crops in India as the demand for its consumption is very high. Hence, preparing citrus crop inventories using remote sensing techniques would help in maintaining a record of its area and production statistics. This study shows how accurately citrus orchard can be classified using both IRS Resourcesat-2 LISS-III and LISS-IV data and depicts the optimum bio-widow for procuring satellite data to achieve high classification accuracy required for maintaining inventory of crop. Findings of the study show classification accuracy increased from 55% (using LISS-III) to 77% (using LISS-IV). Also, according to classified outputs and NDVI values obtained, April and May months were identified as optimum bio-window for citrus crop identification.
The repetitive cultivation of an ordered succession of crops (or crop and fallow) on the same land defined as crop
rotation has a significant role on sustainability of agricultural practice. This paper highlights the methodology used to
map seasonal cropping pattern and crop rotation of West Bengal state in India. Multi-date, remote sensing data of IRS
WiFS and Radarsat SAR were used to map seasonal cropping patterns, which were combined to derive the crop rotation
map. Three distinct crop-growing seasons could be identified. The main one coinciding with monsoon from June-
October, followed by winter crop season from November- February and the summer one March-June. It was feasible to
classify seven major crops using the SAR and WiFS data sets. Rice is the dominant crop in wet season occupying more
than 75 per cent of net sown area. Mustard, potato, wheat, gram, rice are the major dry season crops. Rice-rice, ricepotato,
rice-wheat, rice-mustard, rice-gram, and jute-rice were the major two crop rotations. Rice-fallow was the
dominant practice accounting for 55 per cent of area.