Above-ground biomass density (AGBD) quantification is crucial for understanding carbon dynamics, climate change, and sustainable forest management. This study integrates Global Ecosystem Dynamics Investigation (GEDI) satellite data with multi-spectral, Synthetic Aperture Radar (SAR) based earth observations and soil data for continuous estimation of forest AGBD. Study was focused on the Indian forest. GEDI’s AGBD data from 10,000 points serves as the dependent variable and independent variables are derived from Sentinel-1, Sentinel-2, Digital Elevation Model (DEM), SoilGrids, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). We evaluated Support Vector Regression, Random Forest Regression, and Light Gradient Boosting Machine algorithms for various feature-set scenarios. Hyperparameter tuning employed grid-search-based cross-validation. Results shows that, LightGBM performed well, being computationally efficient and delivering lower RMSE. For the selected LightGBM model, with Sentinel-1, Sentinel-2, DEM, and forest attributes, an RMSE of 68.02 Mg/ha and R2of 0.57 were achieved. Model-generated AGB maps were compared with openly available National Remote Sensing Centre AGBD data at 100m and existing forest AGBD work. Comparison between model predicted AGBD and literature based maps and studies shows that, our model was able to capture the AGBD variations across multiple forest sub-regions from India.

Mapping sugarcane areas is vital for applications like crop monitoring, yield estimation, environmental monitoring, and land use planning. Traditional supervised learning is hindered by the costly and time-intensive collection of ground truth data, whereas, unsupervised methods encounter performance challenges due to parameter initialization. In this study, we propose a hybrid approach that integrates unsupervised learning with domain knowledge of temporal sugarcane crop descriptors, creating reference data for subsequent supervised learning. The main objective of the study was to map sugarcane areas in the absence of ground reference data by combining unsupervised and supervised learning using Sentinel-1 and 2 observations. The study was carried out in two provinces in central Thailand during the sugarcane season of 2021–2022. X means clustering was applied to the raster stack of temporal NDVI and radar backscatter (in VH polarisation). Fifty polygons were then digitized from each cluster and categorized as sugarcane, cassava, field crops, and non-agriculture using the temporal descriptors for each polygon. Polygons not meeting the temporal descriptors based criteria were removed. Further, RF based supervised classification was carried out using bands of Sentinel-1 and 2, and NDVI as the features and labled polygons as the reference. The samples were divided into training (80% data) and testing (20% data). We tuned the RF classifier for a number of trees ranging from 50 to 500. The model with 350 trees performed better on testing data, with an overall accuracy of 87.75% and a Kappa of 0.873. Slight intermixing between sugarcane and cassava was observed mainly due to the planting/sowing window (March–May) and crop duration in the case of the Ratoon sugarcane crop. Further, we implemented the same model in an adjacent province to evaluate the performance of the proposed approach. The overall accuracy of 75.86% was obtained, and precision and recall for sugarcane were 0.79 and 0.76, respectively. This shows that strong temporal descriptors derived from an unsupervised approach can be combined with supervised learning for mapping sugarcane areas in the absence of ground labels.

Tracking sugar intake has become a trending practice, much like keeping track of calorie intake. However, consumer-grade systems or devices for measuring or detecting sugar levels of unlabeled food items are close to none. In this paper, we discuss how low-cost Multi-Spectral Imaging (MSI) technology can be used to identify the sugar level of grapes in a simple, yet effective manner. Here, grapes are used as a representative for fruit or food. Freshly harvested grapes were used for spectral data collection. Unispectral portable multispectral camera EVK – UNS52000 which has a spectral range of 700-940 nm across 10 spectral bands was used for data collection. Actual sugar content was determined using a handheld refractometer and used for reference. We evaluated the performance of two well-known machine learning-based regression algorithms such as Random Forest (RF) and Light Gradient Boosting Machines (LightGBM). Spectral data derived from images captured in 10 bands were used as features or independent variables and actual sugar measured using a refractometer was used as a reference or the dependent variable in regression models. We tested the model performance using different feature scenarios: a) All 10 spectral bands, b) Top 5 spectral bands and c) Top 3 spectral bands. Results showed that R2 was 0.92 for both RF and LightGBM with negligible variation in RMSE (1.05-1.06 °Brix) when using all 10 bands. There was minimal difference in terms of R2 and RMSE. However, there was a significant difference in model training time with LightGBM being much faster than RF. Moreover, by selecting the top 5 and 3 features, we reduced the R2 to 0.89 and 0.78 respectively in the case of LightGBM. While there is a reduction in R2, selecting the top 5 bands will be useful from an operational perspective in terms of computational power and cost of device development. We aim to design and develop the device using the selected top 5 bands for operational on-the-fly prediction of sugar content in grapes. The proposed approach could be helpful for various stakeholders such as people with Hypoglycemia or Diabetes, grape producers to gauge the maturity of grapes, and industrial use cases for vineyards.

Post-pandemic, wellness and healthcare sector is vocal about balanced food consumption, inclusion of fresh fruits and regular exercise. Freshness and sugar contents of fruits are crucial to understand before their consumption. A normal RGB camera can provide information about the freshness of fruits based on the surface composition of images generated by the camera. Interestingly, the Multi-Spectral Imaging (MSI) provides information that is superior to a standard RGB camera, as it considers the NIR band. In this article, the authors have deliberated how MSI can be used to predict the sugar level and freshness in fruits, particularly grapes using modified EfficientNet-B0 as a Deep Learning (DL) model for the analysis. For predicting sugar level, the average value across the 5-fold cross-validation (cv) achieved by the DL model was RMSE of 2.68 (std 0.33) ◦Bx, MAE of 2.11 (0.34) ◦Bx, MAPE of 12% (2%) using MSI and RMSE of 7.76 (1.79) ◦Bx, MAE of 6.65 (1.69) ◦Bx, MAPE of 41% (9%) using RGB camera images. For freshness prediction, the DL model achieved an average 5-fold cv accuracy of 88.35% (9.71%) using MSI and 82.22% (7.9%) using RGB camera images. Results indicated that MSI can predict both the sugar level and freshness whereas RGB camera images can be used only for predicting the freshness but not the sugar level prediction of the grapes. The findings suggest that MSI offers a valuable and versatile solution for quality assessment in the fruit industry, enabling better dietary choices and healthcare regimes.

Vegetation cover plays a crucial role in enriching the soil carbon content. The sequestered CO2gets released due to exposure of soil to the atmosphere by the process of volatilization. Therefore, there is a need to monitor sustainable farm management practises like cover cropping. Remote Sensing coupled with artificial intelligence helps in non-invasive monitoring of vegetation cover. The main objective of this study is to detect the presence of cover crop using the time-series of remote sensing observations. The study was carried out on selected fields from the Europe region. A total of 60 fields from four countries, namely, France, Germany, Poland, and Spain during 2019–2021 were selected. In this study we proposed a two-step approach for cover crop detection. The first step involves separating vegetation period from fallow/bare soil and snow cover. The second step has mainly focused on the vegetation period to initially separate main crop period and subsequent detection of cover crop for the remainder of the vegetation period. Combination of index based thresholding and phenology indicators was used for cover crop detection. The proposed approach was validated using the ground reference data on presence or absence of a cover crop. Results showed an overall accuracy of 91.7% with an F1 score of 91.2%. Moreover, cover crop detection rate was found to be 92.9%. One field was misclassified as cover crop, whereas it had a dense cover of weeds. This was mainly due to higher peak NDVI value of dense weeds than NDVI threshold.

Remote sensing coupled with machine learning is useful for non-invasive monitoring and prediction of Soil Organic Carbon (SOC) and carbon stock. The use of Sentinel-1 and 2 datasets was attempted in this work for SOC, Bulk Density (BD), and carbon stock estimation. The key objective of this study is to estimate Soil Organic Carbon and Bulk Density for carbon stock assessment in croplands using multi-spectral and Synthetic Aperture Radar (SAR) satellite observations. The fields from Colorado and Nebraska states are selected, and assessment is performed after the harvest of the cropping season in 2020. Soil samples were collected during 10-30 Oct 2020. Regression models were developed using bands and indices derived from Sentinel-1 and 2 datasets as independent variables, however SOC or BD were used individually as dependent variables. Models were developed by considering data from individual states and then combining all the data using Random Forest Regression (RFR) and Support Vector Regression (SVR). Results for the SOC estimation showed that RFR performed better than SVR with individual state data. Lowest RMSE achieved using the RFR were 0.114 and 0.159 for fields from Nebraska and Colorado states. However, SVR outperformed over RFR in the case of BD estimation using the combined data from both states (RMSE = 0.24). Further comparison between estimated carbon stock and actual carbon stock at field level shows the good agreement over fields from Colorado compared to Nebraska.

Agriculture tillage is a fundamental practice in farming that involves preparing the soil for planting crops. It has been an essential technique used by farmers for centuries to improve soil conditions, increase crop yields, and enhance overall agricultural productivity. While tillage information can be acquired through manual field data collection, implementing this approach consistently and systematically over a wide area poses considerable challenges. Instead, remote sensing methods offer a viable option to comprehensively, promptly, and affordably investigate tillage activities. Hence, there is significant value in embracing a remote sensing approach to consistently and methodically monitor tillage practices across various fields. The objective of this research was to determine different types of tillage surfaces by analyzing the radar backscatter response received from the ground. The study used data from the Sentinel-1 satellite, specifically the Interferometric Wide-swath (IW) Ground Range Detected (GRD) dataset, which provided radar measurements in both VV and VH polarizations. To monitor tillage, we utilized supervised classification methods, namely decision tree (DT), random forest (RF), and support vector machine (SVM). Among these classifiers, the RF has the highest test accuracy of 0.86. The obtained results were validated using the ground observation data and found encouraging.

In this study, we used time-series radar backscatter response in VV and VH polarizations (i.e. σ°VVand σ°VH) and ground-based wheat height data to establish the multivariate regression models for wheat height estimate. For generating time-series σ°VVand σ°VH, the C-band Sentinel-1 satellite data were used. The ground height observation was obtained from the farm having Durum wheat variety HI 8759 (also known as Pusa Tejas). We experimented with five different multivariate regression models (MVRM) using combinations of independent variables i.e. σ°VV, σ°VH, σ°VV/σ°VHratio, and days after sowing (DAS). Based on the combination of variables, the five models can be depicted as MVRM1 (σ°VV, σ°VH, and DAS), MVRM2 (σ°VHand DAS), MVRM3 (σ°VVand DAS), MVRM4 (σ°VVand σ°VH) and MVRM5 (σ°VV/σ°VHratio and DAS). Based on statistical observation, it is found that although the model R2for MVRM1 and MVRM2 was about 0.90, the condition number is very high in both cases, which causes multicollinearity. This leads to model overfitting. On the other hand, model R2obtained for MVRM3, MVRM4, and MVRM5 are 0.94, 0.65, and 0.93 respectively and all the models are free from multicollinearity as the condition number is low. Another observation shows that DAS is an important variable in the regression model along with the backscatter response. Overall, based on the statistical parameters, MVRM3 and MVRM5 were found suitable for wheat height estimation. This particular model was validated on an independent wheat farm and found encouraging results.

Crop water stress is one of the major factors limiting crop productivity. Maize (Corn) crop is very sensitive to water stress. Efficient monitoring and detection of water stress is crucial for precision irrigation and sustainable agriculture. The main objective of this study is to detect water stress during grain-fill stage in maize crops using hyperspectral (HS) observations. Maize hybrid field trial plots were selected from Hyderabad region, Telangana, India. HS images of the study area were collected using a hexacopter drone mounted with line scanning HS camera having spectral range of 450–950 nanometer (nm). The line scanning HS drone observations were processed for georeferencing, mosaicing, and ortho-rectification. The study area was divided into two sub-plots, viz. control (i.e., no stress) and water stress during grain-fill stage of the crop. Each plot had 66 sub-plots, each representing a unique variety. Visual spectral analysis was performed to identify the differences and spectral regions suitable for water stress detection. Analysis revealed that the visible region around 680 nm and the Red-Edge (RE) - Near-InfraRed (NIR) region were providing the differences between the no-stress and water stressed plots. The optimal subset of wavelength regions were identified using machine learning (ML) based feature selection. Random Forest (RF) and Support Vector Machine (SVM) classifiers were used to assess the feasibility of the chosen bands. SVM with top 10 bands in the range of 670–780 nm found most effective for detecting grain-fill water stress in maize crop varieties.

The optical satellite observations are affected by cloud cover. Vegetation Optical Depth (VOD) has the potential to provide insights into plant water and vegetation structure. The main objective of this study is to spatially downscale VOD using a Moderate Resolution Imaging Spectrometer (MODIS) and Shuttle Radar Topographic Mission (SRTM) observations. The study considered India geography and post-monsoon cropping season (locally called Rabi season). Data for three different Rabi crop seasons, i.e., 2017–18, 2018–19, and 2019–20, was used in the analysis. The VOD estimates at 25 km scale derived from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) were used for spatial down-scaling. Three day VOD composites were created to cover the study area. MODIS products such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), albedo (black and white sky), and Land Surface Temperature (LST) were similarly acquired at a 3-day interval by constructing a 3-day composite. In addition, SRTM digital elevation model with a spatial resolution of 90 m was used in this study. We carried out regression modeling where VOD was used as a dependent variable, with NDVI, NDWI, Albedo (black and white sky), LST, and elevation as independent variables. We compared three regression algorithms, viz., Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR), using the R-square (R2) as the assessment metric. A comparison between the various regression techniques showed that the SVR outperformed monthly and seasonal models. Further more, a comparison of monthly and seasonal models revealed that the model generated with January data performed best, with an R2of 0.85, followed by R2of 0.82, 0.80, and 0.78 for March, December, and February, respectively. The R2for the seasonal model was 0.83. Finally, for the wheat crop time series of down-scaled VOD and Sentinel 2 based NDVI was compared to gain insights on seasonal variations in VOD. We found that down-scaled VOD and NDVI have a significant agreement.

Nitrogen (N) is one of the key nutrient element needed for optimum crop growth and production. Deficiency of N leads to a decrease in crop production and excess results in poor root growth and leaching into groundwater thereby causing environmental issues. Hence the optimum application of N is needed which is possible by exactly estimating the available quantities of N in the plant. In this study, an attempt has been made to estimate N in Soybean leaves using the hyperspectral and simulated Sentinel-2 observations. Spectral observations of fifteen soybean leaf samples were collected using the EKO MS-720 Spectroradiometer. The instrument operates in the spectral range of 350–1050 nm. and collects data in contiguous 213 bands. Support Vector Regression-based models were evaluated using three feature selection methods, 1) individual hyperspectral bands, 2) Normalized band ratio's and 3) simulated Sentinel-2 bands and indices. Model performance was evaluated using R2. Analysis carried out using the individual hyperspectral bands showed that bands from the red and red-edge region are performing best with R2between 0.872 and 0.876. However, NBR's estimated from band combinations in the red-edge region are performing best with R2between 0.938 - 0.956. Further, we identified a subset of wavelengths to simulate Sentinel-2 spectral bands, results showed that red-edge and narrow NIR bands provide the highest R2between 0.878 and 0.893. We observed that indices such as Canopy Chlorophyll Content Index (CCCI) and Chlorophyll Index Red Edge (CIRE) are performing better for N estimation with R2of 0.946, 0.923, respectively. Based on the observations we can conclude that red, red-edge and narrow NIR region is useful for Soybean N estimation.

In the present study, InSAR coherence products extracted from Sentinel-1 were used for the quantification of the agricultural region affected by normal to large excess rainfall events that occurred in the districts of Gujrat, India in July and August 2020. In this analysis, the coherence values retrieved during co-event (i.e. during rainfall) were comparatively found lower than the pre-event (i.e. before rainfall) for all the sub-divisions of the districts. Here, the pre-event and co-event coherence histograms were drawn and their point of intersection was used to determine an optimal threshold value for coherence below which agriculture area was considered as affected due to rainfall. More than 80 % of the total study area was found affected due to rainfall events. The obtained outcomes were crossexamined with the Landsat-8 images obtained for the study duration and the results were found encouraging.

The main objective of this paper is mapping of Soybean and Cotton crop area using the integration of Sentinel-1 and 2 observations and Deep Neural Networks (DNN). The present research also attempted the identification of early and late sowing of Soybean and Cotton using time-series of Normalized Difference Vegetation Index (NDVI) and VH backscatter. The study was carried out in Wardha district of Maharashtra, India during Kharif 2019. The Sentinel-1 observations available during 15 Jun. to 30 Nov. 2019 and Sentinel-2 maximum NDVI and Normalized Difference Water Index (NDWI) composites during Aug. to Sept. and Oct. to Nov. 2019 were used for Cotton and Soybean area mapping. We evaluated the performance of tuned Random Forest (RF) and DNN for classification of Soybean and Cotton. Results showed that DNN performs better with an overall accuracy of 89.15% and the F-score of 0.856. Further, identification of early and late sown Soybean and Cotton was performed using short time-series of VH backscatter and NDVI. Local minima along with subsequent increase/decrease based approach was developed for the separation of early and late sown crop. Visual comparison with geo-tagged crop growth stage images shows good agreement between estimated and actual sowing time window.

To monitor changes in the landscapes at local to regional scales, a large amount of multi-temporal remote sensing products are required for the analysis. Due to the frequent need for remote sensing-based results, research as well as commercial organizations required automated, faster and more efficient methods for downloading and subsequently, processing the enormous amount of such remote sensing datasets. In this study, we have designed and developed geospatial data processing frameworks enriched with automated data search, download, and processing of Sentinel-1,-2 data. The analysis-ready outputs i.e. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and radar backscatter (σ°) etc. are essential in operational remote sensing use cases such as crop area, crop health, crop loss mapping. The overall framework for Sentinel-1,-2 has been successfully implemented in the Python programming environment.

Paddy residue burning is common across Indo-Gagantic plane i.e. Punjab and Haryana states of India. Rice-Wheat cropping system is intensively followed across the Punjab and Haryana. Every year, Rice is cultivated from May to Oct. followed by the Wheat from Nov. to April. Most of the farmers burn the leftover plant debris after Rice harvesting and clear the fields for the next cropping season. The burning of crop residues releases several particles and gases into the atmosphere which causes huge air-pollution. There is an urgent need to monitor such man-made burning to avoid/minimize the air-water pollution. Satellites such as MODIS and VIIRS provide active fire events data daily. We have attempted to analyze the data provided by VIIRS over Punjab and Haryana. This paper attempts to answer a few research questions such as 1. How are the state and zone-wise trend in active fire events cropping seasons of 2017, 2018, and 2019, 2. When is the peak burning period across Punjab and Haryana during 2017, 2018 and 2019 and 3. What is the district-wise percent change in burning across Punjab and Haryana. Our analysis shows that overall burning reduces by 22% in Punjab compared to both 2017 and 2018 and 49% and 61 % in Haryana. Malwa region of Punjab and Hissar division of Haryana was more prone to burning. We have observed that 26 Oct. to 8 Nov. is a peak time of burning in Punjab, however, maximum burning observed during 26 Oct. to 1 Nov. in Haryana. Maximum number of fire incidences were reported from Fate-habad, Sirsa, Jind and Kaithal, Karnal in Haryana. In Punjab, reports poured in from Sangrur, Bathinda, Ferozpur, and Patiala. This trend analysis of time and location can help administrators to optimize the on-ground human and machine resources.

Tea is considered as a healthy beverage due its antioxidant properties and resultant beneficial effects on human health. India is the second largest producer of tea in the world after China, but every year Tea plants are attacked by several pests and diseases that are responsible for 7-10% loss of the crop. Tea growers spray pesticides on the plants for better control on the pests and to reduce crop loss. The uncontrolled usage of pesticide with ineffective management practices results in high residue levels that are hazardous to human health. In this paper, an attempt has been made to detect the pesticide residue on the tea leaves using hyperspectral sensing. The data on leaves treated with three banned chemicals (Acetamiprid, Cypermethrin and Monocrotophos) and control healthy leaves was collected using spectroradiometer in the spectral range of 350-1052 nm in 213 narrow contiguous bands. The spectral observations of the leaf samples (total of 389 samples) in control and treated with each chemical on consecutive days (i.e. from 1 to 7 days) has been collected. The data in 213 narrow contiguous bands is used as feature set for hyperspectral data analysis. The band selection methods are used to overcome collinearity in spectral observations due to contiguous data. Band selection has been performed by 1. Visual Analysis, 2. Principal Component Analysis and 3. Random Forest based feature importance method. The performance of various classifiers such as Support Vector Machine, Random Forest and Linear Discriminant Analysis has been evaluated using different feature sets and classification scenarios. The classification scenarios considered for each pesticide are 1. two class classification with control and pesticide treated samples, 2. three class classification using control samples, 1 and 2 days after pesticide application, 3. four class classification using control samples, 1, 2 and 3 days after pesticide application and 4. eight class classification with control samples and 1-7 days after application. The PCA based feature selection method was found effective among all. The two class classification scenario of control vs treated provided the best classification performance by using the SVM classifier (accuracy of 98.29%) for all the three pesticides (89.04-98.29%). The three class classification results showed that separation of pesticides with one and two days after application is possible with accuracy of 79.81-95.8%.

The Mite is one of the major sucking pests in grape which goes undetected in its initial phase as the symptoms are not easily visible to the naked eyes. In this paper, we address the problem of mites infestation detection using temporal hyperspectral data and also evaluate the potential of using Sentinel-2 data for mites infestation detection. The reflectance data from grape leaves with healthy and low infestations of mites have been collected using spectroradiometer. The hyperspectral remote sensing data is collected from 213 bands with wavelength ranging from 350 nm to 1052 nm during 15th Jan - 18th Feb 2017. Variations observed in the spectral reflectance over time makes the detection based on multitemporal data difficult. Data in 213 narrow contiguous bands is used as feature set for hyperspectral data analysis but this large feature set may cause the over-fitting problem and also poses the requirement of large storage and greater processing time. To avoid this, feature selection using Least Absolute Shrinkage and Selection Operator (LASSO) has been carried out to get the optimum band set. Features selected by LASSO were fed to classifiers such as Random Forest (RF), Artificial Neural Network (ANN) and Logistic Regression (LR) to evaluate their performance. Results suggest that LR based model provides maximum accuracy of 93.24%. In addition to this, to investigate the potential of using Sentinel-2, data in 213 narrow bands were simulated to Sentinel-2. Data has been simulated to 10 and 20m spatial resolution bands available in 350-1050nm range. This simulated 8 band feature set has been fed to the same set of classifiers to evaluate their performance. Results suggests that LR provides maximum classification accuracy of 89.12% using simulated Sentinel-2 bands. Further to validate the algorithm using actual ground observations from the field, we have implemented simulated Sentinel-2 based algorithm on two Sentinel-2 images available during the study period and results are compared with actual ground observations about mites infestation. Results suggest mites detection accuracy of 83.33% which shows the good agreement and potential of Sentinel-2 for mites infestation detection.

Climate change has huge impact on socio-economic and natural systems of semi-arid areas. Vegetation dynamics plays a crucial role in natural resources and land use planning and regional policy decisions. Increasing remote sensing platforms like satellites, airborne surveys, unmanned aerial vehicles, etc. facilitate the collection of spatiotemporal earth observations. The main objective of this study is to improve processing capabilities of proximal wireless sensing systems for crop water management in semi-arid areas. Methodology is proposed comprising of estimation of phenological stages of major Land Use / Land Cover (LU/LC) (i.e. forest, scrub forest and agriculture) in semi-arid region using multi-sensor (Landsat 7 and 8) remote sensing time series observations. A study area from semi-arid region of central India is selected to study the vegetation growth stages. In this study freely available noncommercial satellite imagery store Google Earth Engine (GEE) is used to extract the time series of remote sensing observations over the selected area. The time series of vegetation indices derived from satellite based reflectance has been analyzed to estimate the phenological stages of forest, agriculture and scrub forest. Weather variables like rainfall were used to validate the estimated vegetation growth stages. The proposed methodology attempts towards real / near real time Internet of Things (IoT) based on board sensor data processing to provide information on region specific subsistence irrigation schedules which would result into crop yield improvement.

Phythophthora disease, Gummosis, in Citrus crop results in huge yield losses every year. The purpose of this study is to detect the Gummosis disease infested fields using remote sensing and meteorological data. We present the use of participatory sensing framework to collect the reliable ground truth data about the disease incidences. Various vegetation, soil and water indices have been derived from Landsat-8 data and morphometric parameters of watershed from ASTER DEM. These parameters along with meteorological data are used as a large feature set which is then reduced using the LASSO and Elastic Net regularization techniques. We evaluate SVM, Naive Bayes and ANN based classifiers for three class classification between healthy, low and high disease infested fields. Results depicts that a combination of LASSO regularization with ANN classifier outscores the other approaches considered in this paper.