Hyperspectral Vegetation Indices for Arecanut Crop Monitoring

Abstract

Arecanut (Areca catechu L.) is one of the major profitable plantation crop grown in few regions of the World. Karnataka state in India produces almost half of the world’s total production, in that contribution from Shivamogga district and Coastal Karnataka is significant. The production per unit area in Karnataka is considerably less. The major reasons may be improper irrigation practices, poor soil maintenance, lack of technical knowledge on irrigation water quality, quantity, fertilizers used and frequent occurrence of diseases, small size and spatially scattered farms. These reasons were very typical in Chennagiri region of Karnataka. Farmers’ practice adding tank silt lifted from nearby tanks to their farms followed by drip irrigation in the form of flooding. In this region a typical disorder called crown choke harmed an adult plant’s life. The objective of this research is: to explore the potential of advanced tools for Arecanut crop monitoring and to demonstrate it on portion of Chennagiri region of Karnataka. Advanced technological tools used include GPS, Hyperspectral remote sensing data and GIS. Hyperspectral remote sensing is one of the fastest growing techniques in the field of remote sensing due to its vast applications with improved accuracy over conventional method. Spectral library was built separately for different age group and stressed crops using spectroradiometer. Care was taken to match field data with the Hyperion data acquisition time. Hyperion hyperspectral data was classified into stressed versus healthy and different age group crops using developed spectral library. Stressed versus healthy crop classification revealed 10% crops were under stress in patches. To find a scientific reason for crown choke disease affected crops inflated in study area, grid wise soil and water samples were collected, and subjected to standard physico-chemical analysis. Potential evapotranspiration (ETo) was computed using Normalized Difference Vegetation Index (NDVI) based crop coefficient (Kc) method due to non-availability of weather parameters. ETo, Integrated with Hargreaves Samani method was adopted to compute the crop water requirement of different age crops. Narrow bands in hyperspectral data facilitate computation of several spectral indices and can facilitate improved classification accuracy. Indices developed being Disease Index (DI) to identify disease severity in Arecanut crop, Age Index (AI) to segregate the Arecanut crops into different age groups and Arecanut Crop Water Requirement Index (ACWRI) was built to compute age based crop water requirement.ii Important wavelengths were identified among the hundreds of bands to compute the crop water requirement using statistical techniques. Stepwise Multi Linear Regression (SMLR), Partial Least Square Regression (PLSR), and Variable Importance for Projection (VIP) were the techniques of choice. These techniques also facilitated construction of simple models to predict the Arecanut crop water requirement. On the basis of diseased v/s healthy crop classification, it was inferred that more than 10% of plantation under study was affected by crown choke disease. The physico-chemical analysis revealed that improper soil management is the main cause for crown choke disorder. Soil characterization and water quality analysis infers soil is poorly graded (82% of silt content) with very low hydraulic conductivity of 3.2×10-7 cm/sec, and high bulk density of 2.12 g/cm3. This impervious nature caused water logging and lead to salinity. Age based classification results revealed Arecanut crop can be classified into different age groups; below 3 years, 5 to 7 years, 8 to 15 years and above 25 years. And within class classification accuracy of 72% was observed for Support Vector Machine (SVM) classification with linear kernel. Age based Arecanut crop water requirement map reveals that crop water requirement varies with age of the crop, below 7 years of crop it is 19 and for above 15 years it is 25 liter/day/plant. The derived ACWRI, DI, AI indices to monitor Arecanut crop ranges from 0 to 1 to indicate the age based crop water requirement, disease severity, and age of crop respectively. From the hyperspectral data significant wavelengths were identified: (i) to map the stressed Arecanut crops (750, 550 and 675nm), (ii) Arecanut crop age predication (540, 680 and 780nm). (iii) And to predict the age wise crop water requirement using statistical models: SMLR revealed that 681 and 721nm are significant. PLSR also in agreement with SMLR i.e 681,721 and 548nm are important. Whereas a VIP technique revealed wavelengths 1043, 1053, 1033, 1083, 1023, 1013, 1104, and 854nm are important. This study concludes that, hyperspectral remote sensing data processed with standard procedures with appropriate atmospheric corrections algorithms and integrated with field studies along with statistical models can be effectively used for Arecanut crop monitoring. This study also demonstrates that, how advanced technological tools can be used to address societal problems say crop monitoring. The output of the research is useful to the farming community to actively plan their agriculture water requirement, and also improves water use efficiency.