Site-specific management of common olive: Remote sensing, geospatial, and advanced image processing applications

Site-specific crop management (SSCM) is a part of precision agriculture which is helping increase production with minimal input. It has enhanced the cost-benefit scenario in crop production. The main goal of this paper was to use advanced geospatial techniques in data acquisition, remote sensing (RS), image processing, geographic information systems (GIS), global positioning systems (GPS) and statistical modeling to determine the correlation between image digital information and healthy olive trees growth and production management characteristics. It is to be noted that assumptions were based on the overall canopy greenness of the olive trees as healthy trees. This research was carried out during 2012–2014 in an irrigated olive orchards located in the Tarom region, Zanjan province of Iran. The following data were gathered: fruit set percent in shoot, canopy volume (CV), shoot length (SL), trunk diameter (TD), trunk height (TH), soil plant analysis development (SPAD), leaf area index (LAI), leaf dry matter percent (LDMP), leaf properties like nitrogen (N) and potassium (K) content in leaves, soil properties/characteristics like amount of Clay, Silt, Sand, Sodium adsorption rate (SAR), organic matter (OM), available phosphorous (Pav), available potassium (Kav), boron (B), total neutralizing value (TNV), electrical conductivity (EC), chloride (Cl), available iron (Feav). Advanced land observing satellite-Advanced visible and near infrared radiometer type 2 (ALOS-AVNIR-2) image was used in this experiment. A set of six clusters of olive trees existing in a compacted parcel of olive orchards were chosen. The image indices developed for this study were the normalized digital vegetation index (NDVI), newly developed vegetative vigor index (VVI) and the soil adjusted vegetation index (SAVI). Multivariate regression models were developed using remotely sensed image digital values in relation to the site specific crop growth parameters as mentioned above. As is stated above, individual band DN value statistics as input parameters and plant growth characteristics such as CV, SL, TD, TH, SPAD, LAI, and LDMP as output parameters were used in the multivariate regression models development. Multicollinearity analyses were completed on the input parameters to reduce redundancy of data usage. Multicollinearity analysis of the image related variables shows that VVI and b1 are highly correlated with other variables. It was also observed that NDVI – b3 and b2-b4 are highly correlated and hence omitted from the input parameter list. The multivariate regression models developed with NDVI and SAVI along with individual band (Green, Red and Infrared bands) as input parameters for olive crop growth parameters like TD, TH and SPAD provided excellent coefficient of determination (R2) values of 0.98, 0.99 and 0.99, respectively. SAVI, Red-, Green-, and Blue-band image information together best estimated the olive tree canopy volume with R2 value of 0.84. Similarly, SAVI, Red-, Green-, and Blue-band image information together also best estimated the olive tree SL and LA with R2 value of 0.88 and 0.96, respectively. SAVI, NDVI, Red-, Green-, and Blue-band image information as input parameters estimated the olive tree trunk diameter with R2 value of 0.98. The same SAVI, NDVI, Red-, Green-, and Blue-band image information together best predicted the olive tree trunk height with maximum correlation (R2=0.99). Similarly SPAD and LDMP were estimated with excellent correlations (R2=0.99 and 0.79, respectively) using image related input parameters of SAVI, NDVI, Red-, Green-, and Blue-band. Algorithms developed with this study could be used by farmers or orhard managers for estimating the olive tree physical characteristics in similar environmental conditions that prevailed in our study area using remotely sensed imagery in a non-invasive, economic, and efficient manner.