Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag Original papers Evaluation of Citrus Gummosis disease dynamics and predictions with weather and inversion based leaf optical model Mrunalini R. Badnakhe a, ⁎ , Surya S. Durbha a , Adinarayana Jagarlapudi a , Rajendra M. Gade b a Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, Maharashtra, India b Department of Plant Pathology, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola 444104, Maharashtra, India ARTICLE INFO Keywords: Gummosis Disease Severity Oozing scale Inverse PROSAIL model Propagule count Ground level studies ABSTRACT One of the major threats for crops around the world due to pest and diseases, which can impact the health, economy, environment, and society at large. In general, several issues related to crop yield improvement arises due to insufficient and inadequate knowledge. Therefore, there is a need to develop viable models that in- corporate various weather-soil-plant factors, which can give better understanding of the crop and enable timely interventions for yield improvement. To overcome Citrus Gummosis disease related issues and increase the Citrus productivity, seven different datasets Temperature (T), Humidity (R h ), Rainfall (R), Soil Moisture (SM), Soil Temperature (ST), Leaf Area Index (LAI) and Chlorophyll (C ab ) were used. Considering various plant, soil and environmental factors, the Citrus Gummosis prediction model has been developed with the multi-source datasets from June 2014 to November 2016 using Support vector regression (SVR) and multilinear regression (MLR). The research is carried out for healthy (5–10 Yrs. and 11–15 Yrs.) and unhealthy (5–10 Yrs. and 11–15 Yrs.) age group of plants. Inverse PROSAIL model has been simulated for retrieving citrus C ab and LAI values. These values were validated with the actual field data. Both the weather and soils based disease prediction models has been developed and validated with MLR and SVR. Further, the influence of Gummosis disease on plant parameters was also studies with the new contribution of Biophysical variables (LAI and C ab ) based sta- tistical prediction model. The SVR model gave fairly good performance as compared to MLR. In addition to the separate models a the combined scenario approach (Integrated Gummosis Disease Forecast Model: IGDFM) is designed to understand the interconnectivity of the parametric conditions (weather-soil- plant parameters) with disease physiology with respect to different age group of the plants. The RMSE of proposed approach for higher age group plants (i.e. 11–15 years) in the combined scenario was 0.9061 and 0.8518 for SVR and MLR methods, respectively. It is envisaged that this study could enable farmers to recognize and predict the timing and severity of the Gummosis disease in Citrus and thereby achieve yield improvement. 1. Introduction Agriculture is the backbone of the Indian economy as more than 50 percent of the Indian population directly or indirectly depends on agriculture for their livelihood (Naqvi and Singh, 1999; Choudhari et al., 2018). The Indian agricultural sector is immensely large and productive owing to its diversity in agro-climatic conditions. Though agriculture is the strongest source of economy the farmer’s community is however encountering tremendous hurdles in maximizing crop pro- ductivity. Various attempts have been made to understand the prime issues behind the lower crop yields. Accordingly, pest and disease modeling using different techniques (Patel et al., 2004; Matthews and Woolhouse, 2005; Cooke et al., 2006; Duveiller et al., 2012; Cunniffe et al., 2015; Kim et al., 2014) is one of the important direction to reduce the impact on crop yield (Donatelli et al., 2017). Recently, agricultural forecasting (including pest and diseases) techniques were developed using neural networks, fuzzy neural networks, support vector regres- sion, time series, RBF networks and Fuzzy Support Vector Regression (FSVR) (Leksakul et al., 2015; Shi, 2011). Further, internet-based forecasting system and decision support systems were implemented using various multi-source data to predict fruit crop diseases (Pavan et al., 2011; Small et al., 2015). The dispersal modeling and forecasted meteorology approaches were also developed for disease warnings (Garrett et al., 2006; Skelsey et al., 2009; Luo et al., 2012). The specific focus of this work is on Citrus (Mandarin Orange), and is discussed in more details below: 1. The Citrus (Mandarin Orange) is an important fruit crop in the https://doi.org/10.1016/j.compag.2018.10.009 Received 5 April 2018; Received in revised form 24 August 2018; Accepted 6 October 2018 ⁎ Corresponding author. E-mail addresses: mrunalinibadnakhe@gmail.com, mrunal.badnakhe@iitb.ac.in (M.R. Badnakhe). Computers and Electronics in Agriculture 155 (2018) 130–141 0168-1699/ © 2018 Elsevier B.V. All rights reserved. T