Abstract
Abstract. Stress in the crop not only decreases the production but can also have devastating consequences for farmers whose life depends upon the healthy crops. In recent time (January 2018) a such abiotic stress event (hoar frost) was experienced at ICAR research complex experimental filed, Ri-Bhoi district of Meghalaya on standing Maize crop. Therefore, remote sensing (Multispectral UAV- Unmanned Aerial Vehicle) technology were used to detect the effect of frost on in-filed Maize crop. Two set of multispectral data (before frost and after frost) with four advanced machine learning techniques viz. Random Forest (RF), Random Committee (RC), Support Vector Machine (SVM) and Artificial Neural Network were employed for detection of stress free crop and stressed crop due to frost. Results revealed that all the four methods of classification could able to identify / detect stress-free vs. stressed crops at satisfactory level. However, among the classifiers RF achieved relatively higher overall accuracy (OA = 86.47%) with Kappa Indexanalysis (KIA = 0.80) and found very cost effective in context of computational cost (time complexity = 0.08 Seconds) to train the model. In addition, we have also recorded the area of each classes and found that after frost stress-free area (36.01% of all over filed) is decreased by 11% in comparison of before frost (25.036% of all over filed). Based on the results we can suggest that the RF ensemble classification method can be used for further other crop classification in order to estimate the yield, detect the condition, monitoring the health etc.
Highlights
Crop growth sensitivity to temperature, global increase and higher extremes of temperature represent a threat to crops and may cause yield changes and production losses (Ruiz-Ramos et al, 2011)
Four channels multispectral unmanned aerial vehicles (UAV) data were acquired from the experimental field on 80-meter height
As described in earlier sections four advanced different machine learning techniques were employed to found the effect of frost on Maize crop using classification
Summary
Crop growth sensitivity to temperature, global increase and higher extremes of temperature represent a threat to crops and may cause yield changes and production losses (Ruiz-Ramos et al, 2011). Weather related risk in agriculture has get great attention of researchers and practitioners in last two decades as the occurrence of uncertain extreme climatic events (drought, flood, hailstorm, and frost etc.) increasing rapidly (Berthet et al, 2011; Changnon et al, 2009; Gobin et al, 2012 ;SaaRequejo et al, 2011). Frost is such unpredictable event that can occur at any point of time and causes partial and full damage to the crop (GOBIN et al, 2013). Rapid estimation of frost damage to crops on a spatial basis would allow for timely management decisions to reduce the economic impact of frost events (Perry at al., 2017)
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