Pesticide residues in the fruits, vegetables and agricultural commodities are harmful to humans and are becoming a health concern nowadays. Detection of pesticide residues on various commodities in an open environment is a challenging task. Hyperspectral sensing is one of the recent technologies used to detect the pesticide residues. This paper addresses the problem of detection of pesticide residues of Cyantraniliprole on grapes in open fields using multi temporal hyperspectral remote sensing data. The re ectance data of 686 samples of grapes with no, single and double dose application of Cyantraniliprole has been collected by handheld spectroradiometer (MS- 720) with a wavelength ranging from 350 nm to 1052 nm. The data collection was carried out over a large feature set of 213 spectral bands during the period of March to May 2015. This large feature set may cause model over-fitting problem as well as increase the computational time, so in order to get the most relevant features, various feature selection techniques viz Principle Component Analysis (PCA), LASSO and Elastic Net regularization have been used. Using this selected features, we evaluate the performance of various classifiers such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to classify the grape sample with no, single or double application of Cyantraniliprole. The key finding of this paper is; most of the features selected by the LASSO varies between 350-373nm and 940-990nm consistently for all days. Experimental results also shows that, by using the relevant features selected by LASSO, SVM performs better with average prediction accuracy of 91.98 % among all classifiers, for all days.
In this paper, we envision the use of satellite images coupled with GIS to obtain location specific crop type
information in order to disseminate crop specific advises to the farmers. In our ongoing mKRISHI R
project, the
accurate information about the field level crop type and acreage will help in the agro-advisory services and supply
chain planning and management. The key contribution of this paper is the field level crop classification using
multi temporal images of Landsat-8 acquired during November 2013 to April 2014. The study area chosen is Vani,
Maharashtra, India, from where the field level ground truth information for various crops such as grape, wheat,
onion, soybean, tomato, along with fodder and fallow fields has been collected using the mobile application. The
ground truth information includes crop type, crop stage and GPS location for 104 farms in the study area with
approximate area of 42 hectares. The seven multi-temporal images of the Landsat-8 were used to compute the
vegetation indices namely: Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Difference
Vegetation Index (DVI) for the study area. The vegetation indices values of the pixels within a field were then
averaged to obtain the field level vegetation indices. For each crop, binary classification has been carried out
using the feed forward neural network operating on the field level vegetation indices. The classification accuracy
for the individual crop was in the range of 74.5% to 97.5% and the overall classification accuracy was found to
be 88.49%.
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