Thermal imaging for identification of malfunctions in subsurface drip irrigation in orchards

Abstract

Leaks and clogs in drip-irrigated orchards lead to variable yields, reduced efficiency and profitability. Frequent monitoring of irrigation systems by farmers is important but costly, labor-intensive, and not easily implementable on a regular basis. Moreover, in subsurface drip-irrigation systems, it is difficult to visually detect malfunctions. The objective of this study was to develop processing methodologies based on thermal remote sensing, to produce classification models for detecting irrigation malfunctions in orchards, and distinguish between different types of malfunctions. A thermal camera mounted on an unmanned aerial vehicle platform was used to acquire thermal images in three commercial almond and jojoba plantations with subsurface drip irrigation. An image-processing pipeline was developed to extract plant-specific features, and classification models were used to detect malfunctions in individual plants. Plants were segmented using four algorithms: Otsu, continuous max-flow and min-cut, full-width-half-max, and watershed. Thirty-two features were extracted from the canopy temperature of each plant and normalized with meteorological data. The most significant features were selected using a recursive feature elimination method. Three classification models (multiclass, binary, hierarchical) were constructed using five classification algorithms. Performance was evaluated with k-fold cross-validation and an independent test set. In the almond plants orchard, the hierarchical classification approach with support vector machine (SVM) algorithms yielded 68% accuracy and 33% false-positive rate (FPR) for clog detection and 2.8% FPR for leak detection. In the jojoba plantation, the multiclass classification approach with SVM algorithms gave 82% accuracy for clog and leak detection with 0% FPR.

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