Automated identification of sugar beet diseases using smartphones

Abstract

Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

“…PocketPlant3D measures maize canopy structure (Confalonieri et al, 2017); the smartphone is moved parallel with the leaf lamina from base to tip, and all the leaves can be scanned to obtain the whole leaf architecture. A machine learning-based app can diagnose Cercospora leaf spot and other sugar beet leaf diseases better than experts (Hallau et al, 2018). A smartphone application for Android devices, vitisBerry, can quantify the number of grapevine berries (Aquino et al, 2018).…”

High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

“…The workflow presented should be applicable to a high-throughput phenotyping platform, such as an UAV. Hallau et al [88] presented a smartphone application that was able to detect and distinguish several leaf diseases, including Cercospora leaf spot, beet rust and bacterial blight in sugar beet using pictures taken by a smartphone. The machine learning algorithm had an accuracy of differentiating between these diseases of 82%, which was better than the accuracy of experts in this topic.…”

A Practical Plant Diagnosis System for Field Leaf Images and Feature Visualization

“…In such circumstances, methodologies for automated plant diagnosis characterized by accuracy, speed and low costs have been requested by the agricultural industry. Several studies have been carried out in response to such requests [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. In [4] used support vector machines (SVM) to classify rice plant diseases and attained 92.7% accuracy.…”

“…They claimed 99.98% and nearly 99% accuracy in disease severity estimation and classification, respectively. In [18] investigated six kinds of Cercospora leaf spots of sugar cane with an evaluation of common statistical and handmade image features. Their method attained 82% accuracy.…”