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Remote sensing and machine learning (ML) could assist and support growers, stakeholders, and plant pathologists determine plant diseases resulting from viral, bacterial, and fungal infections. Spectral vegetation indices (VIs) have shown to be helpful for the indirect detection of plant diseases. The purpose of this study was to utilize ML models and identify VIs for the detection of downy mildew (DM) disease in watermelon in several disease severity (DS) stages, including low, medium (levels 1 and 2), high, and very high. Hyperspectral images of leaves were collected in the laboratory by a benchtop system (380–1,000 nm) and in the field by a UAV-based imaging system (380–1,000 nm). Two classification methods, multilayer perceptron (MLP) and decision tree (DT), were implemented to distinguish between healthy and DM-affected plants. The best classification rates were recorded by the MLP method; however, only 62.3% accuracy was observed at low disease severity. The classification accuracy increased when the disease severity increased (e.g., 86–90% for the laboratory analysis and 69–91% for the field analysis). The best wavelengths to differentiate between the DS stages were selected in the band of 531 nm, and 700–900 nm. The most significant VIs for DS detection were the chlorophyll green (Cl green), photochemical reflectance index (PRI), normalized phaeophytinization index (NPQI) for laboratory analysis, and the ratio analysis of reflectance spectral chlorophyll-a, b, and c (RARSa, RASRb, and RARSc) and the Cl green in the field analysis. Spectral VIs and ML could enhance disease detection and monitoring for precision agriculture applications. [ABSTRACT FROM AUTHOR]

In this study hyperspectral imaging (380–1020 nm) and machine learning were utilised to develop a technique for detecting different disease development stages (asymptomatic, early, intermediate, and late disease stage) of powdery mildew (PM) in squash. Data were collected in the laboratory as well as in the field using an unmanned aerial vehicle (UAV). Radial basis function (RBF) was used to discriminate between healthy and diseased plants, and to classify the severity level (disease stage) of a plant; the most significant bands to differentiate between healthy and different stages of disease development were selected (388 nm, 591 nm, 646 nm, 975 nm, and 1012 nm). Furthermore, 29 spectral vegetation indices (VIs) were tested and evaluated for their ability to detect and classify healthy and PM-infected plants; the M value was used to evaluate the VIs. The water index (WI) and the photochemical reflectance index (PRI) were able to accurately detect and classify PM in asymptomatic, early, and late development stages under laboratory conditions. Under field conditions (UAV-based), the spectral ratio of 761 (SR761) accurately detected PM in early stages, and the chlorophyll index green (CI green), the normalised difference of 750/705 (ND 750/705), the green normalised difference vegetation index (GNDVI), and the spectral ratio of 850 (SR850) in late stages. The classification results, by using RBF, in laboratory conditions for the asymptomatic and late stage was 82% and 99% respectively, while in field conditions it was 89% and 96% in early and late disease development stages, respectively. • A lab- and UAV-based powdery mildew (PM) disease detection system was developed. • Spectral reflectance analysis of PM-infected squash leaves was performed. • The most significant wavelengths and vegetation indices were selected. • The technique was able to detect different stages of PM progress in squash. [ABSTRACT FROM AUTHOR]

Highlights • A remote sensing technique for Laurel Wilt detection in avocado was developed. • This low-cost technique can distinguish Laurel Wilt disease from other stressors. • Neural networks were utilizing for disease detection and classification. • Early disease detection was achieved utilizing multispectral imaging. Abstract Early and accurate disease detection is essential for implementing timely disease management practices. Current disease detection tactics, like visual detection through scouting, are labor intensive, expensive, requires a level of expertise in pest identification, and, may result in subjective disease identification. Diagnosis based on visual symptoms is often compromised by the inability to differentiate between similar symptoms caused by different biotic and abiotic factors. In this paper, an automated early disease detection technique for avocado trees is presented and evaluated. This remote sensing technique can detect an important avocado disease, the laurel wilt (Lw) disease, and differentiate it from healthy trees (H), trees infected by phytophthora root rot (Prr), and trees with iron (Fe) and nitrogen (N) deficiencies. Detection of Lw disease in avocado trees, in early stage, is very difficult, because it has similar symptoms with other stress factors such as nutrient deficiency, salt damage, phytophthora root rot, etc. The proposed disease detection procedure contains several steps including image acquisition, image pre-processing, image segmentation, feature extraction and classification. For image acquisition, two cameras were utilized and evaluated: (i) a Tetracamera (6 bands Tetracam) and (ii) a modified Canon camera (3 bands); and two classification methods were studied: (a) neural network multilayer perceptron (MLP), and (ii) K- nearest neighbors, to detect Lw in asymptomatic stage and in late (symptomatic) stage. Additionally, two segmentation methods, region of interest (OVROI) and polygon region of interest (PROI), were utilized. The MLP classification method with the Tetracam was able to successfully detect Lw with an accuracy of 99% in asymptomatic (early) stage. Hence, low-cost remote technique can be utilized to differentiate healthy and unhealthy plants. [ABSTRACT FROM AUTHOR]

A remote sensing technique was developed to detect citrus canker in laboratory conditions and was verified in the grove by utilizing an unmanned aerial vehicle (UAV). In the laboratory, a hyperspectral (400–1000 nm) imaging system was utilized for the detection of citrus canker in several disease development stages (i.e., asymptomatic, early, and late symptoms) on Sugar Belle leaves and immature (green) fruit by using two classification methods: (i) radial basis function (RBF) and (ii) K nearest neighbor (KNN). The same imaging system mounted on an UAV was used to detect citrus canker on tree canopies in the orchard. The overall classification accuracy of the RBF was higher (94%, 96%, and 100%) than the KNN method (94%, 95%, and 96%) for detecting canker in leaves. Among the 31 studied vegetation indices, the water index (WI) and the Modified Chlorophyll Absorption in Reflectance Index (ARI and TCARI 1) more accurately detected canker in laboratory and in orchard conditions, respectively. Immature fruit was not a reliable tissue for early detection of canker. However, the proposed technique successfully distinguished the late stage canker-infected fruit with 92% classification accuracy. The UAV-based technique achieved 100% classification accuracy for identifying healthy and canker-infected trees. [ABSTRACT FROM AUTHOR]