A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery

Abstract

In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

“…As researchers tend to use data augmentation methods for small datasets, these methods are not always efficient and cannot exceed a certain threshold to avoid overfitting. Once the dataset is available, it can suffer from imbalance, where samples of healthy plants are more important than samples of diseased plants, as well as seasonal and regional difficulties with various categories of crop diseases [95,111].…”

“…Deep learning has proven its high performance for disease detection also using satellite images. In [95], the authors proposed a gated recurrent unit (GRU)-based model to predict development of sudden death syndrome (SDS) disease in soybean quadrats. Twelve PlanetScope satellite images were conducted in this study.…”

Intelligent Deep Learning Based Predictive Model for Coronary Heart Disease and Chronic Kidney Disease on People With Diabetes Mellitus

“…In contrast, the gradient would become larger when the gradient is greater than 1. Thus, sometimes it reasons the gradient for developing nearly zero or larger once it gains the first layer of RNN [19]. Therefore, the weight of the first layer won't obtain upgraded in the training phase.…”

“…𝑋 𝑖 + 𝛽 × (𝑋 𝑖 − 𝑋 𝑗 ) + 𝑟 4 × 𝜀 𝑖(19) Whereas 𝑟 4 ∈ [0,1] represent an arbitrary number and 𝜀 𝑖 ∈ 𝑁(𝜇, 𝜎) represent an arbitrary vector. The fundamental step of the FA is shown in Algorithm 2.Algorithm 1: Firefly algorithm Create a collection of 𝑁 fireflies 𝑋 𝑖 (𝑖 = 1, 2, … , 𝑁).…”

Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing

“…Fewer studies on RFS have been based on near earth remote sensing. A large number of researchers have emphasized other crop diseases, such as satellite remote sensing for wheat Fusarium head blight [8], soybean sudden death syndrome [9], tobacco crop [10], rice bacterial leaf blight [11], soybean sudden death syndrome [12], near earth remote sensing for cucumber leaves in response to angular leaf spot disease [13], early disease in wheat fields [14], watermelon disease detection [15], rye leaf rust symptoms [16], paddy leaf disease [17], onion purple blotch [18], etc.…”