Distributed Deep Learning for Persistent Monitoring of agricultural Fields
Abstract
Distributed deep learning algorithms have shown eminent performance in learningfrom data that are privately allocated between several agents.
Recent advances insensor technology have enabled the cheap collection of spatial and temporal highresolution data for agriculture across a wide geographical area.
This continuousincrease in the amount of data collected has created both the opportunity for, aswell as the need to deploy distributed deep learning algorithms for a wide varietyof decision support tasks in agriculture.
Distributed deep learning algorithms aretypically divided into two major categories: centralized vs decentralized learningalgorithms, depending on whether a central parameter server exists for gatheringinformation from participating agents.
In the case of rural agriculture applications,transferring a large amount of high-resolution data (e.g., images, videos) collectedwith IoT devices to a central server/cloud could be very expensive especially withlimited communication infrastructure.
This suggests the need for decentralizedlearning approaches, which also naturally provide some measure of privacy.
Here,autoencoders are trained using a decentralized optimization algorithm to createa latent representation of growing maize plants in a large-scale ﬁeld experimentinvolving several hundred cameras deployed in a maize genome diversity growthexperiment.
We trained the autoencoders for different communication networktopologies of the ﬁeld-deployed cameras.
The feature representations from theseautoencoders are then utilized to solve downstream tasks such as anomaly detectionand image retrieval.
Experimental results show that distributed deep learningis effective in learning from large datasets distributed among several learningagents associated with different cameras.
Anomaly detection in particular wasuseful to make course corrections in imaging protocol and identify localized cropmanagement.
Introduction
Recent dramatic advances in sensor technology have enabled remote (drone, ground) as well asproximal (soil sensors, touch sensors) data acquisition that can target a large range of features(coarse vs. ﬁne spatial resolution, high vs. low temporal frequency, visible vs. hyperspectralwavelengths, chemical vs. physiological attributes) for different decision support tasks in agriculture.
Distributed AI-enabled advances are helpful to extract informative agronomic and physiological traitsfrom the large amounts of raw sensor data collected from agricultural ﬁelds.
Typically distributeddeep learning algorithms are divided into two main categories; centralized McMahan et al.
Kairouz et al.
and decentralized Lian et al.
[2017], Nedi´c et al.
[2018] learning dependingon whether a central parameter server is participating in the learning process Tang et al.
Centralized learning refers to the class of algorithms which generally contain a parameter serverthat aggregates information i.e. gradients, model parameters, etc.
from the participating agents andperforms parameter updates.
On the other hand, in decentralized learning, agents communicatebased on some graph topologies to update parameters Lyu et al.
However, in agriculture applications, transferring a large amount of data (e.g., images, videos) which are collected with IoTdevices to a central server/cloud is computationally expensive.
Additionally, the central parameterserver can act as a single point of failure.
Therefore, decentralized deep learning algorithms Kempeet al.
[2003], Xiao and Boyd [2004], Boyd et al. [2006], Dekel et al.
[2012], Lian et al.
[2017], Jianget al.
[2017], Yu et al.
[2019], Koloskova et al.
[2019], Nadiradze et al.
[2019], Balu et al.
Esfandiari et al.
[2021] appear to act as a better alternative.
As a result, we consider DPMSGD Lianet al.
[2017] which is a decentralized learning algorithm as the method to train deep learning modelsin a distributed fashion.
Identiﬁcation of inputs that lie far away from the training distribution (in-distribution) is called Out-ofdistribution (OOD) detection, anomaly detection or outlier detection Grubbs [1969].
OOD detectionhas been used in broad range of safety-critical applications including medical diagnosis Caruana et al.[2015], autonomous driving Evtimov et al.
[2017], cyber-security Kruegel and Vigna [2003], andbiometric authentication Gunther et al.
In this paper, we deal with the challenging scenario offully unsupervised outlier detection where the goal is the goal is to detect outliers from data containingboth normal and outlier patterns.
We are using LOF Breunig et al.
[2000] and cluster-conditioneddetection Sehwag et al.
[2021] methods for performing anomaly detection using the autoencoderswhich are trained in a distributed fashion.
Content-based image retrieval (CBIR) technique Knorret al.
[2000] is the computer vision based process of retrieving images that are similar to visualcontent of a query image from an extensive archive.
In the case of anomaly detection, the queryimage would be a anomaly example.
Early research for CBIR considered the global (shape, color,and texture) and local descriptors Mikolajczyk and Schmid [2005], Arampatzis et al.
[2013] of animage as a feature vector to perform the retrieval procedure Flickner et al.
[1995], Huang et al.
Recently, representations from deep learning models have been highly efﬁcient for image retrievaltasks Chen et al.
In this work, we use the nearest neighbors algorithm on the features fromthe CNN model for the image retrieval task.
Contributions : Speciﬁcally, in this paper (i) the use of distributed learning algorithms to train modelsfrom real-world agricultural datasets is investigated.
(ii) the efﬁcacy of the autoencoder models areshown, (iii) downstream tasks such as anomaly detection, and image retrieval using these models areperformed followed by the discussion on the results and future work. [Figure]
Figure 1 shows the different algorithms used in the training and inference pipeline.
Distributed Deep
Learning : As discussed before, there are several methods for decentralized distributed learningamong them
DPMSGD Lian et al.
[2017] has shown to produce prominent results in learning from
IID data distributions.
In this algorithm, each agent is assigned a model for its portion of the data set.
After each agent has computed a local stochastic gradient, it then fetches the optimization variablesfrom neighbors and calculates the neighborhood weighted average.
The local optimization variablesare then updated using the neighborhood weighted average and the gradients.
The consensus modelis then achieved by averaging all the local model parameters.
Anomaly Detection : Local Outlier Factor (LOF) is a density-based unsupervised outlier detection algorithm that detects outliers bycalculating the local deviation of a given data point Breunig et al.
The points that containssubstantially lower density than its neighbors are considered outliers.
We used euclidean distance foridentifying the nearest neighbors and used 20 nearest neighbor points to calculate the density of a data point.
Cluster-conditioned detection Sehwag et al. [2021] method uses the feature representationslearnt by the autoencoder for the outlier detection.
We assume that majority of the samples in thedata are inliers.
Then we calculate the centroid of the feature representations of the complete datato represent the inlier data.
Then, we use the Mahalanobis Mahalanobis [1936] distance metric toidentify the outlier data points that are farthest away from the centroid feature representation.
Image Retrieval : In the ﬁrst step, feature maps of the dataset and query image are extracted using the trained CNN model.
Then, we use the ball tree method Andoni and Indyk [2017] based nearest neighborssearch on the feature maps to identify the k nearest neighbors of the query feature.
Experimental Setup and Results
We empirically evaluate the effectiveness of our framework by using the trained model to performdownstream tasks such as anomaly detection and image retrieval on real-world agricultural dataset.
To explore the algorithm performance under different topologies, the experiments are performedusing 10 learning agents.
The agricultural dataset consists of images of 655 rows of ﬁeld-grown Maizeplants.
Figure 2a shows the imaging setup of the ﬁeld-deployed stationary camera.
The imageswere collected at an interval of 20 minutes from 8:00 AM to 5:00 PM over a period of two and halfmonths in 2019 in the midwestern United States.
Each row consists of 6 maize plants of a speciﬁcgenotype.
These ﬁeld photos of maize plants are high-resolution 5152 × 3864 RGB images.
Astationary camera was used to collect data from each row separately and a total of 655 cameras wereused.
In our experiments, data from a diverse subset of 30 cameras are used to create a dataset of74 k images.
The images were resized to 128 × 128 pixels each for computational efﬁciency andthe data collection process is described thoroughly elsewhere.
We use a deep convolutional neuralnetwork (CNN) autoencoder (with 3 convolutional layers with 12 , 24 , 48 ﬁlters in the encoder sectionand 3 layers with 24 , 12 , 3 ﬁlters for the decoder, ReLU activation is used in convolutional layers).
A mini-batch size of 128 is used, the initial step-size is set to 0 .
The step size is decayed with aconstant 0 . 981 .
The stopping criterion is a ﬁxed number of epochs and the momentum parameter( β ) is set to be 0.98.
We start our analysis in learning real-world agricultural datasets.
In theseexperiments, data from 30 cameras are divided between 10 learning agents so each agent has accessto data from 3 distinct cameras.
Figure 2b shows that DPMSGD is converging for the agriculturaldataset for all the three graph topologies.
We then compare the original images in the dataset withtheir reconstructed counterparts to further analyze the model.
Figure 3 shows that the autoencoder ismoderately effective in reconstructing the images.
[Figure]
The trained autoencoder model is then used to perform anomaly detection and image retrievaltasks.
Figure 4 shows the anomaly predictions of cluster-conditioned detection and LOF methods.
Identiﬁcation of anomalies was particularly useful in making course corrections to imaging protocolsof ﬁeld-deployed stationary cameras.
We then use the same model to perform image retrieval.
Figure 5 shows some examples of image retrieval results by the nearest neighbors algorithm.
Theseresults show that the models trained in decentralized distributed fashion are effective to do imageretrieval tasks on diverse images (normal weather, rainy weather, or from tilted cameras).
[Figure] [Figure]
Conclusion
[Figure] In this paper, the viability of distributed learning to train autoencoders which learn from real-worldagricultural datasets gathered from ﬁeld-deployed stationary cameras was discussed.
Experimentsconducted on this dataset showed that our proposed framework is effective to perform anomalydetection and image retrieval tasks.
Future work includes: comparing different distributed learningalgorithms, proposing graph topologies to optimize the communications between the cameras, andconducting experiments using larger datasets.