USA
October 4, 2016
A network of computers fed a large image dataset can learn to recognize specific plant diseases with a high degree of accuracy, potentially paving the way for field-based crop-disease identification using smartphones, according to a team of researchers at Penn State and the Swiss Federal Institute of Technology (EPFL), in Lausanne, Switzerland.
The technology could have particular benefits for producers in developing countries, such as in sub-Saharan Africa, who often do not have the research infrastructure or agricultural extension systems to support smallholder farmers, the researchers said.
"Global food security is threatened by a number of factors, not the least of which is plant diseases that can reduce yields or even wipe out a crop," said study co-author David Hughes, assistant professor of entomology and biology, College of Agricultural Sciences and Eberly College of Science, Penn State.
In addition, Hughes said, plant diseases can have disastrous consequences for smallholder farmers whose livelihoods depend on healthy crops. In the developing world, more than 80 percent of agricultural production is generated by smallholder farmers, and as many as half of hungry people live in smallholder farming households.
"Identifying a disease correctly when it first appears is a crucial step for effective disease management," he said. "With the proliferation of smart phones and recent advances in computer vision and machine learning, disease diagnosis based on automated image recognition, if technically feasible, could be made available on an unprecedented scale."
To begin to test this hypothesis, the researchers built a neural network, which is a large cluster of computers with graphical processing units. Using a deep-learning approach -- an emerging area of machine learning that uses algorithms to model high-level abstractions in data across multiple processing layers -- they fed more than 53,000 images of diseased and healthy plants into the network and trained it to recognize patterns in the data.
The research builds upon tremendous improvements in the past few years in computer vision, and object recognition in particular, said co-author Marcel Salathé, associate professor and head of the Laboratory of Digital Epidemiology, EPFL.
"Neural networks provide a mapping between an input, such as an image of a diseased plant, to an output, such as a crop-disease pair," he explained. "Deep neural networks recently have been applied successfully in many diverse domains. These networks are trained by tuning the network parameters in such a way that the mapping improves during the training process."
As an example of how this emerging technology works, Hughes cited the way Facebook can identify a user by analyzing an uploaded photo.
"These algorithms can classify complex phenotypes, such as recognizing a face," he said. "Our goal is to use them to identify plant diseases."
The images used in the study were part of a public-access archive of photographs contained in PlantVillage, a free, online plant-disease library and database Hughes and Salathé developed in 2012. The data set depicted 14 crop species -- both healthy and with disease symptoms -- and 26 diseases. Each image was assigned to one of 38 classes, each representing a crop-disease pair, and the researchers measured the performance of their model in placing images into the correct class.
"Our goal was to classify crop species and the presence and type of disease on images that the model had not seen before," said lead author Sharada Mohanty, doctoral researcher in biotechnology and bioengineering, EPFL. "Within the PlantVillage data set, the model achieved an accuracy rate as high as 99.35 percent, meaning it correctly classified crop and disease from 38 possible classes in 993 out of 1,000 images."
Mohanty noted that building the algorithms and training the model require significant computing power and time, but once the algorithms are built, the classification task itself is very fast, and the resulting code is small enough to easily be installed on a smartphone.
"This presents a clear path towards smartphone-assisted crop-disease diagnosis on a massive global scale," he said.
Hughes noted that in addition to assisting growers in developing countries, the technology has great potential in a developed-world setting. "This could be a tool for land-grant extension personnel at public institutions as they assist their grower clients, as well as for the legions of backyard gardeners who want to identify what is harming their produce," he said.
The researchers pointed out online in Frontiers in Plant Science that this approach is not intended to replace existing solutions for disease diagnosis, but rather to supplement them.
"Laboratory tests ultimately always are more reliable than diagnoses based on visual symptoms alone, and early-stage diagnosis only by visual inspection often is challenging," Hughes explained. "Nevertheless, given the expectation that more than 5 billion smartphones will be in use around the world by 2020 -- almost a billion of them in Africa -- we do believe that the approach represents a viable additional method to help prevent yield loss. With the ever improving number and quality of sensors on mobile devices, we consider it likely that highly accurate diagnoses via the smartphone are only a question of time."
PlantVillage: A deep-learning app diagnoses crop diseases
Scientists at EPFL and Penn State have trained a computer-learning algorithm to identify crop diseases with extremely high accuracy. The algorithm will be incorporated in a smartphone app to help farmers prevent future food shortages.
Crop diseases, which threaten the world’s food security, can be fought with the help of artificial intelligence systems. Scientists from EPFL and Penn State University have trained a deep-learning neural network that can accurately diagnose crop diseases by “seeing” and analyzing normal photographs of individual plants. The algorithm, which is part of the “PlantVillage” project, represents the first successful proof of concept for disease diagnosis through smartphone photos, and will be used to build an app for farmers. The work has been published in Frontiers in Plant Science.
The unprecedented growth of the world’s population means food shortage and ecosystem pressure will become global problems in the coming decades. PlantVillage, a project that employs algorithms to train computers to diagnose crop disease, is the brainchild of Marcel Salathé at EPFL and David Hughes at Penn State. The algorithm development itself is led by computer scientist Sharada P. Mohanty, a PhD student in Salathé’s Laboratory of Digital Epidemiology.
The project benefits from the progress that has been made in the field of “deep learning” in recent years. Deep learning is a type of machine learning that uses algorithms to find patterns in big sets of data – in this case, over 50,000 digital photographs of diseased plants, made openly available by PlantVillage. Through a computational neural network, the system processes the photographs through multiple layers of artificial neurons, and so gradually “learns” to identify different diseases with high degrees of certainty.
The goal is to put the tool in the hands of farmers, agriculturists, and everyday gardeners in the form of a smartphone app. “People will be able to snap a photograph of their sick plant with the app and get a diagnosis within seconds,” says Salathé.
To enable anyone in the world to develop such algorithms, the scientists made their database of over 50,000 photographs openly available in 2015. The current paper demonstrates their deep-learning algorithm at work: The researchers assigned every one of 54,306 photographs of diseased and healthy plant leaves to one of 38 classes of crop-disease pairs (e.g. Tomato plant-Tomato Early Blight, Apple tree – Apple scab etc).
They then trained their “deep convolutional neural network” to identify plants and diseases (or lack thereof for healthy plants), and they measured how accurately it could assign each image to the correct class. In total, working with 14 crop species and 26 different plant diseases, the system could identify diseases on images it had never seen before with an accuracy of 99.35%.
Building the algorithm and training the model require significant computing power and time, but once trained, the classification task itself is very fast, and the resulting code is small enough to be easily installed on a smartphone. "This presents a clear path towards smartphone-assisted crop-disease diagnosis on a massive global scale,” says lead author Sharada Mohanty.
However, these photographs were taken under controlled conditions of lighting, color etc, which don’t always correspond to a snapshot taken in a field. To address this, the team is now expanding their database of images to about 150,000 in order to improve the system’s ability to identify diseases. In addition, they are planning to also expand the amount of data that the network will use to make accurate diagnoses.
“At this point, we’re relying on a photograph taken by a user in a field under natural conditions,” explains Salathé. “But in the future, we would like to also bring in time, location, epidemiological trends, weather conditions and other signals to bear upon the network, which would vastly improve its abilities.” Although this system aims to supplement rather than replace existing diagnostic methods, the fact that there will be over 5 billion smartphones around the world by 2020 will be a tremendous advantage.
“We do believe that the approach represents a viable additional method to help prevent yield loss," says David Hughes. "With the ever-improving number and quality of sensors on mobile devices, we consider it likely that highly accurate diagnoses via the smartphone are only a question of time."
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