The FGVC Plant Pathology 2020 challenge dataset to classify foliar diseases of apples
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The FGVC Plant Pathology 2020 challenge dataset to classify foliar diseases of
apples
Ranjita Thapa
Plant Pathology and Plant-Microbe Biology Section, Cornell University
Geneva, NY, 14456, USA
Noah Snavely Serge Belongie
Cornell Tech Cornell Tech
2 W Loop Rd, New York, NY 10044, USA 2 W Loop Rd, New York, NY 10044, USA
Awais Khan
Plant Pathology and Plant-Microbe Biology Section, Cornell University
Geneva, NY, 14456, USA
awais.khan@cornell.edu
Abstract 1. Introduction
Apple (Malus x domestica) is one of the most impor-
Apple is one of the most economically important fruit tant temperate fruit crops in term of its value (in the US,
crops in temperate regions of the world. A large number $15 billion annually) and production. Producing apples is
of diseases and pests pose a significant threat to apple pro- costly, due to the high cost of land, labor and orchard man-
duction in the U.S and globally. Diseases and pests not only agement, and high risk, due to reliance on a few cultivars,
result in tree loss but also affect cosmetic appearance of the which increases the risk of pests and diseases. Many bac-
fruit, leading to decrease in consumer appeal. Early and terial, fungal and viral pathogens and insects pose a con-
accurate disease detection is critical for disease manage- stant threat to apple orchards in the growing season. Pre-
ment in orchards. Traditional disease and pest detection cise and early identification of diseases and pests is needed
in apple orchards relies on human scouts, and is labor in- for timely management to prevent crop loss. The most
tensive and time consuming. Recently, the growth of com- common destructive diseases of apples are fire blight, ap-
puter vision has provided new opportunities for accurate ple scab, powdery mildew, cedar apple rust, Alternaria leaf
identification of disease infections in many other crops. In spot, frogeye leaf spot, sooty blotch, flyspeck, and summer
this study we collected 3,651 high-quality, real-life RGB im- rot [8, 11] common insects are mites, aphids, codling moth,
ages with symptoms of multiple foliar diseases of apples, brown marmorated stink bug, and European Sawfly (Fig-
under variable illumination, angle, surface, and noise con- ure 1). Severe leaf infections cause chlorotic and necrotic
ditions. These images were expert-annotated to create a lesions and can result in leaf fall and even death of the tree.
pilot dataset for apple scab, cedar apple rust, and healthy The damage caused by fruit disease infection reduces their
leaves. This dataset was made available to the Kaggle com- cosmetic appearance and quality, ultimately leading to low
munity for ‘Plant Pathology Challenge’ as part of Fine- marketability and great economic losses [11]. Disease de-
Grained Visual Categorization (FGVC) workshop at CVPR tection is most commonly done by manual scouting. Dur-
2020 (Computer Vision and Pattern Recognition). The top ing the growing season, human scouts visually inspect trees
performing model so far reported an AUC (Area Under the for insects, pests, and diseases at 1-2 week intervals. Due
ROC Curve) value of 0.99. This pilot study shows that com- to large orchard sizes, high number of clients and a high
puter vision and machine learning could be promising tools cost/time analysis, scouts usually make random samplings
for disease detection and classification in apples. within an orchard block and look for specific susceptible
cultivars, or specific regions of the orchard (e.g., edges etc.).
1
Figure 1. Sample images from the dataset showing symptoms of a) cedar apple rust, b) apple scab, and effect of differences in lighting,
capturing method, age of the leaf and disease symptoms as well as host resistance response.
humidity, and the physiological developmental stage of a
plant also play crucial role on disease infection and insect
development [3, 12]. The shape and form of symptoms
also change over time as the disease progresses and leaf
or fruit tissue ages. In recent years, computer vision and
machine learning have been extensively used as promising
tools across fields of plant study. Digital imaging and ma-
chine learning can speed up plant disease diagnosis [10],
but when using computer vision as a tool for precise dis-
ease identification, all variables must be accounted for in
Figure 2. Images of disease symptoms on apple leaves captured the digital image database [1]. For example, image capture
under different light conditions (a) Indirect sunlight on leaf, (b) conditions must include multiple positions and angles of in-
Direct sunlight on leaf, and (c) Strong reflection on leaf. fected tissue in the trees, many shades of lighting, capture
sensor types, season and weather results, in addition to each
of the different diseases on fruits and leaves of various ages
Unfortunately, few experienced scouts are available, so the (Figure 2).
ratio of number of orchards to scouts is high. Also, the
random sampling strategy undertaken by scouts in a large
orchard is prone to error [6, 7, 4]. 2. Apple foliar disease symptoms
Apple scab, caused by the fungal pathogen called ‘Ven-
1.1. Problem context
turia inaequalis’, is one of the most harmful fungal diseases
Many disease symptoms in apples have similar appear- of apples in temperate regions of world [9]. The typical
ances, which leads to complexities and difficulty in identifi- symptoms of apple scab are visible fungal structures on the
cation of the actual causal pathogen [2]. At the same time, leaf and fruit surface. The initial infection appears as black
the visual symptoms of a single disease can vary across or olive-brown lesions bulging on the upper surface of leaf;
varieties due to their varying morphology e.g. leaf color, later stages show chlorotic sporulating lesions on infected
leaf shape, leaf pubescence etc. Factors like temperature, leaves. Apple scab affects not only leaves, but fruits as well,
2
and can cause premature fruit and leaf fall and deformation
of fruits. Infected fruits show dark colored, sharply bor-
dered, brown, and corky lesions. Cedar apple rust is caused
by a Basidiomycotina fungus called Gymnosporangium ya-
madai miyabe. Early symptoms of the disease are small,
light yellow spots on leaves. Later, these spots expand and
turn bright orange. The infected leaves get swollen, en-
larged, and curled at the edges and in severe cases, pre-
mature dropping of leaves occurs. Severe outbreaks of rust
pathogen for two to three years can severely injure or kill the
trees of susceptible apple varieties [5]. In natural scenario, Figure 3. Frequency distribution of entries with top auc (area
the most common condition is the appearance of multiple under the ROC curve) values from each team.
diseases in same plants which make it difficult to visually
identify the causal pathogens.
Mean AUC (area under the ROC curve) values will be used
to select the three winners with top models. A total of 1,345
3. Apple foliar disease dataset teams have participated in the competition and have sub-
In this study, we used a large, high-quality, real-life dis- mitted approximately 5,796 entries. Approximately 41% of
ease pilot dataset of multiple apple foliar diseases captured teams had top entries below 0.95 AUC, 28% teams had en-
during the 2019 growing season from commercially grown tries between 0.95 and 0.97 AUC, and 31% of teams had top
cultivars in an unsprayed apple orchard at Cornell AgriTech, entries above 0.97 AUC. More than 20 contending teams re-
Geneva, NY. Photos were taken using a Canon Rebel T5i ported an AUC value greater than 0.985 (Figure 3).
DSLR and smartphones under various conditions. A ma-
jority of the pictures are of apple scab, cedar apple rust, 5. Conclusion
Alternaria leaf blotch, frogeye leaf spot, and healthy leaves. This dataset will contribute towards pushing the state of
We have manually confirmed diseases and annotated images the art in automatic image classification for identification
of apple scab, cedar apple rust, and healthy leaves. The fi- and quantification of apple diseases from a large number
nal dataset contains a total of 3,651 RGB images of leaves of symptom classes in various real scenarios. We will con-
with 1,200 apple scab, 1,399 cedar apple rust, 187 complex tinue adding more images captured at a diverse range of an-
disease, and 865 healthy leaves, respectively. The leaves gles, lighting, and distances to our pilot dataset to build an
with complex disease patterns comprised more than one dis- even larger, more comprehensive expert-annotated dataset.
ease in the same leaf. To reflect real-world scenarios, the This will include manually capturing and annotating images
images were acquired directly from apple orchards during of symptoms on apple leaves representing apple scab, fire
the growing season under varying light/angle/surface/noise blight, powdery mildew, cedar apple rust, alternaria leaf
conditions: (1) Imbalanced dataset of disease categories, (2) blotch, frogeye leaf spot, and marssonina leaf blotch, as
Different background of images, (3) Time of the day when well as insects, including apple aphids and mites, on leaves.
the images were taken, (4) Different physiological age of We will also capture and annotate images of fruits with ap-
the plants, (5) Co-occurrence of multiple diseases on the ple scab, bitter rot, and brown rot.
same plant, and (6) Different focus of the images. The
dataset was randomly split into training and stratified test 6. Acknowledgments
set of 80% and 20%, respectively, or 2,921 training images
and 723 test images. This dataset has been made public for We acknowledge financial support from Cornell Initia-
automatic image-based disease classification and quantifi- tive for Digital Agriculture (CIDA) and special thanks to
cation to train and test computer vision models. Zach Guillian for help with data collection.
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