Self-supervised learning improves classification of agriculturally important insect pests in plants

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Kar, Soumyashree
Nagasubramanian, Koushik
Elango, Dinakaran
Carroll, Matthew E.
Abel, Craig A.
Nair, Ajay
Mueller, Daren S.
Sarkar, Soumik
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O'Neal, Matthew
Singh, Asheesh
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The Department of Agronomy seeks to teach the study of the farm-field, its crops, and its science and management. It originally consisted of three sub-departments to do this: Soils, Farm-Crops, and Agricultural Engineering (which became its own department in 1907). Today, the department teaches crop sciences and breeding, soil sciences, meteorology, agroecology, and biotechnology.

The Department of Agronomy was formed in 1902. From 1917 to 1935 it was known as the Department of Farm Crops and Soils.

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  • Department of Farm Crops and Soils (1917–1935)

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Mechanical Engineering
The Department of Mechanical Engineering at Iowa State University is where innovation thrives and the impossible is made possible. This is where your passion for problem-solving and hands-on learning can make a real difference in our world. Whether you’re helping improve the environment, creating safer automobiles, or advancing medical technologies, and athletic performance, the Department of Mechanical Engineering gives you the tools and talent to blaze your own trail to an amazing career.
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The Department of Horticulture was originally concerned with landscaping, garden management and marketing, and fruit production and marketing. Today, it focuses on fruit and vegetable production; landscape design and installation; and golf-course design and management.
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Plant Pathology, Entomology and Microbiology
The Department of Plant Pathology and Microbiology and the Department of Entomology officially merged as of September 1, 2022. The new department is known as the Department of Plant Pathology, Entomology, and Microbiology (PPEM). The overall mission of the Department is to benefit society through research, teaching, and extension activities that improve pest management and prevent disease. Collectively, the Department consists of about 100 faculty, staff, and students who are engaged in research, teaching, and extension activities that are central to the mission of the College of Agriculture and Life Sciences. The Department possesses state-of-the-art research and teaching facilities in the Advanced Research and Teaching Building and in Science II. In addition, research and extension activities are performed off-campus at the Field Extension Education Laboratory, the Horticulture Station, the Agriculture Engineering/Agronomy Farm, and several Research and Demonstration Farms located around the state. Furthermore, the Department houses the Plant and Insect Diagnostic Clinic, the Iowa Soybean Research Center, the Insect Zoo, and BugGuide. Several USDA-ARS scientists are also affiliated with the Department.
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Insect pests cause significant damage to food production, so early detection and efficient mitigation strategies are crucial. There is a continual shift toward machine learning (ML)-based approaches for automating agricultural pest detection. Although supervised learning has achieved remarkable progress in this regard, it is impeded by the need for significant expert involvement in labeling the data used for model training. This makes real-world applications tedious and oftentimes infeasible. Recently, self-supervised learning (SSL) approaches have provided a viable alternative to training ML models with minimal annotations. Here, we present an SSL approach to classify 22 insect pests. The framework was assessed on raw and segmented field-captured images using three different SSL methods, Nearest Neighbor Contrastive Learning of Visual Representations (NNCLR), Bootstrap Your Own Latent, and Barlow Twins. SSL pre-training was done on ResNet-18 and ResNet-50 models using all three SSL methods on the original RGB images and foreground segmented images. The performance of SSL pre-training methods was evaluated using linear probing of SSL representations and end-to-end fine-tuning approaches. The SSL-pre-trained convolutional neural network models were able to perform annotation-efficient classification. NNCLR was the best performing SSL method for both linear and full model fine-tuning. With just 5% annotated images, transfer learning with ImageNet initialization obtained 74% accuracy, whereas NNCLR achieved an improved classification accuracy of 79% for end-to-end fine-tuning. Models created using SSL pre-training consistently performed better, especially under very low annotation, and were robust to object class imbalances. These approaches help overcome annotation bottlenecks and are resource efficient.
This article is published as Kar, S., Nagasubramanian, K., Elango, D., Carroll, M. E., Abel, C. A., Nair, A., Mueller, D. S., O’Neal, M. E., Singh, A. K., Sarkar, S., Ganapathysubramanian, B., & Singh, A. (2023). Self-supervised learning improves classification of agriculturally important insect pests in plants. The Plant Phenome Journal, 6, e20079.

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