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1. Self-supervised maize kernel classification and segmentation for embryo identification

   https://doi.org/10.3389/fpls.2023.1108355  

   Dong, David; Nagasubramanian, Koushik; Wang, Ruidong; Frei, Ursula K.; Jubery, Talukder Z.; Lübberstedt, Thomas; Ganapathysubramanian, Baskar (April 2023, Frontiers in Plant Science)

   IntroductionComputer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource- intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models. MethodsHere, we implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellum’s size and for classifying haploid and diploid kernels. Results and discussionWe show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision. 

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2. Ballot Tabulation Using Deep Learning

   Fei Zhao, Chengcui Zhang; Nitesh Saxena; Dan Wallach; Akm Shahariar Azad Rabby (August 2023, 2023 IEEE International Conference on Information Reuse)

   Currently deployed election systems that scan and process hand-marked ballots are not sophisticated enough to handle marks insufficiently filled in (e.g., partially filled-in), improper marks (e.g., using check marks or crosses instead of filling in bubbles), or marks outside of bubbles, other than setting a threshold to detect whether the pixels inside bubbles are dark and dense enough to be counted as a vote. The current works along this line are still largely limited by their degree of automation and require substantial manpower for annotation and adjudication. In this study, we propose a highly automated deep learning (DL) mark segmentation model-based ballot tabulation assistant able to accurately identify legitimate ballot marks. For comparison purposes, a highly customized traditional computer vision (T-CV) mark segmentation-based method has also been developed to compare with the DL-based tabulator, with a detailed discussion included. Our experiments conducted on two real election datasets achieved the highest accuracy of 99.984% on ballot tabulation. In order to further enhance our DL model’s capability of detecting the marks that are underrepresented in training datasets, e.g., insufficiently or improperly filled marks, we propose a Siamese network architecture that enables our DL model to exploit the contrasting features between a handmarked ballot image and its corresponding blank template image to detect marks. Without the need for extra data collection, by incorporating this novel network architecture, our DL modelbased tabulation method not only achieved a higher accuracy score but also substantially reduced the overall false negative rate. 

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3. Improving CLIP Counting Accuracy via Parameter-Efficient Fine-Tuning

   Zhang, Ruisu; Chen, Yicong; Lee, Kangwook (January 2025, Transactions on machine learning research)

   We focus on addressing the object counting limitations of vision-language models, with a particular emphasis on Contrastive Language-Image Pre-training (CLIP) models. Centered on our hypothesis that counting knowledge can be abstracted into linear vectors within the text embedding space, we develop a parameter-efficient fine-tuning method and several zero-shot methods to improve CLIP's counting accuracy. Through comprehensive experiments, we demonstrate that our learning-based method not only outperforms full-model fine-tuning in counting accuracy but also retains the broad capabilities of pre-trained CLIP models. Our zero-shot text embedding editing techniques are also effective in situations where training data is scarce, and can be extended to improve Stable Diffusion's ability to generate images with precise object counts. We also contribute two specialized datasets to train and evaluate CLIP’s counting capabilities. Our code is available at https://github.com/UW-Madison-Lee-Lab/CLIP_Counting. 

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4. A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning

   https://doi.org/10.3390/agriengineering3030032  

   Ahmed, Ahmed Abdelmoamen; Reddy, Gopireddy Harshavardhan (September 2021, AgriEngineering)

   Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species. 

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5. Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study

   https://doi.org/10.1186/s40537-023-00815-3  

   Rostami, Masoud A.; Balmaki, Behnaz; Dyer, Lee A.; Allen, Julie M.; Sallam, Mohamed F.; Frontalini, Fabrizio (December 2023, Journal of Big Data)

   Abstract Pollen identification is necessary for several subfields of geology, ecology, and evolutionary biology. However, the existing methods for pollen identification are laborious, time-consuming, and require highly skilled scientists. Therefore, there is a pressing need for an automated and accurate system for pollen identification, which can be beneficial for both basic research and applied issues such as identifying airborne allergens. In this study, we propose a deep learning (DL) approach to classify pollen grains in the Great Basin Desert, Nevada, USA. Our dataset consisted of 10,000 images of 40 pollen species. To mitigate the limitations imposed by the small volume of our training dataset, we conducted an in-depth comparative analysis of numerous pre-trained Convolutional Neural Network (CNN) architectures utilizing transfer learning methodologies. Simultaneously, we developed and incorporated an innovative CNN model, serving to augment our exploration and optimization of data modeling strategies. We applied different architectures of well-known pre-trained deep CNN models, including AlexNet, VGG-16, MobileNet-V2, ResNet (18, 34, and 50, 101), ResNeSt (50, 101), SE-ResNeXt, and Vision Transformer (ViT), to uncover the most promising modeling approach for the classification of pollen grains in the Great Basin. To evaluate the performance of the pre-trained deep CNN models, we measured accuracy, precision, F1-Score, and recall. Our results showed that the ResNeSt-110 model achieved the best performance, with an accuracy of 97.24%, precision of 97.89%, F1-Score of 96.86%, and recall of 97.13%. Our results also revealed that transfer learning models can deliver better and faster image classification results compared to traditional CNN models built from scratch. The proposed method can potentially benefit various fields that rely on efficient pollen identification. This study demonstrates that DL approaches can improve the accuracy and efficiency of pollen identification, and it provides a foundation for further research in the field. 

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