Analisis Penggunaan Pra-proses pada Metode Transfer Learning untuk Mendeteksi Penyakit Daun Singkong

Singkong menjadi salah satu tanaman penting pada bidang agronomi dan banyak dikonsumsi oleh masyarakat. Namun, terdapat salah satu kendala dalam menjaga kelestarian tanaman singkong yaitu pendeteksian penyakit. Jika penyakit pada tanaman singkong dapat terdeteksi lebih dahulu melalui citra daunnya, maka penyakit tersebut dapat segera diobati. Proses klasifikasi dapat dilakukan untuk mendeteksi penyakit pada tanaman secara otomatis. Pada penelitian ini dilakukan klasifikasi tanaman singkong dengan menggunakan beberapa tahap pra-proses yaitu pra-proses dengan augmentasi, tanpa augmentasi dan pra-proses dengan rotasi, pada beberapa metode transfer learning seperti ResNet50 dan MobileNetV2. Penggunaan beberapa metode tersebut bertujuan untuk mencari metode mana yang memiliki hasil akurasi tertinggi. Penelitian menunjukkan bahwa MobileNetV2 tanpa augmentasi memberikan akurasi tertinggi sebesar 98.64% dalam mendeteksi penyakit tanaman singkong. Hal ini dapat menjadi referensi bagi peneliti selanjutnya dalam menentukan tahap pra-proses terbaik dalam metode transfer learning

OSC-CO\u3csup\u3e2\u3c/sup\u3e: Coattention and Cosegmentation Framework for Plant State Change with Multiple Features

Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object’s pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and cosegmentation-based dense Conditional Random Fields (CRFs) to address segmentation accuracy in high-dimensional plant imagery with evolving plant objects. The efficacy of OSC-CO2 is demonstrated using plant growth sequences imaged with infrared, visible, and fluorescence cameras in multiple views using a remote sensing, high-throughput phenotyping platform, and is evaluated using Jaccard index and precision measures. We also introduce CosegPP+, a dataset that is structured and can provide quantitative information on the efficacy of our framework. Results show that OSC-CO2 out performed state-of-the art segmentation and cosegmentation methods by improving segmentation accuracy by 3% to 45%

Object Counting with Deep Learning

This thesis explores various empirical aspects of deep learning or convolutional network based models for efficient object counting. First, we train moderately large convolutional networks on comparatively smaller datasets containing few hundred samples from scratch with conventional image processing based data augmentation. Then, we extend this approach for unconstrained, outdoor images using more advanced architectural concepts. Additionally, we propose an efficient, randomized data augmentation strategy based on sub-regional pixel distribution for low-resolution images. Next, the effectiveness of depth-to-space shuffling of feature elements for efficient segmentation is investigated for simpler problems like binary segmentation -- often required in the counting framework. This depth-to-space operation violates the basic assumption of encoder-decoder type of segmentation architectures. Consequently, it helps to train the encoder model as a sparsely connected graph. Nonetheless, we have found comparable accuracy to that of the standard encoder-decoder architectures with our depth-to-space models. After that, the subtleties regarding the lack of localization information in the conventional scalar count loss for one-look models are illustrated. At this point, without using additional annotations, a possible solution is proposed based on the regulation of a network-generated heatmap in the form of a weak, subsidiary loss. The models trained with this auxiliary loss alongside the conventional loss perform much better compared to their baseline counterparts, both qualitatively and quantitatively. Lastly, the intricacies of tiled prediction for high-resolution images are studied in detail, and a simple and effective trick of eliminating the normalization factor in an existing computational block is demonstrated. All of the approaches employed here are thoroughly benchmarked across multiple heterogeneous datasets for object counting against previous, state-of-the-art approaches

Analysis of Argonaute-Small RNA-Transcription Factor Circuits Controlling Leaf Development

Experimental studies of plant development have yielded many insights into gene regulation, revealing interactions between core transcriptional and post-transcriptional regulatory pathways present in all land plants. This work describes a direct connection between the three main small RNA-transcription factor circuits controlling leaf shape dynamics in the reference plant Arabidopsis thaliana. We used a high-throughput yeast 1-hybrid platform to identify factors directly binding the promoter of the highly specialized ARGONAUTE7 silencing factor. Two groups of developmentally significant microRNA-targeted transcription factors were the clearest hits from these screens, but transgenic complementation analysis indicated that their binding sites make only a small contribution to ARGONAUTE7 function, possibly indicating a role in fine tuning. Timelapse imaging methodology developed to quantify these small differences may have broad utility for plant biologists. Our analysis also clarified requirements for polar transcription of ARGONAUTE7. This work has implications for our understanding of patterning in land plants