Image quality assessment is a challenging computer vision task due to the lack of corresponding reference (pristine) images. This no-reference bottleneck has been tackled with the utilisation of subjective mean opinion scores (MOS) termed as supervised blind image quality assessment (BIQA) methods. However, inaccessible opinion score scenarios limits their applicability. To relieve these limitations, we propose to employ reconstruction based learning trained only on pristine images. This permits an implicit distribution learning of pristine images and the deviation from this learned feature distribution is subsequently utilised for unsupervised image quality assessment. Specifically, an adversarial convolutional variational auto-encoder framework is employed with KL divergence, perceptual and discriminator loss. With state-of-the-art results on four benchmark datasets, we demonstrate the effectiveness of our proposed framework. An ablation study has also been conducted to highlight the contribution of each module i.e. loss and quality metric for an efficient unsupervised BIQA.

Multi-task learning (MTL) permits joint task learning based on a shared deep learning architecture and multiple loss functions. Despite the recent advances in MTL, one loss often dominates the learning optimization in multiple unrelated tasks. This often results in poor performance compared to the corresponding single task learning. To overcome the aforementioned "negative transfer", we propose a novel hierarchical framework that leverages task relations via inter-task affinity to supervise multi-task learning. Specifically, the inter-task affinity generated task sets, with low-level task set and complex task set at the bottom and top layers respectively, enables iterative multi-task information sharing. In addition, it also alleviates simultaneous image annotations for multiple tasks. The proposed framework achieves state-of-the-art results on classification, detection, semantic segmentation and depth estimation across three standard benchmarks. Furthermore, with state of the results on two benchmarks for image retrieval task, we also demonstrate that the embeddings learned using such a framework provide good generalization and robust representation learning.

Plant diseases severely limits agriculture production, necessitating the high-throughput monitoring of plant leaves. Currently, this is formulated as an automatic disease segmentation task addressed via deep learning frameworks. These deep leaning frameworks trained with leaf image data in a supervised paradigm have few limitations, mainly: (1) training datasets are heavily imbalanced towards healthy leaf images, (2) disease region annotation is labour-intensive and (3) due to the heterogeneity of disease symptoms, these frameworks lacks generalisability. In this paper, we reformulate disease segmentation as an anomaly localisation task. Specifically, we introduce a novel unsupervised framework (AnoLeaf) based on an edge-guided in-painting that optimises the learning of contextual attention on only healthy leaf images. The network utilisation on diseased leaf images results in reconstruction of its healthy counterparts, generating an inpainting error. The contextual attention maps reinforce the inpainting error to effectively localise the disease. Thus, AnoLeaf alleviates the acquisition and annotation of rare disease images. Additional experiments on MVTec anomaly detection dataset further demonstrate its generalisability.

Plant disease is a major factor in yield reduction. Thus, plant breeders currently rely on selecting disease-resistant plant cultivars, which involves disease severity rating of a large variety of cultivars. Traditional visual screening of these cultivars is an error-prone process, which necessitates the development of an automatic framework for disease quantification based on field-acquired images using unmanned aerial vehicles (UAVs) to augment the throughput. Since these images are impaired by complex backgrounds, uneven lighting, and densely overlapping leaves, state-of-the-art frameworks formulate the processing pipeline as a dichotomy problem (i.e. presence/absence of disease). However, additional information regarding accurate disease localization and quantification is crucial for breeders. This paper proposes a deep framework for simultaneous segmentation of individual leaf instances and corresponding diseased region using a unified feature map with a multi-task loss function for an end-to-end training. We test the framework on field maize dataset with Northern Leaf Blight (NLB) disease and the experimental results show a disease severity correlation of 73% with the manual ground truth data and run-time efficiency of 5fps.

Image based analysis of plants is a high-throughput and non-invasive approach to study plant traits. The quantitative estimation of many plant traits (leaf area index, biomass etc.) from plant images is primarily based on accurate segmentation of individual leaves. This is a challenging task due to the presence of overlapped leaves and lack of discernible boundaries between them. To overcome these limitations, state-of-the-art supervised deep learning algorithms have been recently employed. However, the annotations of individual leaf instances is time consuming, in addition the variability in leaf shapes and its arrangement among different plant species limits the broad utilisation of these algorithms. To relieve this bottleneck, we propose a novel framework that relies on a graph based formulation to extract leaf shape knowledge for the task of leaf instance segmentation. These shape priors are generated based on leaf shape characteristics independent of plant species. Evaluation of the proposed framework on multiple plant datasets i.e. Arabidopsis, Komatsuna and salad demonstrates its broad utility.

Stomatal morphology is a key phenotypic trait for plants' response analysis under various environmental stresses (e.g. drought, salinity etc.). Stomata exhibit diverse characteristics with respect to orientation, size, shape and varying degree of papillae occlusion. Thus, the biologists currently rely on manual or semi-automatic approaches to accurately compute its morphological traits based on scanning electron microscopic (SEM) images of leaf surface. In contrast to these subjective and low-throughput methods, we propose a novel automated framework for stomata quantification. It is realized based on a hybrid approach where the candidate stomata region is first detected by a convolutional neural network (CNN) and the occlusion is dealt with an inpainting algorithm. In addition, we propose stomata segmentation based quantification framework to solve the problem of shape, scale and occlusion in an end-to-end manner. The performance of the proposed automated frameworks is evaluated by comparing the derived traits with manually computed morphological traits of stomata. With no prior information about its size and location, the hybrid and end-to-end machine learning frameworks shows a correlation of 0.94 and 0.93, respectively on rice stomata images. Furthermore, they successfully enable wheat stomata quantification showing generalizability in terms of cultivars.