DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis

Abstract

The emergence of deep neural networks has allowed the development of fully automated and efficient diagnostic systems for plant disease and pest phenotyping. Although previous approaches have proven to be promising, they are limited, especially in real-life scenarios, to properly diagnose and characterize the problem. In this work, we propose a framework which besides recognizing and localizing various plant abnormalities also informs the user about the severity of the diseases infecting the plant. By taking a single image as input, our algorithm is able to generate detailed descriptive phrases (user-defined) that display the location, severity stage, and visual attributes of all the abnormalities that are present in the image. Our framework is composed of three main components. One of them is a detector that accurately and efficiently recognizes and localizes the abnormalities in plants by extracting region-based anomaly features using a deep neural network-based feature extractor. The second one is an encoder–decoder network that performs pixel-level analysis to generate abnormality-specific severity levels. Lastly is an integration unit which aggregates the information of these units and assigns unique IDs to all the detected anomaly instances, thus generating descriptive sentences describing the location, severity, and class of anomalies infecting plants. We discuss two possible ways of utilizing the abovementioned units in a single framework. We evaluate and analyze the efficacy of both approaches on newly constructed diverse paprika disease and pest recognition datasets, comprising six anomaly categories along with 11 different severity levels. Our algorithm achieves mean average precision of 91.7% for the abnormality detection task and a mean panoptic quality score of 70.78% for severity level prediction. Our algorithm provides a practical and cost-efficient solution to farmers that facilitates proper handling of crops.

Deep learning based genomic breeding of pest-resistant grapevine

“…Therefore, our analysis focused on damages to the grape leaves, which negatively corelated with SLF resistance for different grape varieties. Generally, the damages of plants caused by the disease and pest are often considered together in most studies using popular DL models designed for image recognition 54,60,61 . It is difficult to disengage the disease and pest resistant capabilities from the same plant.…”

“…Generally, the damages of plants caused by the disease and pest are often considered together in most studies using popular DL models designed for image recognition 54,60,61 .…”

Tomato leaf disease recognition based on multi-task distillation learning

“…In addition to identifying the presence of a plant disease, it is also crucial to estimate the severity of the disease, providing a quantitative assessment for disease diagnosis ( Ilyas et al., 2022 ; Ji and Wu, 2022 ). The precise localization, size, and distribution of infected regions in plant leaves can significantly enhance the accuracy of disease classification, especially in field images with complex backgrounds ( Barbedo, 2019 ).…”

Overcoming field variability: unsupervised domain adaptation for enhanced crop-weed recognition in diverse farmlands

“…One of the main challenges in developing an automatic weed management system is accurately detecting and recognizing weeds in crops. This can be difficult because weeds and crops often have similar colors, textures, and shapes, and may appear differently at different growth stages ( Sarvini et al., 2019 ; Khan et al., 2020 ; Ilyas et al., 2022 ). Other challenges include occlusion, variations in color and texture due to lighting and illumination, and the presence of motion blur and noise in images ( Sa et al., 2016 ).…”