An Image Recognition Approach for Crop Disease Detection in Agro-Field from Infected Plant Area

2024-11-18T06:53:57+00:00

A plant pathogen is an abnormal metabolic disorder that inhibits a plant's typical structure, development and function. Disease lowers the quality of crops and the yield they produce, which harms the economies of countries like Bangladesh, where agriculture is the primary economic industry. Crop disease identification considers visually observable patterns and colors of the diseased region. Manually observing patterns and colors to categorize diseases is time-consuming and appears not to be as effective when dealing with diseases not indigenous to the area. Image processing has become the most widely used method for identifying and categorizing diseases. Despite this, it is still thought that implementing image processing is a difficult and complex undertaking. The first difficult assignment to complete is an entirely distinct feature extraction task. The authors of this paper present an ensemble-based model that applies unique approaches to extracting numerous features from plant leaves, such as color, shape, and texture to identify various diseases. The mean value of the pixels in the affected area is determined to extract the color feature. We employed the Sobel edge detector to exhibit edges, which primarily include shape and texture information about an image. The results showed that the proposed method was effective in identifying the infected areas of the leaves, with an accuracy rate of 94%. This method could potentially be used for early detection and prevention of plant diseases in agriculture.

Advancements in Natural Language Processing for Human-Computer Interaction

2024-11-04T06:43:19+00:00

This systematic review explores the advancements in Natural Language Processing (NLP) for Human-Computer Interaction (HCI) over the period from 2010 to 2024. It highlights the significant breakthroughs achieved through deep learning models, particularly transformer architectures such as BERT and GPT, which have transformed the ability of machines to understand and generate human language. The integration of multimodal capabilities has further enriched user interactions by enabling the processing of diverse data types, including text, audio, and visual inputs. However, the review also identifies persistent challenges, including maintaining coherence in long dialogues, resolving ambiguous language, addressing bias in training data, and the need for resource-efficient models. Additionally, the paper emphasizes the importance of cross-lingual capabilities for low-resource languages and the necessity of personalized, adaptive systems. The findings underscore the need for ongoing research to overcome existing limitations and enhance the effectiveness and inclusivity of NLP technologies in HCI, ultimately contributing to a more intuitive and accessible user experience.

Global Mainstream Journal of Innovation, Engineering & Emerging Technology

An Image Recognition Approach for Crop Disease Detection in Agro-Field from Infected Plant Area

Advancements in Natural Language Processing for Human-Computer Interaction