Classification Models for Plant Diseases Diagnosis: A Review

Plants are important source of our life. Crop production in a good figure and good quality is important to us. The diagnosis of a disease in a plant can be manual or automatic. But manual detection of disease in a plant is not always correct as sometimes it can be not be seen by naked eyes so an automatic method of detection of plant diseases should be there. It can make use of various artificial intelligence based or machine learning based methods. It is a tedious task as it needs to be identified in earlier stage so that it will not affect the entire crop. Disease affects all species of plant, both cultivated and wild. Plant disease occurrence and infection severity vary seasonally, regarding the environmental circumstances, the kinds of crops cultivated, and the existence of the pathogen. This review attempts to provide an exhaustive review of various plant diseases and its types, various methods to diagnose plant diseases and various classification models used so as to help researchers to identify the areas of scope where plant pathology can be improved

A Review on Advances in Automated Plant Disease Detection

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images

Machine vision detection of pests, diseases, and weeds: A review

Most of mankind’s living and workspace have been or going to be blended with smart technologies like the Internet of Things. The industrial domain has embraced automation technology, but agriculture automation is still in its infancy since the espousal has high investment costs and little commercialization of innovative technologies due to reliability issues. Machine vision is a potential technique for surveillance of crop health which can pinpoint the geolocation of crop stress in the field. Early statistics on crop health can hasten prevention strategies such as pesticide, fungicide applications to reduce the pollution impact on water, soil, and air ecosystems. This paper condenses the proposed machine vision relate research literature in agriculture to date to explore various pests, diseases, and weeds detection mechanisms

A Novel Paddy Leaf Disease Detection Framework using Optimal Leaf Disease Features in Adaptive Deep Temporal Context Network

Since paddy has become the staple food for all human beings, crop productivity is highly demanded. Nowadays, the agriculture industry faces the leaf disease issue as the insect or pests affects the plant leaves to hinder further growth. Owing to this, the productivity gets affected that makes the farmers have economic loss. In earlier time, several methods have been explored to detect the disease significantly. However, such methods become more time consuming, structure complexity and other issues. To alleviate such complex, a new paddy leaf disease detection model is proposed using adaptive methodology. Initially, images related with paddy leaf are gathered from standard resources and offered as the input to segmentation region. Here, segmentation is performed by Fuzzy C-Means (FCM) to get the abnormal regions. Then, the segmented images are fed to ensemble feature extraction region to attain different features like deep, textural, morphological, and color features. Further, the acquired ensemble features are provided to concatenation phase to obtain the concatenate features and the optimal features are selected by the Fire Hawk Optimizer (FHO). Finally, the optimal features are subjected to paddy leaf detection phase, where leaf disease will be detected by Adaptive Deep Temporal Context Network (ADTCN), where the parameters are tuned by the FHO. Hence, the developed model secures efficient leaf disease detection rate than the classical techniques in the experiential analysis

Customized CNN Model for Multiple Illness Identification in Rice and Maize

Crop diseases imperil global food security and economies, demanding early detection and effective management. Convolutional Neural Networks (CNNs), particularly in rice and maize leaf disease classification, have gained traction due to their automatic feature extraction capabilities. CNN models eliminate manual feature extraction, enabling precise disease diagnosis based on learned features. Researchers have rapidly advanced these models, achieving promising results. Leaf disease characteristics like color changes, texture variations, and lesion appearance have been identified as useful for automated diagnosis using machine learning. Developing CNN models involves crucial stages: dataset preparation, architecture selection, hyperparameter tuning, and model training and evaluation. Diverse and accurately annotated datasets are pivotal, and appropriate CNN architecture selection, such as ResNet101 and XceptionNet, ensures optimal performance. These architectures' pre-training on vast image datasets enhances feature extraction. Hyperparameter tuning fine-tunes the model, and training and evaluation gauge its precision. CNN models hold potential to enhance rice and maize productivity and global food security by effectively detecting and managing diseases

Rice Leaf Blast Disease Detection Using Multi-Level Colour Image Thresholding

Rice diseases have caused a major production and economic losses in the agricultural industry. To control and minimise the impacts of the attacks, the diseases need to be identified in the early stage. Early detection for estimation of severity effect or incidence of diseases can save the production from quantitative and qualitative losses, reduce the use of pesticide, and increase country’s economic growth. This paper describes an integrated method for detection of diseases on leaves called Rice Leaf Blast (RLB) using image processing technique. It includes the image pre-processing, image segmentation and image analysis where Hue Saturation Value (HSV) colour space is used. To extract the region of interest, image segmentation (the most critical task in image processing) is applied, and pattern recognition based on Multi-Level Thresholding approach is proposed. As a result, the severity of RLB disease is classified into three categories such as infection stage, spreading stage and worst stage

Autoencoders for semantic segmentation of rice fungal diseases

Received: January 4th, 2021 ; Accepted: March 22th, 2021 ; Published: March 31th, 2021 ; Correspondence: [email protected] the article, the authors examine the possibility of automatic localization of rice fungal infections using modern methods of computer vision. The authors consider a new approach based on the use of autoencoders - special neural network architectures. This approach makes it possible to detect areas on rice leaves affected by a particular disease. The authors demonstrate that the autoencoder can be trained to remove affected areas from the image. In some cases, this allows one to clearly highlight the affected area by comparing the resulting image with the original one. Therefore, modern architectures of convolutional autoencoders provide quite acceptable visual quality of detection

A Comprehensive Review on Intelligent Techniques in Crop Pests and Diseases

Artificial intelligence (AI) has transformative potential in the agricultural sector, particularly in managing and preventing crop diseases and pest infestations. This review discusses the significance of early detection and precise diagnosis of various AI tools and techniques for disease identification, such as image processing, machine learning, and deep learning. It also addresses the challenges of AI implementation in agriculture, including data quality, costs, and ethical concerns. The analysis classifies the hurdles and AI offers benefits such as improved resource management, timely interventions, and enhanced productivity. Collaborative efforts are essential to harness AI's potential for sustainable and resilient agriculture

Techniques of deep learning and image processing in plant leaf disease detection: a review

Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated