Top Research Papers on Plant Disease Detection

This project is based on deep convolutional neural networks which enhances the accuracy and training efficiency and will help many farmers who are uneducated to get correct information about diseases and help increase their yield.

Agriculture is demographically the broadest economic sector in India and plays a significant role in the overall socio economic fabric of India. Several diseases affect plants and cause economic, social and economic losses. So detection of diseases accurately and giving control measures timely will increase growth of agriculture in India. The proposed system identifies the disease by observable patterns of particular plant; here SVM used for classification. This is an efficient and accurate method for automatic disease detection in plants.

The SVM, Random Forest classifier, and CNN are found to be efficient and accurate methods of classification and the potential for automation and smartphone-assisted diagnosis is covered.

An innovative approach that integrates machine learning and image processing techniques is introduced that identifies diseases based on key factors such as leaf color, damage extent, area, and texture parameters and ensures precise disease detection with minimized computational complexity and reduced prediction time.

With the use of the proposed algorithm, farmers will be able to identify diseases in their early stages, diagnose leaf diseases, and then control the crop as needed to maintain the health and safety of plants.

An analysis of various methods for detecting image processing plant leaf diseases and how the denoising step can be achieved by application of different filters is presented.

The best way to forestall misfortune in gather and number of cultivating items is through sickness identification. Plant sickness is not characterized as an illness that can be recognized by actual irregularities of the plant (like shrinking of leaves). Ill-advised upkeep or confirmation of plant illness can make colossal misfortunes to the ranchers. Plant wellbeing checking and sickness location are vital for maintainable farming. It is undeniably challenging to detect the illness physically for goliath regions. It requires more labor who work in plant sicknesses and furthermore requires real...

This research examines the possibility of methodologies for detecting plant disease detection systems that contribute in agricultural improvement and consists of several processes, such as image acquisition, image segmentation, feature extraction, and classification.

A brief overview on methodology is provided and recent research trends to identifying disease in the plant which is based on image processing techniques are reviewed.

Agricultural productivity is something on which the economy highly depends. This is one of the reasons that disease detection in plants plays an important role in the agriculture field, as having disease in plants is quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at a very early stage itself it detects the symptoms of disea...

One of the most major occupations and a key contributor to the GDP of our country is agriculture. Food crops have a vital role in the ecosystem and for human health, and plant diseases can result in large losses in food supply. Plant leaves can become infected with a variety of diseases, including blights, leaf spots, and other bacterial and fungal infections. It's critical to identify plant diseases in order to minimise yield losses. Disease identification and plant monitoring are essential to sustainable agriculture. Manually observing plant diseases is a challenging task. We therefore prese...

This review provides a comprehensive explanation of DL models used to visualize various plant diseases and some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.

One of the biggest revolutions of modern history is the invention of agriculture for a healthier lifestyle. It significantly changed the human culture and played an important role in the development of the population and biological improvements in food production and domestication. The frequency of pests on food crops increased because environmental circumstances were changing, and diseases on crops increased rapidly. These diseases inflict catastrophic social, economic, and ecological casualties, and this extraordinary challenge is a concern for the correct and prompt detection of diseases. I...

The proposed approach can successfully detect and classify the examined diseases with a precision between 83% and 94%, and able to achieve 20% speedup over the approach proposed in (1).

: Plant diseases are becoming more common, which poses a serious risk to the sustainability of agriculture and global food security. The prevention of crop losses and the reduction of pesticide use both depend critically on the early and precise detection of plant diseases. Deep learning methods, such Convolutional Neural Networks (CNNs), have recently displayed astounding performance in a variety of image identification tasks. In this study, leaf pictures are used to train CNNs for the identification and categorization of plant illnesses. The suggested method entails a multi-step procedure th...

In India, agriculture is the main driver of economic growth. Farmers choose the best crops for each season based on soil fertility, weather, and crop economics. Agricultural industries strive for better ways to produce food in order to meet the demands of an expanding population. New technologies that would increase yields while lowering investment are being sought after by researchers. A new technology called precision aids in enhancing farming practices. This makes it one of the most significant and vital factors to take into account when looking for plant illnesses. The notable uses of prec...

The research presents an automated vision a system that makes use of the processing of images techniques to identify plant diseases in agricultural contexts and demonstrates that the network classifier in use offers reduced training error and increased classification accuracy.

One of the computer vision technique, a convolutional neural network with the transfer learning method for effective classification of diseases in 3 crops namely Capsicum, Potato and Strawberry effectively provides an accuracy of 95%.

All human nutrition is based on vegetables. As a result, they are critical for crop production, ensuring that everyone has access to enough, safe, and nutritious food to live an active and healthy life. Plant diseases are a major threat to food security because they harm plants, decrease food supply and accessibility, and raise food prices. Diseases caused by microorganisms such as viruses, bacteria, and fungi pose a risk to plants. For this reason, plants must be monitored for infections from the beginning of their life cycle. The objective of this paper is to review various techniques of pla...

The need for a solution for early detection of cotton plant diseases, the diseases of cotton and their characteristics, different challenges farmers face while cultivating cotton and while identifying diseases in them, and the step by step technical approaches being used for the detection of Cotton plant diseases are majorly focused on.

Agriculture is an important part in the human lives and economy of the countries. Agriculture based countries like India are greatly dependent on their agricultural outcome for feeding the large population. Crop yields plays a major factor in the economy of every country. Agricultural production and economic development are closely connected. Moreover, agriculture provides the vital food to feed all living creatures on earth. Plant diseases are a significant threat to farmers, food production and economic well-being of the country. Significant research in this domain is required to protect the...

Agriculture is one of the most important sources of income for people in many countries. However, plant disease issues influence many farmers, as diseases in plants often naturally occur. If proper care is not taken, diseases can have hazardous effects on plants and influence the product quality, quantity or productivity. Therefore, the detection and prevention of plant diseases are serious concerns and should be considered to increase productivity. An effective detection and identification technology can be beneficial for monitoring plant diseases. Generally, the leaves of plants show the fir...

The Plant Disease Detection and classification project, aims to develop an automated system using machine learning and deep learning techniques to detect and classify plant diseases early on by analyzing leaf images by integrating diverse datasets and leveraging sophisticated machine learning algorithms.

A mobile application to identify plant disease and connect them with scientists and it is hoped it will help all farmers to do quality control, especially for the old age farmers.

This document summarizes research conducted in the last ten years on using artificial intelligence for agricultural disease identification and addresses the various obstacles that need to be addressed and potential fixes.

This research explores the transformative capabilities of Deep Learning models, primarily focusing on Convolutional Neural Networks (CNNs) and MobileNet architectures in the early and precise identification of plant ailments, by incorporating eXplainable Artificial Intelligence through GradCAM.

This paper has shown a general flow observed in most of the Plant Disease Detection techniques and given a detailed overview and comparison at stages such as selected dataset, pre-processing methods, feature selection and extraction, classification and performance metrics utilized.

A system involving a standard smartphone to predict the plant diseases using machine learning approach that potentially benefits the cultivators as it is capable to detect the diseases without minimal human intervention with prompt results.

The goal of this paper is to create a Disease Recognition Model that is supported by leaf image classification and utilizing image processing with a convolutional neural network, a form of artificial neural network that is used in image recognition.

A novel plant disease detection technique based on deep learning is proposed in this work and significantly detect diseases and achieves an accuracy of respectively.

Various image processing and classification techniques to detect and further eliminate plant diseases which has tremendous significance on the productivity of agriculture are reviewed.

A deep convolutional neural network is trained on a dataset of diseased and healthy agricultural crop plant leaves under controlled conditions to identify diseases or absence thereof and the innovative solution that provides efficient disease detection in agricultural crop plants is developed.

In this study, automatic detection of plant diseases was performed by using image processing and machine learning methods and the proposed model achieved 94 % success.

This review paper focused mainly on the most utilized classification mechanisms in disease detection of plants such as Convolutional Neural Network, Support Vector Machine, KNearest Neighbor, and Artificial Neural Network and observed that Convolutional Neural Network approach provides better accuracy compared to the traditional approaches.

This project aims for a web application which acts as user-friendly application for the farmers and supports the organic farming where they can capture the images of leaves and instantly they receive the name of the disease along with the description of disease and prevention methods of plant disease.

Agriculture is an essential sector that provides direct and indirect to economic growth. New technologies are constantly developing according to the changes and demands from the Agricultural sector. It is more important to conduct routine crop monitoring to identify diseases in their early stages and enhance crop yields. Black gram, a vital pulse crop cultivated across the nation, leads to annual financial losses for farmers as it is vulnerable to various diseases that impact crop yields. The diseases commonly detected and frequently observed are Anthracnose, Powdery Mildew, and Yellow-mosaic....

This paper studied and investigated existing techniques for the detection of early plant diseases and found each of the techniques has given its unique results by choosing appropriate datasets and algorithms.

is trained over a dataset of diseased plants obtained from ‘Plant Village Dataset’. The developed detection approach is evaluated on measures of F1 score, precision and recall.

This paper studied and evaluated existing techniques for detection of plant diseases to get clear outlook about the techniques and methodologies followed and discussed existing segmentation method along with classifiers for detection in Monocot and Dicot family plant.

An Android application that gives users the ability to identify plant species based on photographs of the plant’s leaves taken with a mobile phone using an algorithm that acquires morphological features of the leaves, computes well documented metrics such as the angle code histogram (ACH), then classifies the species based on a novel combination of the computed metrics.

In this research proposal, the various advantages and disadvantage of the plant diseases prediction techniques are discussed and a novel approach for the detection algorithm is proposed.

This work described the innovative solution that provides efficient disease detection and deep learning with CNN has achieved great success in the classification of various plant leaf diseases.

The proposed system is a software solution for automatic detection and computation of texture statistics for plant leaf diseases, and experimental results on a database of about 500 plant leaves of 30 different plants confirm the robustness of the proposed approach.

The need of simple plant leaf diseases detection system that would facilitate advancement in agriculture along with some hardware solution like providing useful fertilizers is reviewed.

Characteristics of pathogenic microbes symptoms of plant diseases dissemination of plant pathogens cross protection chemodiagnostic methods electron microscopy seriodiagnostic Methods nucleic acid based techniques detection of double-stranded RNAs diagnosis and monitoring of plant disease.

This paper demonstrates how various CNN architectures and transfer learning techniques can be applied for the disease detection in cassava plant.

This research focuses on creating a deep learning model that detects the type of disease that affected the plant from the images of the leaves of the plants by performing transfer learning.

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Productivity of plant decreases due to infections caused by variety of diseases. The diseases not only restrict the growth of plant but also reduce quality and quantity of plant. Different technique is adopted for detecting and diagnosis the diseases but the better way is by using Image Processing. Automatic plant disease detection is an important topic in research as it has been proved useful in monitoring large crop fields, and thus automatically detects the leaf disease symptoms as soon as they appear in plant leaves. The proposed system consist of five steps, first RGB image is acquired th...

A deep-learning based approach that is based on improved convolution neural networks for the real-time detection of apple leaf diseases can automatically identify the discriminative features of the diseased apple images and detect the types of apple leaf diseases with high accuracy.