Top Research Papers on Plant Disease Detection

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.

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.

This project develops a Plant Leaf Disease Detection System that uses machine learning to identify plant diseases from uploaded leaf images and integrates the Google Translate API for multilingual support, making the system accessible to a global audience.

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...

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...

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.

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%.

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...

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.

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.

This project develops Mobile app for automatically detecting plant disease through image processing technique with the objective of providing fast, accurate, ease of use and inexpensive solutions to farmers in India.

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.

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.

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...

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...

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...

: 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...

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.