Top Research Papers on Plant Disease Detection

A CNN based method for plant disease detection has been proposed here and performs well in terms of time complexity and the area of the infected region.

Agriculture is the basis of every economy worldwide. Crop production is one of the major factors affecting domestic market condition in any country. Agricultural production is also a major prerequisite of economic development, be it any part of any country. It plays a crucial role as it even provides raw material, employment and food to different citizens. A lot of issues are responsible for estimated crop production varying in different parts of the world. Some of these include overutilization of chemical fertilizers, presence of chemicals in water supply, uneven distribution of rainfall, dif...

This model is able to detect several diseases from plants using pictures of their leaves and can be integrated with drone or any other system to live detect diseases from plants and report the diseased plants location to people so that they can be cured accordingly.

New datasets for food grains specifically for rice, wheat, and maize are developed to address the identified challenges and a new convolutional neural network model is proposed trained from scratch on all three food grain datasets developed.

It was shown that the most common disease is blight; fungus is the most important pathogen and grape and watermelon are the most studied crops; the most used drone type is the quadcopter and the most applied machine learning task is classification.

This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years and presents the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques.

CNN’s architectures applying transfer learning and deep feature extraction are evaluated, and the result obtained shows that SVM is the best classifier for leaf's diseases detection.

This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model and results showed that the proposed approach achieved a detection accuracy of 98.26%.

A valid lightweight network architecture, namely MobInc-Net, is proposed to perform the crop disease recognition and detection and can attain the desired performance with an average recognition accuracy of 99.21% on the public dataset and 97.89%" on the local dataset.

FieldPlant is suggested as a dataset that includes 5,170 plant disease images collected directly from plantations and evaluated state-of-the-art classification and object detection models and found that classification tasks on FieldPlant outperformed those on PlantDoc.

Common infections along with the research landscape at different stages of such detection systems are discussed and the modern feature extraction techniques are analyzed for identifying those that appear to work well covering several crop categories.

Deep Learning is used because of the advantages it offers to work with images especially in image classification to get improvised results on classification of crop diseases based on the patterns extracted from the diseased leaves.

This study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized.

This model has the potential to apply to smart farming of Solanaceae crops and will be widely used by adding more various crops as training dataset and the low accuracy of non-model crops was improved by adding these crops to the training dataset implicating expendability of the model.

This work has reviewed 100 of the most relevant CNN articles on detecting various plant leaf diseases over the last five years, and identified and summarized several problems and solutions corresponding to the CNN used in plant leaf disease detection.

The process of training ResNet models on an open image dataset provides a sound way towards crop disease detection using automated networks on an enormous global scale.

A novel classification scheme is proposed that categorizes all relevant works in the associated classes of plant diseases and shows that object detection accuracy is high with YOLOv5 and the networks ResNet50 and MobileNetv2 have the most optimal trade-off on accuracy and training time.

A robust plant disease classification system is introduced by introducing a Custom CenterNet framework with DenseNet-77 as a base network and is more proficient and reliable to identify and classify plant diseases than other latest approaches.

The experimental results prove the validity of the proposed approach, and it is accomplished efficiently for rice disease detection.

Abstract One of the essential components of human civilization is agriculture. It helps the economy in addition to supplying food. Plant leaves or crops are vulnerable to different diseases during agricultural cultivation. The diseases halt the growth of their respective species. Early and precise detection and classification of the diseases may reduce the chance of additional damage to the plants. The detection and classification of these diseases have become serious problems. Farmers’ typical way of predicting and classifying plant leaf diseases can be boring and erroneous. Problems may aris...

Novel ResTS architecture incorporates the residual connections in all the constituents and it executes batch normalization after each convolution operation which is dissimilar to the formerly proposed Teacher/Student architecture for plant disease diagnosis.

The techniques used for plant virus diagnosis and disease control are reviewed, including characteristics such as accuracy, detection level, multiplexing, quantification, portability, and designability.

A new computer vision system is presented to automatically recognize several diseases, detect previously unseen disease and to estimate per-leaf severity of tomato disease.

This paper develops a probabilistic programming approach for plant disease detection using state-of-the-art Bayesian deep learning techniques and the uncertainty as a misclassification measurement and shows that Bayesian inference achieves classification performance that is comparable to the standard optimization procedures for fine-tuning deep learning models.

Abstract As it is known, a significant part of the yield of agricultural crops is lost due to harmful organisms, including diseases. The article reveals the data on the widespread types of plant diseases (rot, wilting, deformation, the formation of tumors, pustules, etc.) and their symptoms. Early identification of the pathogen type of plant infection is of high significance for disease control. Various methods are used to diagnose pathogens of disease on plant. This article discusses the review of the literature data on traditional methods for diagnosis of plant pathogens, such as visual obse...

This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process that uses Convolutional Neural networks as an underlying deep learning engine for classifying 38 disease categories.

In the field of modern agriculture, plant disease detection plays a vital role in improving crop productivity. To increase the yield on a large scale, it is necessary to predict the onset of the disease and give advice to farmers. Previous methods for detecting plant diseases rely on manual feature extraction, which is more expensive. Therefore, image-based techniques are gaining interest in the research area of plant disease detection. However, existing methods have several problems due to the improper nature of the captured image, including improper background conditions that lead to occlusi...

This research examines four crop diseases: tomato, chilli, potato, and cucumber and highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms.

In this document, a methodology was proposed for the detection as well as the classification of diseases that occur for the potato plants, using the openly accessible, standard, and reliable data set, popularly known as Plant Village Dataset.

This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages.

The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A c...

Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recommended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is ...

An IASM mechanism is proposed to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer.

Human activities significantly contribute to worldwide spread of phytopathological adversities. Pathogen-related food losses are today responsible for a reduction in quantity and quality of yield and decrease value and financial returns. As a result, “early detection” in combination with “fast, accurate, and cheap” diagnostics have also become the new mantra in plant pathology, especially for emerging diseases or challenging pathogens that spread thanks to asymptomatic individuals with subtle initial symptoms but are then difficult to face. Furthermore, in a globalized market sensitive to epid...

A system that is used to classify and detect plant leaf diseases using deep learning techniques and has excellent accuracy in training and testing is presented.

This study addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants.

Plant diseases and pernicious insects are a considerable threat in the agriculture sector. Therefore, early detection and diagnosis of these diseases are essential. The ongoing development of profound deep learning methods has greatly helped in the detection of plant diseases, granting a vigorous tool with exceptionally precise outcomes but the accuracy of deep learning models depends on the volume and the quality of labeled data for training. In this paper, we have proposed a deep learning-based method for tomato disease detection that utilizes the Conditional Generative Adversarial Network (...

This work proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data using a recently developed convolutional neural network—using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work.

The various bacterial/fungal capsicum diseases are discussed, how to identify/classify these diseases using image processing technique and how these diseases can be classified by using support vector machine (SVM).

A convolutional neural network that consists of smaller number of layers leading to lower computational burden is developed that is well fit to identify apple leaf diseases and achieves 98% classification accuracy.