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.

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.