Plant diseases are a vital risk to crop yield and early detection of plant diseases remains a complex task for the farmers due to the similar appearance in color, shape, and texture. In this work, authors have proposed an automatic plant disease detection technique using deep ensemble neural networks (DENN). Transfer learning is employed to fine-tune the pre-trained models. Data augmentation techniques include image enhancement, rotation, scaling, and translation are applied to overcome overfitting. This paper presents a detailed taxonomy on the performance of different pre-trained neural networks and presents the performance of a weighted ensemble of those models relevant to plant leaf disease detection. Further, the performance of the proposed work is evaluated on publicly available plant village dataset, which comprises of 38 classes collected from 14 crops. The performance of DENN outperform state-of-the-art pre-trained models such as ResNet 50 & 101, InceptionV3, DenseNet 121 & 201, MobileNetV3, and NasNet. Performance evaluation of the proposed model demonstrates that effective in categorizing various types of plant diseases that comparatively outperform pre-trained models.

Transfer learning-based deep ensemble neural network for plant leaf disease detection

The оссurrenсe or сhаnge in the diseases in а specific аreа саn be рrediсted in аdvаnсe with the help оf рlаnt disease fоreсаsting model. This helps to undertake suitable management measures to аvоid the losses well in аdvаnсe. Disease forecasting рrediсts рrоbаble outbreaks or increased disease intensity over a period in a particular area. This technique helps in timely аррliсаtiоn оf сhemiсаls to рlаnts, which also involve all асtivities оf сrор protection and intimate the farmers in the community via text messages or e-mail etс. means оf соmmuniсаtiоn. Environment controls the evolution and survival period of various pathogens. Environmental соnditiоns like minimum leaf wetness duration, soil moisture, micro-level relative humidity etс. contribute in evolution of disease causing раthоgens. Disease fоreсаsting system thus helps in рrediсting and avoiding evolution and spread of diseases. This рарer uses Mасhine Learning (ML) and Deep Learning (DL) algorithms to detect, classify and рrediсt the роssible раthоgens/diseases in the раrtiсulаr type оf сrор/рlаnt соnsidering based on weather соnditiоns. Temperature, moisture and humidity are the раrаmeters taken into соnsiderаtiоn. Соnvоlutiоn Neural Networks (СNN), Recurrent Neural Network (RNN), Artificial Neural Network (АNN), Suрроrt Vector Mасhines (SVM) and K-Nearest Neighbоurs (KNN) аre the five algorithms implemented and соmраred based on the obtained оutрut ассurасy. ANN outperforms all the other algorithms compared in this paper with accuracy of 90.79%.

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Comparison of Artificial Intelligence Algorithms in Plant Disease Prediction

Agriculture is the most critical part of the Indian economy so to prevent production loss, disease attacks must be predicted and treated early. In plants with differing climatic conditions, disease is very normal and natural which decreases the crop productivity. Study into the use of image processing techniques for plant disease identification has become a hot subject in order to address these issues. Deep learning (DL) is the golden age of machine learning (ML), and it is now helping to identify and classify plant diseases early. This review investigates and analyzed recent methods on Deep learning, Transfer learning and convolution neural network for crop disease detection. First, a look at the deep learning architectures, data sources and various image processing techniques that were used to process the imaging data. Many DL architectures have recently been adopted, along with visualization tools, which are critical for identifying signs and classifying plant diseases. We also go through some of the unsolved issues that must be tackled in order to create functional automated plant disease recognition systems that can be used in the field.

Review on Leaf diseases detection using Deep learning

Agriculture contributes majorly in the Indian economy being the most important aspect of it. Often the plants suffer from many diseases which may be dependent on climatic conditions and pests which further degrades the quality of the crop. Early and accurate detection of disease in plants is very crucial step in depicting the overall yield of the crop, as this can increase the yield and productivity of the crop by a great margin. In the current climatic conditions, to obtain the superior quality crop is getting difficult day by day as the plants suffer from different diseases. To solve the problem of early and accurate detection image processing has come up with various techniques to find best and suitable ways. This paper presents a review to examine the power of these techniques in detection for plant diseases and add in the agriculture advancement. This survey enfolds larger scope of deep learning in the future research while detecting plant diseases along with improvised performance and accuracy. This paper also states certain challenges which still exist in the detection of diseases in plants and opens some areas of research for the researchers. Various problems in the field of dataset collection have been addressed. Some possible solutions also have been suggested which can help the accuracy to increase for a model.

Plant Disease Identification Using Deep Learning: A Systematic Review

Detecting plant sickness is urgent according to the viewpoint of agribusiness, as illnesses frequently limit plants’ creation limit and will be helpful for phytopathologist. Notwithstanding, it is challenging, regularly worldly, and tedious to perceive plant sicknesses with manual methodologies. All things considered, the more significant part of the proposed models can analyze the diseases of a particular plant. Regardless of multi-class grouping, the model utilizes a multilabel format system to recognize both the plant and their disease. To examine and evaluate, we have made our own data set from different online resources that consisted leaf pictures of ten plants. In our analysis, we compared various famous CNN designs. The exploratory outcomes approve that the EfficientNet performs better in multilabel plant illness classification tasks. Additionally, we have tracked down that a CNN architecture, skip associations, spatial convolutions, and more limited secret layer network impact better outcomes in plant illness characterization.

Plant Disease Identification from Leaf Images using Deep CNN’s EfficientNet

Agricultural productivity is a key component of Indian economy. Therefore the contribution of food crops and cash crops is highly important for both the environment and human beings. Every year crops succumb to several diseases. Due to inadequate diagnosis of such diseases and not knowing symptoms of the disease and its treatment many plants die. This study provides insights into an overview of the plant disease detection using different algorithms. A CNN based method for plant disease detection has been proposed here. Simulation study and analysis is done on sample images in terms of time complexity and the area of the infected region. It is done by image processing technique. A total of 15 cases have been fed to the model, out of which 12 cases are of diseased plant leaves namely, Bell Paper Bacterial Spot, Potato Early Blight, Potato Late Blight, Tomato Target Spot, Tomato Mosaic Virus, Tomato Yellow Leaf Curl Virus, Tomato Bacterial Spot, Tomato Early Blight, Tomato Late Blight, Tomato Leaf Mold, Tomato Septoria Leaf Spot and Tomato Spider Mites and 3 cases of healthy leaves namely, Bell Paper Healthy, Potato Healthy and Tomato Healthy. Test accuracy is obtained as 88.80%. Different performance matrices are derived for the same.

Deepsikha

Papers

Plant Disease Detection Using CNN