Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

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Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models.

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A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment

Custom Convolutional Neural Network for Detection and Classification of Rice Plant Diseases

Tomato Leaf Disease Recognition Using Depthwise Separable Convolution

AbstractIn the 21st-century crop complaint is a serious concern for food security. In this period, utmost of the husbandry support centers and numerous growers use different technologies to ameliorate productivity in farming, but still concerned about plant safety and fast detection of plant leaf diseases which remains difficult in different parts of the regions. Rice plants are frequently infected with diseases that can result in social and financial damage. Many rice crop diseases manifest themselves first on the leaves of the plants. As we say, automated rice plant disease diagnosis is an important aspect of food security, yield loss estimate, and disease management. As a result, computer vision and image processing are utilized to identify infected leave. With the proliferation of digital cameras and ongoing advancements in the computer vision area, automated disease detection techniques are in great demand in precision agriculture, high-yield agriculture, smart greenhouses, and other fields. This study uses an open dataset with 4 types of leaf infections, namely brown spot, blast, bacterial blight, and tungro. In this research, automatically identify plant leaf infection and classify whether the leaf is healthy or diseased, instead of the traditional overlong manual disease diagnostic method, deep CNN models may obtain the best accuracy. We have compared our suggested model (custom-CNN) against pre-trained deep CNN models i.e. VGG19, DensNet121, InceptionV3, as well as ResNet152 deep learning models, and with a learning rate of 0.001, the custom-CNN model obtained greater accuracy of 97.47%. This paper is willing to support and help the farmer Community of the world.KeywordsRice leaf diseaseConvolutional neural networkDeep learningResNet-152InceptionV3Custom-CNN 

Rice Leaf Disease Detection and Classification Using a Deep Neural Network

The rice leaf suffers from several bacterial, viral, or fungal diseases and these diseases reduce rice production significantly. To sustain rice demand for a vast population globally, the recognition of rice leaf diseases is crucially important. However, recognition of rice leaf disease is limited to the image backgrounds and image capture conditions. The convolutional neural network (CNN) based model is a hot research topic in the field of rice leaf disease recognition. But the existing CNN-based models drop in recognition rates severely on independent dataset and are limited to the learning of large scale network parameters. In this paper, we propose a novel CNN-based model to recognize rice leaf diseases by reducing the network parameters. Using a novel dataset of 4199 rice leaf disease images, a number of CNN-based models are trained to identify five common rice leaf diseases. The proposed model achieves the highest training accuracy of 99.78% and validation accuracy of 97.35%. The effectiveness of the proposed model is evaluated on a set of independent rice leaf disease images with the best accuracy of 97.82% with an area under curve (AUC) of 0.99. Besides that, binary classification experiments have been carried out and our proposed model achieves recognition rates of 97%, 96%, 96%, 93%, and 95% for Blast, Brownspot, Bacterial Leaf Blight, Sheath Blight and Tungro, respectively. These results demonstrate the effectiveness and superiority of our approach in comparison to the state-of-the-art CNN-based rice leaf disease recognition models.

Mohammed Abser Bin Ali

Papers

Rice Leaf Diseases Recognition Using Convolutional Neural Networks