Handling Severity Levels of Multiple Co-Occurring Cotton Plant Diseases Using Improved YOLOX Model (original) (raw)

Deep Learning Based Model For Rice Plant Disease Classification Using YOLOV8

International Journal Of Computer Science And Explorer (IJCSE) | Volume I | Issue I., 2023

Proper plant diseases classification is important as it helps farmers in providing appropriate pesticide to the affected area. Rice farmers are facing a lot of challenges in classifying diseases in their rice farm, improper classification can lead to in appropriate pesticides and as such the damages can increase. In Nigeria rice is among the most stable food that is consumed in almost all house hold, as it’s normally used in wedding events, ceremonies and during festival celebration. Rice farmers used necked eye observation to classify the sign or symptoms of the disease which is time consuming and sometimes expert need to be taken to the field for proper classification. In some cases samples need to be taken to laboratory which is another expensive and time consuming. Therefore, the need for thorough research to investigate the classification of diseases in rice plant is of much significant in order to improve quantity and quality during cultivation which as a result will enhance food security of the nation. This research present a deep learning based model for rice plant diseases classification using YOLOv8 as pretrained model it also used data that are directly captured from our environment using android phones. 500 images were captured under white background and are used during the training of the model. 100% accuracy was obtained from both training and validation on 50 epochs. The model will performed only binary classification and it was trained in goggle colab. Keywords: Plant disease classification, Yolov8, Deep learning, Rice disease, Pesticide

Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis

Journal of Electrical and Computer Engineering

Cotton is one of the economically significant agricultural products in Ethiopia, but it is exposed to different constraints in the leaf area. Mostly, these constraints are identified as diseases and pests that are hard to detect with bare eyes. This study focused to develop a model to boost the detection of cotton leaf disease and pests using the deep learning technique, CNN. To do so, the researchers have used common cotton leaf disease and pests such as bacterial blight, spider mite, and leaf miner. K-fold cross-validation strategy was worn to dataset splitting and boosted generalization of the CNN model. For this research, nearly 2400 specimens (600 images in each class) were accessed for training purposes. This developed model is implemented using python version 3.7.3 and the model is equipped on the deep learning package called Keras, TensorFlow backed, and Jupyter which are used as the developmental environment. This model achieved an accuracy of 96.4% for identifying classes ...

Deep Learning-Based Rice Leaf Diseases Detection Using Yolov5

Sukkur IBA Journal of Computing and Mathematical Sciences

The Rice crop in Agriculture field is playing an important role in economy of Pakistan and fulfilling the needs of living hood of human beings. The rice leaf faces several diseases like Bacterial Bligh, Brown Spot, Blast and Tungro. This research attempts to create a simple and best model for Rice leaf disease detection using deep learning model Yolov5. The model has been upgraded to v5 which is the latest version of Yolo. The performance and accuracy of object detection using Yolov5 is better than Yolov3 and Yolov4 models. This model is able to differentiate and successfully detect the rice leaf diseases. The Rice leaf images Dataset is downloaded from Kaggle website, the dataset contains 400 images of leaf infected by disease. This paper uses Google colab platform to train, validate and test the model for Rice Leaf disease detection. All necessary steps to be implemented, the rice leaf disease are detected and fully described. The developed model utilize epochs: 100. The experimen...

Plant Diseases Prediction Using Image Processing

IRJET, 2022

In India, the tomato plant is the most advanced crop. Farmers across the world are dealing with difficulties because of disruptions, illness, or inadequacies in their equipment. For the assurance of plant leaf contamination, they rely on the information they receive from the cultivating divisions. This engagement is complicated and lengthy. Here is a framework that will assist ranchers all around the world in accurately and quickly recognizing plant leaf diseases. The main purpose of this framework is to achieve more consistent execution in the detection of infections. In the middle of several plant diseases that affect leaves, such as Late scourge, bacterial, and viral infections, it has been chosen to separate contaminated leaves from healthy leaves, which includes Late scourge, bacterial, and viral infections. Using a large dataset, the proposed approach is designed to successfully discriminate specified illnesses that affect tomato plant leaves. We proposed using CNN techniques to predict tomato leaf disease.

MULTIPLE PLANT LEAF DISEASE CLASSIFICATION USING DENSENET-121 ARCHITECTURE

IAEME PUBLICATION, 2021

Agriculture is the backbone of a country in terms of economy and survival of the people. To maintain a high efficiency of crop production we look to avoid plant diseases. The proposed algorithm is to optimize the information from the resources available to us for the betterment of the result without any complexity. The neural network used for classification is the Dense Convolution Neural Network (DCNN). In this project, a pre-trained neural network model (densenet-121) which isimported from the keras library has been used for training. Aconvolution may be a simple application of a filter to an input that leads to activation. Frequent application of equivalent filterto an input, leads to a map of activations called feature map, indicating the locations and strength of a detected feature in an input such as an image. The convolutional networks help to automatically learn an outsized number of filters in parallel specific to a training dataset. This algorithm helps provide an efficient result in detecting plant diseases, which in turn helps the economy of the country as well. Using 35779 images from Huges DP Plant- Village dataset from Kaggle, the densenet-121 has been used to classify the 29 different diseases for 7 plants (potato, tomato, corn, bell pepper, grape, apple and cherry). In this project, the original image is converted to HSV color form and then the masked image is generated by thresholding and given to the proposed model for training and classification, giving an average accuracy (theoretical) of 98.23%. When all classes of plant disease are given together to the model for training on google colab platform, (Tesla-T4 processor) we got an average accuracy of 94.96% for 50 epochs with a learning rate of 0.002. Additionally, a basic user-friendly website to test the trained model for the disease affected plant images and get prescription for the plant disease. Further the scope to extend the dataset used for training to identify many plant diseases.

Recognition of Image-Based Plant Leaf Diseases Using Deep Learning Classification Models

Nature Environment and Pollution Technology, 2021

Plant diseases are spread by a variety of pests, weeds, and pathogens and may have a devastating effect on agriculture, if not handled in a timely manner. Farmers face umpteen challenges from a proper water supply, untimely rain, storage facilities, and several plant diseases. Crops disease is the primary threat and it causes enormous loss to farmers in terms of production and finance. Identifying the disease from several hectares of agricultural land is a very difficult practice even with the presence of modern technology. Accurate and rapid illness prediction for early illness treatment to crops minimizes economical loss to the individual and further proves to be productive for healthy crops. Many studies use modern deep learning approaches to improve the accuracy and performance of object detection and identification systems. The suggested method notifies farmers of different agricultural illnesses, prompting them to take further essential precautions before the disease spreads t...