Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach

Abstract

<p>The acknowledgment of plant diseases assumes an indispensable part in taking infectious prevention measures to improve the quality and amount of harvest yield. Mechanization of plant diseases is a lot advantageous as it decreases the checking work in an enormous cultivated area where mango is planted to a huge extend. Leaves being the food hotspot for plants, the early and precise recognition of leaf diseases is significant. This work focused on grouping and distinguishing the diseases of mango leaves through the process of CNN. DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet152V2, and Xception all these models of CNN with transfer learning techniques are used here for getting better accuracy from the targeted data set. Image acquisition, image segmentation, and features extraction are the steps involved in disease detection. Different kinds of leaf diseases which are considered as the class for this work such as anthracnose, gall machi, powdery mildew, red rust are used in the dataset consisting of 1500 images of diseased and also healthy mango leaves image data another class is also added in the dataset. We have also evaluated the overall performance matrices and found that the DenseNet201 outperforms by obtaining the highest accuracy as 98.00% than other models.</p>

IBSHC: Improved BIRCH‐based segmentation and hybrid classification model for Mango Disease Detection

“…In 2021, Rajbongshi et al. concentrated on using CNN to classify and identify the illnesses affecting mango leaves.…”

“…The PMRFO performance is computed with different classifiers like RNN. DBN, GRU, CNN, NNE (Mia et al., 2020), and TLA (Rajbongshi et al., 2021) along with the findings are provided in Table 5. When observing the table, the PMRFO offered better outcomes over the others for mango disease detection.…”

“…As agriculture is the origin of all cultures, the king fruit, mango has a significant impact on businesses in the agricultural sector (Mia et al., 2020). Growing interest in mango plant culture has been seen throughout the world to support the production of fruit using sustainable farming methods (Rajbongshi et al., 2021; Saleem et al., 2021). Because of climate change (Chouhan et al., 2020) and other related issues, the farmers in this region are currently worried about the consequences of numerous illnesses on mango plants.…”

A Deep Learning Based Smartphone Application for Detecting Mango Diseases and Pesticide Suggestions

“…In their trial, they discovered that segmentation of anthracnose (fungal) sickness was accurate, with an average specificity of 91.15 % and Sensitivity of 90.86%. Rajbongshi et al [12] employed convolutional neural network models to diagnose mango leaf disease using a transfer learning method. They ought to employ methods like CNN and others to concentrate on more ailments in order to increase accuracy.…”

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

“…Adducing the major findings about the viability of employing an in-band scenario for deploying NB-IoT over a 4G network in a suburban setting based on the acquired data. Rajbongshi et al [36], Erwin et al [37] suggested different types of leaf diseases, such as anthracnose, gall machi, powdery mildew, and red rust, are employed in the dataset, which includes 1500 photos of damaged and healthy mango leaves. A new category has been added to the dataset.…”