High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems

Abstract

Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence. Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers.
 In this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions.

Plant Disease Classifier: Detection of Dual-Crop Diseases Using Lightweight 2D CNN Architecture

“…Chowdhury et al [18] proposed a work in which classification is done on 18,162 images from PlantVillage dataset for ten classes where DenseNet201 achieved an accuracy of 98.05% for ten classes & 97.99% for six classes. For the same amount of data from PlantVillage dataset, Muhamad et al [19] proposed a method using CNN model and attained an average accuracy of 97.39% and Özbilge et al [38] proposed a method using Compact CNN model and attained an average accuracy of 99.70% for ten classes. Chen et al [20] proposed a method to implement the function of AlexNet modification architecture-based CNN on the Android platform to predict tomato diseases which gave 98% accuracy for a dataset having 10 classes and 22,930 images.…”

“…The lightweightness of the proposed 2D CNN model has also been analyzed against the existing research works mentioned in Table 7. By looking at some of the previous works, it was noticed that some methods proposed in references [19], [20], [22] and [23] used very lightweight CNN models that have very similar accuracies and parameter count is lower than that of the proposed model. However, they are incapable of classifying 14 different classes with high accuracy and precision unlike the proposed method.…”

Diagnosis Retinal Disease by using Deep Learning Models

“…To manage grid-based data like images, Deep Learning uses Convolutional Neural Networks (CNN). A Deep Learning system mimics the way human knowledge recognizes visual characteristics that separate normal from abnormal groups [6 -12] and for more details about the design (CNN) with medical image analysis in [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Some of the most important related works in this field were in 2020 [8] proposed a deep convolutional neural network (CNN) structure for successful diagnosis and classification into Normal, DMD, and DME also the same year [3] proposed a self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis and in [9] A model based on deep learning (DL) architecture, consisting of a densely connected neural network (DenseNet) and a trainable end-to-end recurrent neural network (RNN), is proposed.…”

Investigation of early symptoms of tomato leaf disorder by using analysing image and deep learning models

“…Plant pathogens may be broken down into many distinct categories, such as fungi, fungal-like organisms, bacteria, viruses, viroid, viruslike organisms, nematodes, protozoa, algae, and parasitic plants. Applications in the fields of renewable energy power prediction [4,5], biomedicine [6,7], and computer vision [8,9] have all benefited greatly from the use of AI, ML, and CV. As a consequence of the COVID-19 epidemic, there has been an increase in the use of AI for the diagnosis of lung-related diseases [8,9,10,11] and other predictive applications [12].…”

“…Applications in the fields of renewable energy power prediction [4,5], biomedicine [6,7], and computer vision [8,9] have all benefited greatly from the use of AI, ML, and CV. As a consequence of the COVID-19 epidemic, there has been an increase in the use of AI for the diagnosis of lung-related diseases [8,9,10,11] and other predictive applications [12]. The implementation of comparable cutting-edge technology may alleviate plant ailments' unfavourable impacts through prompt identification and assessment during their initial phases.…”