Assessment of plant disease detection by deep learning

Abstract

Plant disease and pest detection machines were originally used in agriculture and have, to some extent, replaced traditional visual identification. Plant diseases and pests are important determinants of plant productivity and quality. Plant diseases and pests can be identified using digital image processing. According to the difference in the structure of the network, this study presents research on the detection of plant diseases and pests based on three aspects of the classification network, detection network, and segmentation network in recent years, and summarizes the advantages and disadvantages of each method. A common data set is introduced and the results of existing studies are compared. This study discusses possible problems in the practical application of plant disease and pest detection based on deep learning. Conventional image processing algorithms or manual descriptive design and classifiers are often used for traditional computer vision-based plant disease and pest detection. This method usually uses various characteristics of plant diseases and pests to create an image layout and selects a useful light source and shooting angle to produce evenly lit images. The purpose of this work is to identify a group of pests and diseases of domestic and garden plants using a mobile application and display the final result on the screen of a mobile device. In this work, data from 38 different classes were used, including diseased and healthy leaf images of 13 plants from plantVillage. In the experiment, Inception v3 tends to consistently improve accuracy with an increasing number of epochs with no sign of overfitting and performance degradation. Keras with Theano backend used to teach architectures

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Mathematical Foundations of Algorithmization of Water Pollution Modeling Processes

“…The general conclusion about the state of the system will depend on the solution of many tasks, but in general it can be described as the processing of each type of information over a period of time [t 1 , t 2 ]. That is, on the basis of the totality of all objects of system S, a sample is formed describing the behavior of the system over time periods (Alpyssov et al, 2023).…”

“…If we imagine that the information [t1, t2]. That is, on the basis of the totality of all objects of system S, a sample is formed describing the behavior of the system over time periods (Alpyssov et al, 2023).…”

“…It turns out to be expedient to introduce into consideration the probability of the criterion falling into the critical has the form [t1, t2]. That is, on the basis of the totality of all objects of system S, a sample is formed describing the behavior of the system over time periods (Alpyssov et al, 2023).…”

“…Such programs are based on algorithms describing the processes occurring in large masses of water, operations are performed with large data that are difficult to collect and process in the study of small bodies of water. In addition, when studying a small reservoir as a separate biota, it is necessary to consider many disparate factors (Alpyssov et al, 2023), obtained, among other things, as a result of paleontological studies, combining them into a complex system, while subjecting or refuting various hypotheses based on statistical data and the results of field experiments Orazbayev et al, 2018). That is, in this case, it is necessary to algorithmically describe the processes that make it possible to simulate the ecological development or degradation of biota.…”