The use of machine learning methods in the diagnosis of diseases of crops

Abstract

The approach to solving the problems of diagnosis and prognosis of diseases of agricultural crops using machine learning methods is described. To solve the problem of forecasting diseases of agricultural crops, it is proposed to use a genetic algorithm in the work. The analysis of the effectiveness of the proposed method is carried out depending on the convergence rate of such parameters as the mutation coefficient and population size. To solve the problem of diagnostics of agricultural crops, it is proposed to use a recurrent type of neural network. A software modelling complex has been developed that allows solving the problems of plant diseases diagnostics and making forecasts. The results obtained can reduce the costs of agricultural enterprises by reducing the cost of diagnosing agricultural diseases.

Mathematical Modeling, Analysis and Evaluation of the Complexity of Flight Paths of Groups of Unmanned Aerial Vehicles in Aviation and Transport Systems

“…Next, we worked with these data. In addition, to check the quality of our models and their compliance with experimental data, we used data on drone flights obtained in the sources [35][36][37][38][39][40][41][42].…”

Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

“…We proposed using the Viola-Jones algorithm at the first stage of processing the image from a video camera, which, unlike convolutional neural networks, works in a real-time mode [58][59][60][61][62][63]. This method was created for recognizing human faces and did not give good results when used to detect potato tubers; however, by selecting preprocessing filters, we achieved a probability of 97%, which corresponds to the results of a convolutional neural network (from 91 to 95% in works on convolutional networks for the last three years) [25][26][27][28][29][30][31].…”

“…The researchers used morphological op- To recognize damaged tubers, we must consider a set of factors related to the conditions in which tubers are selected. Most often, convolutional neural networks (CNNs) are used to solve such problems, which have recently significantly improved their performance [25][26][27][28][29][30][31]. However, as the authors of [32] have shown, convolutional neural networks working with high-resolution images are not intended to be implemented on devices with weak processors.…”

Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network

“…Modern researchers often apply hybrid approaches involving evolutionary (EA), particle swarm optimization (PSO), backtracking search optimization (BSA) algorithms, and neural networks to solve multicriteria optimization problems [17][18][19][20][21][22]. Y. Hu et al [17] presented a hybrid solution combining GA, PSO, and backpropagation neural networks (BPNN) to forecast electric load.…”