Абстрактный

Analysis of Neural Network and Hybrid Techniques for Plants classification

Priya Dhir, Jasmeen Gill

Today, computer science is increasingly involved in agricultural and food sciences. Various artificial intelligence and soft computing techniques are used to classify plants and detect defects to provide a better quality product to the final consumer. This article focuses on advances in automatic plant classification using soft computing techniques. Various ANN, CNN, PNN as well as Heuristic and meta heuristic optimization techniques are reviewed for plants classification. There are several meta-heuristic optimization algorithms developed on inspiration from nature. The review of Neural networks like ANN, CNN, PNN as well as some of the hybrid artificial neural networks with optimization methods like Genetic Algorithm (GA), Ant Bee Colony (ABC), Differential Evolution (DE), Group Search Particle Swarm Optimization (GSPSO), Firefly method, etc. are applied for benchmark data sets and to specific real-time experiments for plants classification are discussed.

Automatic plants classification, Artificial neural network, PNN, CNN meta-heuristic optimization algorithms, Classification