Classification of rice leaf diseases using artificial neural network

Abstract

Rice is one of the important food crops for humans. The yield of this crop is affected by several factors, one of them is climate change. The irregularities of climate can cause the crops were infected by various diseases. By knowing the diseases through its unusual characteristics, fast remedial actions could be expected. The purpose of this essay is to find the best artificial neural network (ANN) architecture in classifying brown spot and leaf smut diseases on rice crops, two common types of diseases attack rice plant, based on the leaf images. Two features of rice leaf images i.e. shape and color were extracted to classify both diseases. The result of this research demonstrated the best ANN architecture to classify the diseases is the architecture with 4 neurons in the input layer, 5 neurons in one single hidden layer, and one neuron in the output layer. This architecture has accuracy as much as 66.3 percent.

Artificial neural networks to investigate the bioavailability of selenium nanoparticles in soil–crop systems

“…19 Some researchers examined the classification of rice leaf diseases using attention-based depthwise separable ANN, achieving an accuracy of 96.45%. 20 Rossi et al (2019) used an ANN to identify key physiological factors (i.e., root fresh weight, the net photosynthesis rate, and Fv/Fm ratio) affecting Brassica napus plant uptake of cooccurring Cd and CeO 2 NPs. 18 Although ANN has been applied in agriculture systems, its utilization in investigating the bioavailability of NPs in soil-crop systems is limited.…”