Plant Diseases Classification Using Feature Reduction, BPNN and Pso

Abstract

Agriculture is the culture of land and rearing of the plants to provide food for the nourishment and enhancement of life. In India, it is one of the fundamental financial sources; various sorts of plants are cultivated each year. There are various microorganisms that cause many plant diseases and impede normal plant growth. That is the reason from long ago which led researchers to search for new methods of classification of plant diseases. Although there are different neural networks have already been used for plant disease classification, but using these methods alone do not create the best tradeoff between time and precision. So to remove this constraint, we proposed a method for plant disease classification based on Back-propagation Neural Network (BPNN) and Particle Swarm Optimization (PSO). Now we have added some more data to our dataset and applied principal component analysis to reduce the number of total features and on these features we have applied BPNN with PSO. We first train neural network using back-propagation and then we further use PSO to get more optimized weights or fine-tune the parameters of neural network. In our experiment, we have used images of leaves that are infected by various bacterial and fungal diseases: Alternaria alternata, Anthracnose, Bacterial blight, Bacterial leaf scorch, Cercospora leaf spot, and Downy mildew, and our proposed method achieves approximately 96.42 % precision.

Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)

“…Once the researchers reverse engineer the executable file using specific tools, they need to select features from the source code. Feature selection is important in order to increase the accuracy of the detection system ( Feizollah et al, 2015 ; Chanda & Biswas, 2019 ; Klaib, Sara & Hasan, 2020 ) Figure 5 presents the taxonomy of multiple static features. The next sections are the details for each type of static feature.…”