An Efficient Plant Disease Detection: Possibility Neutrosophic Hypersoft Set Approach with Whale Optimization Algorithm.

Abdalla Musa, Abdalla Ibrahim; Al-Hagery, Mohammed Abdullah

Indian agriculture aims at achieving sustainable development, which increases crop production per square unit without damaging the ecosystem and natural resources. Timely and prompt diagnosis and analysis of plant diseases are very beneficial in increasing food crop productivity and plant health and decreasing plant diseases. Plant disease specialists are not accessible in distant regions therefore there is an urgent need for reliable, automatic low-cost, and approachable solutions to detect plant disease without the expert's opinion and laboratory inspection. Classical machine learning (ML)-based image classification techniques and Deep learning (DL)-based computer vision (CV) approaches such as Convolutional Neural Networks (CNN) was employed to detect plant disease. Neutrosophic set (NS), a generality of fuzzy set (FS) and intuitionistic FS (IFS), presented to characterize inconsistent, uncertain, imprecise, and incomplete data in realistic conditions. Besides, interval NS (INSs) was exactly proposed to resolve the problems with a collection of numbers in the actual entity. On the other hand, there are high levels of operational reliability for INSs, along with the decision-making method and INS aggregation operators. This study presents an Efficient Plant Disease Detection using the Possibility Neutrosophic Hypersoft Set Approach (EPDD-pNSHSS) method. The suggested EPDD-pNSHSS method uses the DL method for the recognition and classification of plant diseases. Initially, the EPDD-pNSHSS method takes place the Median filtering (MF) through the preprocessing to progress image superiority and eliminate noise. In the meantime, the possibility neutrosophic hypersoft set (pNSHSS) classifier is utilized for the detection of diseased and healthy leaf images. To optimize the detection accuracy of the pNSHSS mechanism, the whale optimization algorithm (WOA) is employed for adjusting the hyperparameter value of the DSAE technique. Wide-ranging experiments are implemented to exhibit the supremacy of the EPDD-pNSHSS method. The empirical findings showcased the development of the EPDD-pNSHSS method over other existing techniques.

PLANT diseases; NEUTROSOPHIC logic; SET theory; SUSTAINABLE development; ALGORITHMS

International Journal of Neutrosophic Science (IJNS), 2025, Vol 25, Issue 2, p11

2692-6148

Academic Journal

10.54216/IJNS.250202