Comparative Study of Plant Leaves Detection for Precision Agriculture using Machine Learning Techniques
Year: 2025
Type: conference-paper
Abstract: Crop production is an important factor in a country’s economic growth. The production of plants is greatly reduced by plant illnesses, which further decrease the quality of plants while charging farmer’s funds. The different section of a plant exhibit symptoms of plant diseases, however leaves are the most frequently seen area for spotting an infection. Extensive studies are conducted to perform precision agriculture and aid in the premature identification of phytopathology’s. This paper shows the comparative analysis for identifying healthy & unhealthy plant leaves images. The proposed methodology is a multi-step process tends to select optimal subset of features using nature inspired algorithm such as Binary Bat Algorithm (BBA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). These features are utilized in classification method such as Artificial Neural Networks (ANN) and Support Vector Machine (SVM) for predictive analysis. The findings show that obtained result analysis is efficient and determines the predictive analysis.
Source: Procedia Computer Science
Authors: Aishwarya Churamani, Aakanksha Singh, Ayushman Sengar, Pratush Bhandari, Surbhi Vijh, Sumit Kumar, Amaan Shahid
Institution: Amity University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4410300296