Abstract:
Plants are affected by many diseases, which farmers find challenging to control. When
these diseases are classified at an early stage, it will be easy for the farmers to detect the
disease and control it with less expense. This research addresses the problem by
introducing an adaptive mobile-based application, that enhances precision agriculture
by classifying and predicting plant leaf diseases in real-time. The objective of this
research is to develop a Convolutional Neural Network (CNN) based model that
leverages image augmentation techniques and MobileNet architectures. This project
develops a CNN model, using a dataset containing images of plant leaves from various
crops such as potatoes, beans, corn, wheat and pepper. The research methodology
encompasses data collection, image augmentation, training, and transfer learning,
whereas fine-tuning is also employed to optimize the model’s performance. The CNN
model is deployed in a real-time mobile application developed using the Flutter
framework, enabling users, particularly farmers to capture and analyse the plant leaf
images on-site. Comparative analysis with other machine learning models including K
Nearest Neighbours, Naïve Bayes, Random Forest, Support Vector Machine,
and Decision Tree highlights the superiority of the CNN model based on the evaluation
metrics. Overall, this research empowers the advancement of precision agriculture and
provides a dynamic solution by delivering a practical tool for plant leaf disease
management in real time which offers a significant contribution to agricultural
technology and sustainable farming practices.
