Abstract

The smart farming is to deliver solutions that are revolutionary to the question of how humankind can continue to exist in a sustainable manner over the long stretch of time. Identification of the recorded image is absolutely necessary in order to monitor the development of the plant and protect it from various diseases and pests. Currently, the objective of automatic disease recognition is to conduct research on crop diseases through the use of deep learning. However, existing classifiers have problems with a variety of challenges, including the identification of appropriate disease categories, among other things. This page is dedicated to the disease that specifically affects tomatoes as a crop, which is known as tomato disease. The purpose of this research is to improve the structure of tomato plant photographs for the purpose of image identification. Because of this, the process of extracting features from photographs of plants is more effective and precise than the approach that is typically taken in artificial recognition. Using three separate sets of photographs recorded by a camera and a drone, the effectiveness of the proposed architecture was evaluated. These images were taken in three different environments where tomatoes are grown. Taking into consideration the statistics, this method of counting articles achieves an accuracy rate of approximately 96.30% on average. The decision-making process in precision agriculture is aided by the scientific support and reference it receives.

Authors

R. Swathi, K. Swasthika
Amal Jyothi Engineering College, India

Keywords

Leaf Disease, CNN, MobileNets