Deep Learning Models for Prediction of Disease in Lycopersicum

In the Indian economy, agriculture is one of the main economic resources. Unemployment is one of the biggest problems. In many areas, agriculture offers about 60% of jobs. Various roles and responsibilities are involved in the agricultural sector including farmers, farm equipment operators, technical specialists, and more. There are a number of opportunities available in the agricultural sector. In developing countries, unemployment has decreased significantly since agriculture accounts for 20% of GDP, and agriculture can yield much greater benefits. Crops are classified into commercial and non-commercial crops. Most people can access and use non-commercial crops in their daily lives. Tomatoes are considered non-commercial crop. The edible berry of the species, Solanum lycopersicum, is commonly known as the tomato plant. Tomato plants are affected by many different diseases and because of these diseases, the losses are heavy. The disease can affect leaf, stem, or tomatoes. Initially, the disease is observed in the leaves, and eventually the disease worsens. Anticipating disease at an early stage is a major concern and therefore preventive measures are taken to obtain good crop quantity and quality. Applying deep learning techniques to disease detection has many advantages over other machine learning techniques. Deep learning is a part of machine learning techniques that helps train computers to do things that are natural to humans. 

Keywords: Convolutional neural network(CNN), Deep learning(DL), Machine learning (ML), Neural network.