International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 11 Issue: 9 Article Received: 25 July 2023 Revised: 12 September 2023 Accepted: 30 September 2023 ___________________________________________________________________________________________________________________ 4307 IJRITCC | September 2023, Available @ http://www.ijritcc.org Tomato Plant Leaf Disease Detection Using Convolutional Neural Network Miss Tejswini S. Danwadkar, Student, Sharad Institute of Technology College of Engineering, Department of AI&DS, Yadrav 416146, India, tejswinisit105@gmail.com Dr. Pushpender Sarao, Professor CSE Department, Sharad Institute of Technology College of Engineering, Department of CSE, Yadrav 416146,India,pushpendersarao@sitcoe.org.in Abstract: Our country is growing country, India is mostly depended on the agriculture field, and Agriculture is pillar of our country’s economy. Because of this India has to improve in agriculture filed, in many of situations’ farmer face difficulties to detect crop disease in a case large area farm, or late in detection of crop disease in such cases farmer lose their all crop and face the huge loss, to avoid this, in agricultural field is detection of plant diseases is very important. And it is very difficult tasks. In regular normal procedure it requires a more time period and trained person to detect accurate plant disease. In this paper, we proposed effective way for plant disease detection using computer science and machine learning model. Disease transmission from unhealthy plant to all other healthy plants in farm is one of the major damages to crop farm. And these diseases spread like forest fire and have the possible to impact the whole operation if not identified in early on. Now Plant disease detection methods helps to identify infected plants in a very early stage and also help us to identify plant disease in a wide range of area of crops in a cost-effective manner. The aim of this project model is to implement machine learning models, in our proposed system we take a plant leaf image on that leaf images we predict the plant disease using Convolution Neural Networks (CNN) model, in that we build a such a model to predict the plant disease with maximum accuracy and it is for plant disease detection for tomato plant. This machine learning model is analyzing different image metrics pixels data to determine the best performance of network. For that dataset were used around 7around 8016 images we going to use to train the model. We going to use 14 layers CNN models to get better accuracy results. Model consist various layers like convolution, pooling, flatten and dense. Early two layers that is preprocessing and augmentation of images Finally, we get the result of which disease that plant have. Keywords: CNN, Pooling, Convolution, Classification, Dense, Flatten, Machine learning, Hypermeters 1.Introduction Agriculture is very important for humans and all living things life. In our traditional system, there is no mechanism to detect diseases on different crops in the agricultural farm that affect the growth of individual species in a farm. With the help of machine learning, we achieve accuracy in this area of research is improving continuously. In India, about 70% of the population is dependent on the agriculture field. Identification of plant diseases is important to prevent crop loss. Identifying plant diseases by hand is extremely slow. It requires a huge amount of work, and expertise in the reorganization of plant diseases and also needs a lot of time. Therefore, image processing and machine learning models can be used to detect plant diseases and it is very beneficial for early detection of plant disease. In this project, we described a technique for detecting plant diseases using leaf images. Image processing is a branch of signal processing that can extract image features or useful information from an image. Machine learning is a branch of artificial intelligence that works automatically or gives instructions to perform a specific task in a model. The main goal of machine learning is to understand training data and fit that training data into designed models that should be useful to humans. In such a way, it can help make good decisions and predict the very right results from large amounts of training datasets. Color of Leaf, how many degree of leaf damage, area of leaf, and texture specifications of leaf are used for classification process. We analyzed different image specifications or features to recognize different plant diseases to achieve the more possible accuracy. Ahead of time, the detection of plant diseases was done by visual inspection of leaves or chemical processes by experts. This requires a large team of experts as well as constant observation of the plants, resulting in high costs for large farms. Under such conditions, the recommended system proves itself in monitoring large crop fields. Automatic disease detection by simply recognizing symptoms on plant leaves makes it easy and cheap. The proposed system is a solution for plant disease detection is computationally less