Agriculture is considered as the main source of income for majority of the population, with almost the whole population depending on it for their livelihood. However, many farmers do not have the complete knowledge about crops and their related diseases. The arrival of new diseases affecting crop yields makes the issue even more difficult to tackle. Proper detection of these diseases can significantly provide benefits to the farmers, helping them to protect their crops and also better productivity. Today, various image processing techniques are being used to analyze crop images and identify the diseases. Manual detection relies on individual expertise, which is sometimes time-consuming and variable in accuracy and not every farmer is able to analyze the diseases. To improve speed and precision, computer-aided techniques are now utilized in the agricultural sector. Deep learning models, specifically Sequential Convolutional Neural Networks (CNN), automatically extracts the features and classifies the images with the help of a fully connected network. In this study, we have trained a Sequential CNN for disease detection and also integrating the Random Forest Algorithm to provide crop and fertilizer recommendations. In addition to that, our system incorporates a chatroom and an e-commerce platform, offering farmers fertilizers, manure, and seeds, providing a comprehensive farming solution. Index Terms Crop Disease Detection, Deep learning in Agriculture, Random Forest Algorithm, Convolutional Neural Networks, E-commerce for Farmers.