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India is a diversified country, of that more than 70 % of the population's main income depends on agriculture and farming. The large range of diversified corps provides farmers with an opportunity to select from and find suitable pesticides for the plants. But the various plant diseases will cause a significant reduction in the quality and productivity of agricultural products. Plant productivity, health, and disease monitoring play an important role in agriculture and farming. The advanced image processing techniques have made it possible to keep these plant diseases in check. The major plant disease symptoms can be detected in the various plant parts such as leaves, fruits, and stems but the majority of these plant disease symptoms are considered to be detected from the leaves. These may occur due to a change in climate and pollution. To extend a helping hand for farmers in making the identification of diseased plants easier, we have come up with an autonomous robot ‘AGRIBOT’ where it tends to move around the farm and captures the image of the plant. The image is captured when a command is given to the pi. Though there are many researches working related to plant disease detection, the use of ‘AGRIBOT’ makes us unique as it goes around the field and detects the disease. In this paper, we propose an intelligent deep learning approach to identify plant disease, which have been used or investigated for estimating or measuring disease severity. With the help of Convolutional Neural Networks (CNN) and VGG16 Architecture, it can identify 38 different plant disease classes. The accuracy of the proposed increased to 99.44. After classifying healthy and unhealthy plants. Nowadays, they used to refer the chemicals to protect from plant diseases. We turn to the most benign and natural forms of control.