Abstract:

Agriculture is undoubtedly one of the largest sectors of India and is considered as the backbone of India. The country's Gross Domestic Product is also significantly influenced by the agriculture industry. Yet, the yield of crops per hectare in relation to global norms is unsatisfactory. This is one of the likely reasons why marginal farmers in India commit suicide at a higher rate. Two of the main reasons for the failure of achieving required yield are plant diseases and lack of knowledge about the most suitable crop for the soil that is available. The model presented in this was created using cutting-edge technical techniques like Deep Learning, Machine Learning, and CNN. Early plant disease diagnosis is one of the features that our application can perform, and it is implemented using different methods. The evaluation revealed that convolutional neural networks were more effective at reliably identifying plant diseases. A crop recommendation system is also included in the model which is based on appropriate parameters such as rain, temperature, soil structures, and crop diseases. India is a bilingual nation; hence the programme was created to streamline language interpretation in ten different languages using Google Translate API.

Description:

Machine Learning, Deep Learning, Convolutional Neural Networks, Plant Disease Detection, Crop Recommendation, Google Translate API.