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This paper mainly deals with the design and analysis of Agriculture data using Machine Learning (ML) techniques. Agriculture includes cultivating the soil and we can grow different types of crops. In India there are 28 states and 8 Union Territories. In each and every state people are farming and they are developing our agriculture in so many ways. Every state has its own farm for example: - Andhra Pradesh-Rice, Bazra, maize, ragi, pulses etc; the other name for agriculture is "Back Bone of India ". The sediment pressure is due to erosion and soil loss. Diffusion in nature occurs in the fine-grained sediments. Statistical analysis is being carried out for effective solution based on different parameters. This helps to get more information for farmers to make critical farming decisions. Researchers focused on agriculture and found an optimal option to increase production. Experimental data is analyzed and the production is optimized for agriculture such that it is more resilient to climatic change. [ABSTRACT FROM AUTHOR]

Indian economy is contributed heavily by agriculture. Most of the Indian farmers rely on their instincts to decide the crop to be sown at a particular time of year. They do not realize that the crop output is circumstantial, and depended heavily on the present-day weather and soil conditions. A single uninformed decision by the farmer can have undesirable consequences on the economic conditions of the region and also mental and financial impacts on the farmer himself. Applying systematic Machine Learning models will effectively help to alleviate this issue. The dataset used in the paper is built by adding up the datasets of India's rainfall, climate, and fertilizer. Machine learning models will be used on the dataset to get the highest accuracy model to recommend a crop for the farm's location. This recommendation will help the farmers in India to make learned decisions about the crops. The recommendation will take into account the parameters like the farm's location, sowing season, soil properties, and climate. [ABSTRACT FROM AUTHOR]

Identification and classification of plant diseases is a major field of study, as most people in India depend on agriculture for their main source of income and food. This is one of the reasons why the identification of plant diseases plays a significant role in agriculture. It is useful to detect plant disease through any automated technique as it eliminates a significant amount of monitoring work in large crop farms and detects the symptoms of diseases at a very early stage, i.e. when they appear on plant leaves. The framework mainly involves different concepts related to image processing, such as image acquisition, image pre-processing, and extraction of features, database formation, and artificial neural network classification. This paper covers research on various methodologies for the use of neural networks to detect plant leaf diseases. [ABSTRACT FROM AUTHOR]