Authors - Sandeep M.Chaware, Mohit Matte, Pratik Dahagaonkar, Anurag Deotale, Laukik Pagar, Jayesh Sarwade Abstract - Agriculture is the land-cultivation, crop-growing, livestock-raising processes. A nation's economic growth depends on its agricultural sector. Agriculture makes for about 58% of a nation's primary revenue source. Up to now, farmers sow and cultivate or practice agriculture based on favorable weather and soil conditions without considering the future supply and demand of crops and the type of agriculture practiced, thus often doing reduce profits from agriculture. Typically, when demand for a crop is low and supply is high, the price drops too low, leading to debt for the farmer and vice versa. Predicting what crops should be grown or what type of agriculture should be adopted in today's world is essential to meet people's needs and increase farmer productivity. Machine learning, data mining, and data analytics can be used to collect data, train models, and predict the market demand, supply chain, demanding type of agriculture and location of agriculture for revenue generating agriculture. This will help reduce losses for farmers. Due to the ongoing changes in the world, the proposed Machine Learning assistanat helps determine how to manage agriculture intelligently. It assists an individual towards profitable agriculture This work's primary goal is to sustain a single farm profitably while achieving high output at reasonable expenses. Questions including pricing comparisons, government activities, plant protection, animal husbandry, weather, and fertilizer management are addressed by the proposed method.
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