In general, the innovative foods produced on fruit and vegetable based farms are always high quality and healthy. Indicators of fruit and vegetable consumption include plasma vitamin C and arytenoids, which are plant pigments discovered in blood samples. The researchers decided to utilize blood samples rather than the more common food frequency questionnaire in their investigation. to assess the amount of food consumed in order to forestall measuring errors and to establish dependencies. Because vitamin C and arytenoids may be found in a wide variety of fruits and vegetables, we can use them as objective measures of our consumption of these food groups. The fact that individuals who do not consume a diet that is abundant in fruits and vegetables do not consume significant quantities of vitamin C and arytenoids is reflected in the plasma levels of these individuals. In this paper a smart machine learning algorithm was proposed to predict the micro plasma impacts. This monitors the regular shape and harvesting of different farm fresh products and predicts the impacts of it. This will helpful for farmers to enhance the harvesting.

A significant part of sedentary human civilization is agriculture. The crop yield will grow with the correct kind of crop planted. Systems for making recommendations can be made using a variety of machine learning techniques. According to the agricultural criteria, crop recommendations are made. With the aid of data science, a suggestion system can be given to the farmer to help him plant crops. Numerous resources might be lost in the event of quality prediction. We have applied the Light GBM Machine Learning Algorithm to address this flaw in the current system and enhance its accuracy and dependability. Based on the analysis of data sets and consideration of environmental elements and soil nutrient concentration, crop recommendations were developed. The recommendation system trustworthy. Certain measurable data, including temperature, humidity, rainfall, pH level, and soil nutrient content (N, P, and K), are taken into account in this particular kind of recommendation system. Using a third-party API, the environmental variables temperature, humidity, and air pressure are discovered. By offering solutions to crop disease predictions and offering appropriate fertilizer recommendations, this data assists in providing an accurate prediction about suitable crop growth.