Grapes Quality Prediction Using Iot & Machine Learning Based on Pre Harvesting

Main Article Content

Swati Vishal Sinha
B.M. Patil


Minimizing pesticide use, preserving water, as well as enhancing soil health are just a few of the sustainable farming techniques that must be carefully considered while growing grapes of a high calibre. These practices can help preserve the environment and ensure the longevity of the vineyard. However, it is difficult for the farmers to find the suitability of the soil and its environment to cultivate grapes with high quality. Thus this research aims to evaluate the fitness of the soil for the fitness of growing quality grapes with the aid of machine learning algorithm. The research was done on Nasik region which is called as the “Grape Capital of India” situated in Maharashtra. Total of 154 villages were considered for the examination and soil specimens were collected and sent to the government testing lab in Maharashtra. The soil characteristics by considering both micro and macro nutrients, and the water characteristics were obtained from the lab. Also the climatic features, quality of the petiole and fruit characteristics were included for creating the dataset. These data was given to six different machine learning algorithm to classify the soil by defining whether the soil is fit for grapes or not. Moreover, this research proposed to analyze the correlation between the nutrients by which the relationship and dependency between the different nutrients and features were considered for defining the grapes quality. Also both the micro and macro nutrients were given equal importance in defining the soil quality suitable for obtaining high quality grapes. Based on the results obtained, Pimpalas Ramche contains more nutrients for the grape to grow more successfully based on samples gathered from different vine yards and the decision tree classifier scores better than any other classifiers among the machine learning algorithms employed in terms of accuracy.

Article Details

How to Cite
Sinha, S. V. ., & Patil, B. . (2023). Grapes Quality Prediction Using Iot & Machine Learning Based on Pre Harvesting. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 275–285.


K. Archana, & K. G. Saranya, “Crop yield prediction, forecasting and fertilizer recommendation using voting based ensemble classifier,” SSRG Int. J. Comput. Sci. Eng, vol. 7, pp. 1-4, 2020.

Prof. Madhuri Zambre. (2016). Automatic Vehicle Over speed Controlling System using Microcontroller Unit and ARCAD. International Journal of New Practices in Management and Engineering, 5(04), 01 - 05. Retrieved from

D. R. Babu, S. Koneru, K. N. Rao, B. S. Kumar, S. Kolati, & N. S. Kumar, “Identifying opportunities to start industries on the food production potential in Telangana and Andhra Pradesh, India,” International Journal of Engineering and Advanced Technology, vol. 8, no. 5, pp. 2189-2193, 2019.

K. N. Ravi Kumar, & S. C. Babu, “Value chain management under COVID-19: responses and lessons from grape production in India,” Journal of Social and Economic Development, pp. 1-23, 2021.

A. Chetia, R. V. Chavan, & S. V. Bharati, “Export profile and trade direction of fresh grapes from India: Markov chain approach,” 2022.

Ravi, G. ., Das, M. S. ., & Karmakonda, K. . (2023). Energy Efficient Data Aggregation Scheme using Improved LEACH Algorithm for IoT Networks. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 174 –. Retrieved from

M. Hafez, A. I. Popov, & M. Rashad, “Integrated use of bio-organic fertilizers for enhancing soil fertility–plant nutrition, germination status and initial growth of corn (Zea mays L.),” Environmental Technology & Innovation, vol. 21, pp. 101329, 2021.

S. Assefa, & S. Tadesse, “The principal role of organic fertilizer on soil properties and agricultural productivity-a review,” Agri Res and Tech: Open Access J, vol. 22, no. 2, pp. 556192, 2019.

S. Thapa, A. Bhandari, R. Ghimire, Q. Xue, F. Kidwaro, S. Ghatrehsamani, & M. Goodwin, “Managing micronutrients for improving soil fertility, health, and soybean yield,” Sustainability, vol. 13, no. 21, pp. 11766, 2021.

Chaudhary, D. S. ., & Sivakumar, D. S. A. . (2022). Detection Of Postpartum Hemorrhaged Using Fuzzy Deep Learning Architecture . Research Journal of Computer Systems and Engineering, 3(1), 29–34. Retrieved from

D. Bekele, & M. Birhan, “The impact of secondary macro nutrients on crop production,” International Journal of Research Studies in Agricultural Sciences, vol. 7, 2021.

G. Valida, & U. Cagasan, “A Review Article on Mineral Nutrition and Fertilizer Management of Cereal Crops,” Eurasian Journal of Agricultural Research, vol. 6, no. 2, pp. 62-73, 2022.

NC Brady and RR Weil, “The Nature and Properties of Soils. Revised 14th ed. Pearson Prentice Hall,” New Jersey, 2008.

C. Jones, and J. Jacobsen, “Plant nutrition and soil fertility,” Nutrient Management Extension Publication 4449-2. Montana State University, 2001.

J. M. Blumenthal, D. D. Baltensperger, K. G. Cassman, S. C. Mason, & A. D. Pavlista, “Importance and effect of nitrogen on crop quality and health,” In Nitrogen in the Environment Academic Press, pp. 51-70, 2008.

K. O. Soetan, C. O. Olaiya, & O. E. Oyewole, “The importance of mineral elements for humans, domestic animals and plants: A review,” African journal of food science, vol. 4, no. 5, pp. 200-222, 2010.

M. L. Verma, J. C. Sharma, & P. S. Brar, “Nutrient management in vegetable crops in Himachal Pradesh,” International Journal of Farm Sciences, vol. 10, no. 2, pp. 96-107, 2020.

Thompson, A., Walker, A., Rodriguez, C., Silva, D., & Castro, J. Machine Learning Approaches for Sentiment Analysis in Social Media. Kuwait Journal of Machine Learning, 1(4). Retrieved from

Z. Zhao, C. Chu, D. Zhou, Z. Sha, & S. Wu, “Soil nutrient status and the relation with planting area, planting age and grape varieties in urban vineyards in Shanghai,” Heliyon, vol. 5, no. 8, pp. e02362, 2019.

S. Mohammed, K. Alsafadi, G. O. Enaruvbe, & E. Harsányi, “Assessment of soil micronutrient level for vineyard production in southern Syria,” Modeling Earth Systems and Environment, pp. 1-10, 2021.

R. Vijayakumar, A. Arokiaraj, & P. M. D. Prasath, “Macronutrient and micronutrients Status in relation to soil characteristics in South-East coast plain-riverine Soils of India,” Oriental Journal of Chemistry, vol. 27, no. 2, pp. 567, 2011.

K. Archana, & K. G. Saranya, “Crop yield prediction, forecasting and fertilizer recommendation using voting based ensemble classifier,” SSRG Int. J. Comput. Sci. Eng, vol. 7, pp. 1-4, 2020.

A. Ali, S. Ali, M. Husnain, M. M. Saad Missen, A. Samad, & M. Khan, “Detection of deficiency of nutrients in grape leaves using deep network,” Mathematical Problems in Engineering, 2022.

M. A. Olego, M. J. Quiroga, M. Sánchez-García, M. Cuesta, J. Cara-Jiménez, & J. E. Garzón-Jimeno, “Effects of overliming on the nutritional status of grapevines with special reference to micronutrient content,” OENO One, vol. 55, no. 2, pp. 57-73, 2021.

Gabriel Santos, Natural Language Processing for Text Classification in Legal Documents , Machine Learning Applications Conference Proceedings, Vol 2 2022.