Machine Learning for Precision Agriculture: Predictive Analysis of Crop Growth Frequencies


  • Niketa Kadam, Raj Mishra, Vishal Shirsath


Plant Growth Patterns, Green Area Calculation, Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Sound Frequency


By combining cutting-edge machine learning techniques with the examination of plant development patterns, presented research employs an innovative dual methodology for precisely calculating green areas, i.e., plant growth. One unique aspect of the research is the correlation between plant development and specific musical frequencies, which span from 1 to 10 kHz and include pop, classical, and Normal. By utilizing support vector machines (SVM) and artificial neural networks (ANN), the dual method improves our comprehension of the dynamics of plant development. Interestingly, the study shows that SVM performs better than ANN, offering more accuracy in predicting green areas. This sophisticated approach shows how fusing state-of-the-art neural networks with conventional machine learning may revolutionize the field and change the course of precision agriculture. The study highlights the complementary nature of contemporary and traditional methods, demonstrating their effectiveness in providing a thorough understanding of plant development and a productive assessment of green areas. SVM's astounding accuracy levels up to 92.10% highlight the significance of this technology in the advancement of precision farming practices. The stability and applicability of proposed strategy are emphasized, especially in light of accurate and successful agricultural management techniques. Three plant species were watched over the course of three months for this study, giving the results a strong real-world component.


Download data is not yet available.


Dhanusha, Jannu & Reddy, Beeram & Yuvasree, Avidi & Sravanthi, Kolusu & Bandi, Surendra. (2023). Recommendation System to Precision Agriculture Using Machine Learning Algorithm. Journal of Data Mining and Management. 8. 19-32. 10.46610/JoDMM.2023.v08i03.003.

Patil, Sagar & Kulkarni, R. & Kharade, Pramod & Patil, Suchita. (2023). Review of Machine Learning Model Applications in Precision Agriculture. 105. 916-930. 10.2991/978-94-6463-136-4_81.

Ed-Daoudi, Rachid & Alaoui, Altaf & Ettaki, Badia & Zerouaoui, Jamal. (2023). A Machine Learning Approach to Identify Optimal Cultivation Practices for Sustainable apple Production in Precision Agriculture in Morocco. E3S Web of Conferences. 469. 10.1051/e3sconf/202346900052.

Mistry, Vishakha & Mishra, Abhishek & Ahmed, Nadiyah. (2023). Machine Learning Use Case in Indian Agriculture: Predictive Analysis of Bihar Agriculture Data to Forecast Crop Yield. International Journal for Research in Applied Science and Engineering Technology. 11. 1004-1009. 10.22214/ijraset.2023.48709.

Jackson, Majwega & Marvin, Ggaliwango & Chakrabarty, Amitabha. (2022). Robust Ensemble Machine Learning for Precision Agriculture. 10.1109/ICISET54810.2022.9775879.

Rajendiran, Gowtham & Rethnaraj, Jebakumar. (2024). IoT-Integrated Machine Learning-Based Automated Precision Agriculture-Indoor Farming Techniques. 10.4018/979-8-3693-0639-0.

Kakade, Suhas & Kulkarni, Rohan & Dhawale, Somesh & C, Muhammed. (2023). Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection. Green Intelligent Systems and Applications. 3. 86-97. 10.53623/gisa.v3i2.313.

Musanase, Christine & Vodacek, Anthony & Hanyurwimfura, Damien & Uwitonze, Alfred & Kabandana, Innocent. (2023). Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices. Agriculture. 13. 2141. 10.3390/agriculture13112141.

Reddy, Vutukuru & Varshini, Vanmalli & Namineni, Gireesh & Naresh, M. (2023). A Comprehensive Study on Crop Recommendation System for Precision Agriculture Using Machine Learning Algorithms. Electrical and Automation Engineering. 2. 30-36. 10.46632/eae/2/1/5.

Zhang, Ying. (2023). Simulation of Crop Planting Decision System Based on “U + CSA” Public Welfare Agriculture and Machine Learning Algorithm. 10.1007/978-981-99-5203-8_14.

Neubürger, Felix & Arens, Joachim & Kopinski, Thomas & Hermes, Matthias. (2023). Development of a Demonstator Plant for Hot Stamping of Metal Sheets with a Machine Learning Assisted Anomaly Detection Control System. 10.1007/978-3-031-40920-2_28.

Saravanakumar Venkatesan, Jonghyun Lim, Yongyun Cho, "A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms", Computational Intelligence and Neuroscience, vol. 2022, Article ID 2648695, 18 pages, 2022.

Parate, Rajesh & Dhole, K. & Sharma, S.. (2023). Classification of Leaf using Teachable Machine. International Journal for Research in Applied Science and Engineering Technology. 11. 307-311. 10.22214/ijraset.2023.55629.

Walsh, Ian & Fishman, Dmytro & Garcia-Gasulla, Dario & Titma, Tiina & group, The & Harrow, Jen & Psomopoulos, Fotis & Tosatto, Silvio. (2020). Recommendations for machine learning validation in biology.


Suresh Bhadane, Dinesh & Patil, Suvarna & Bhandari, Abhay & Mahajan, Danish & Katoch, Ajay & Abrol, Naman. (2023). Plant Leaf Recognition Using Machine Learning: A Review. 2395-0056.

Jaramillo-Alcázar, Angel & Govea, Jaime & Villegas, William. (2023). Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning. Sensors. 23. 8286. 10.3390/s23198286.

Sia, Jayson & Zhang, Wei & Cheng, Mingxi & Bogdan, Paul & Cook, David. (2023). Machine learning general transcriptional predictors of plant disease. 10.1101/2023.08.30.555529.

Rai, Krishna. (2023). Stress phenotyping in plants using arti cial intelligence and machine learning EDITORIAL. Journal of Agriculture and Livestock Farming. 1. 10.61577/jalf.2023.100001.

Deshwal, Pushkar & Sharma, Kaushal & Moudgil, Suveg. (2023). Plant Leaf Disease Detection using Machine Learning. International Journal for Research in Applied Science and Engineering Technology. 11. 5928-5932. 10.22214/ijraset.2023.52895.

Paithankar, Prof & Awari, Ajinkya & Raskar, Akash & Patil, Shrirameshwar & Jamdar, Namrata. (2023). Plant Disease Detection using Machine Learning. International Journal of Advanced Research in Science, Communication and Technology. 267-272. 10.48175/IJARSCT-9297.

Kabour, Shaimaa & Almalki, Raghad & Alghamdi, Lujain & Alharthi, Wujud & Alshagi, Nisreen. (2023). Fault classification and detection for photovoltaic plants using machine learning algorithms. Indonesian Journal of Electrical Engineering and Computer Science. 32. 353. 10.11591/ijeecs.v32.i1.pp353-362.

Upadhya, Nischitha. (2023). Classification and Detection of Plant Disease Using CNN and Machine Learning. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. 07. 10.55041/IJSREM25038.




How to Cite

Raj Mishra, Vishal Shirsath, N. K. . (2024). Machine Learning for Precision Agriculture: Predictive Analysis of Crop Growth Frequencies. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 986–997. Retrieved from



Research Article