Crop Yield Prediction using Machine Learning and Deep Learning Techniques

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Venkata Rama Rao Kolipaka, Anupama Namburu


Crop yield prediction has been designated as a major predictive analysis technique that increases the potential of the agricultural industry. The utilisation of such a measure has been important for the farmers to understand the yields of crops during the particular season from a data analytical point of view. Such an aspect has fallen under the concept of predictive analysis which allows the farmers, agricultures, and farming businessmen to make strategic decisions in terms of cultivation. The application of predictive analysis has been useful for understanding the specific set of crops to be sown during the season, and the types of fertilisers to be applied to the crops for an increased output. Risk analysis with the help of predictive modelling of crops helps in the improvement of the overall agriculture business and increases the potential of the farmers to improve their revenue collection. Once they have the potential of understanding the specific parameters of agriculture, the decision making for reducing the risks, increasing the overall gain from the crops, and such other aspects can be easily known. Predictive analysis allows the farmers to gain an expansive amount of knowledge regarding the weather conditions in the future, the quality of the soil for growing the crops, the nutrients required which are to be replenished for increasing the crop field, and several such parameters. Machine Learning or ML and Deep Learning or DL methods have been seen to be extremely important for data analysis and predictions. Several kinds of tools and techniques such as neural networking, Bi GRU, Maxout classifiers, and others have been applied within the agricultural industry. The study would lead to an extensive analysis of the different kinds of Machine Learning and Deep Learning techniques used for increasing the crop yields by prediction analysis. Such a measure would prove to be extremely important to make significant decisions regarding the importing and exporting of crops, and the pricing structure for the grains to be sold in the market. The distribution of crops and also making fruitful decisions regarding future crop plantations can also be inspected with the help of the ML and DL tools.

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How to Cite
Venkata Rama Rao Kolipaka, et al. (2023). Crop Yield Prediction using Machine Learning and Deep Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 582–589.
Author Biography

Venkata Rama Rao Kolipaka, Anupama Namburu

Venkata Rama Rao Kolipaka1, Anupama Namburu2

1School of Computer Science and Engineerig

VIT-AP University

Amaravati, Andhra Pradesh 522237, India

2School of Computer Science and Engineering

VIT-AP University

Amaravati, Andhra Pradesh 522237, India


Alfred, R., Obit, J. H., Chin, C. P. Y., Haviluddin, H., & Lim, Y. (2021). Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks. IEEE Access, 9, 50358-50380.

Ang, K. L. M., &Seng, J. K. P. (2021). Big data and machine learning with hyperspectral information in agriculture. IEEE Access, 9, 36699-36718.

Bhanumathi, S., Vineeth, M., &Rohit, N. (2019, April). Crop yield prediction and efficient use of fertilizers. In 2019 International Conference on Communication and Signal Processing (ICCSP) (pp. 0769-0773). IEEE.

Bhatnagar, R., &Gohain, G. B. (2020). Crop yield estimation using decision trees and random forest machine learning algorithms on data from terra (EOS AM-1) & Aqua (EOS PM-1) satellite data. Machine Learning and Data Mining in Aerospace Technology, 107-124.

Chandraprabha, M., &Dhanaraj, R. K. (2020, November). Machine learning based Pedantic Analysis of Predictive Algorithms in Crop Yield Management. In 2020 4th International conference on electronics, communication and aerospace technology (ICECA) (pp. 1340-1345). IEEE.

Elavarasan, D., & Vincent, P. D. (2020). Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE access, 8, 86886-86901.

Feng, S., Zhao, J., Liu, T., Zhang, H., Zhang, Z., &Guo, X. (2019). Crop type identification and mapping using machine learning algorithms and sentinel-2 time series data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9), 3295-3306.

Gadiraju, K. K., &Vatsavai, R. R. (2023). Application of Transfer Learning in Remote Sensing Crop Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Jain, S., & Ramesh, D. (2020, February). Machine Learning convergence for weather based crop selection. In 2020 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-6). IEEE.

Kale, S. S., &Patil, P. S. (2019, December). A machine learning approach to predict crop yield and success rate. In 2019 IEEE Pune Section International Conference (PuneCon) (pp. 1-5). IEEE.

Katarya, R., Raturi, A., Mehndiratta, A., &Thapper, A. (2020, February). Impact of machine learning techniques in precision agriculture. In 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE) (pp. 1-6). IEEE.

Kondaveti, R., Reddy, A., &Palabtla, S. (2019, March). Smart irrigation system using machine learning and IOT. In 2019 international conference on vision towards emerging trends in communication and networking (ViTECoN) (pp. 1-11). IEEE.

Kumar, Y. J. N., Spandana, V., Vaishnavi, V. S., Neha, K., & Devi, V. G. R. R. (2020, June). Supervised machine learning approach for crop yield prediction in agriculture sector. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 736-741). IEEE.

Li, Z., Chen, G., & Zhang, T. (2019). Temporal attention networks for multitemporalmultisensor crop classification. Ieee Access, 7, 134677-134690.

Medar, R., Rajpurohit, V. S., &Shweta, S. (2019, March). Crop yield prediction using machine learning techniques. In 2019 IEEE 5th international conference for convergence in technology (I2CT) (pp. 1-5). IEEE.

Metzger, N., Turkoglu, M. O., D’Aronco, S., Wegner, J. D., & Schindler, K. (2021). Crop classification under varying cloud cover with neural ordinary differential equations. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-12.

Militante, S. V., Gerardo, B. D., & Medina, R. P. (2019, October). Sugarcane disease recognition using deep learning. In 2019 IEEE Eurasia conference on IOT, communication and engineering (ECICE) (pp. 575-578). IEEE.

Muniasamy, A. (2020, September). Machine learning for smart farming: a focus on desert agriculture. In 2020 International Conference on Computing and Information Technology (ICCIT-1441) (pp. 1-5). IEEE.

Nejad, S. M. M., Abbasi-Moghadam, D., Sharifi, A., Farmonov, N., Amankulova, K., &Lászl?, M. (2022). Multispectral crop yield prediction using 3D-convolutional neural networks and attention convolutional LSTM approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 254-266.

Nosratabadi, S., Szell, K., Beszedes, B., Imre, F., Ardabili, S., &Mosavi, A. (2020, October). Comparative analysis of ANN-ICA and ANN-GWO for crop yield prediction. In 2020 RIVF International Conference on Computing and Communication Technologies (RIVF) (pp. 1-5). IEEE.

Pallagani, V., Khandelwal, V., Chandra, B., Udutalapally, V., Das, D., &Mohanty, S. P. (2019, December). DCrop: A deep-learning based framework for accurate prediction of diseases of crops in smart agriculture. In 2019 IEEE international symposium on smart electronic systems (iSES)(formerly inis) (pp. 29-33). IEEE.

Pande, S. M., Ramesh, P. K., ANMOL, A., Aishwarya, B. R., ROHILLA, K., & SHAURYA, K. (2021, April). Crop recommender system using machine learning approach. In 2021 5th international conference on computing methodologies and communication (ICCMC) (pp. 1066-1071). IEEE.

Priyadharshini, A., Chakraborty, S., Kumar, A., &Pooniwala, O. R. (2021, April). Intelligent crop recommendation system using machine learning. In 2021 5th international conference on computing methodologies and communication (ICCMC) (pp. 843-848). IEEE.

Reddy, K. S. P., Roopa, Y. M., LN, K. R., &Nandan, N. S. (2020, July). IoT based smart agriculture using machine learning. In 2020 Second international conference on inventive research in computing applications (ICIRCA) (pp. 130-134). IEEE.

Sadeghiravesh, M. H., Khosravi, H., Abolhasani, A., Ghodsi, M., &Mosavi, A. (2021). Fuzzy logic model to assess desertification intensity based on vulnerability indices. ActaPolytech. Hung, 18, 7-24.

Shadrin, D., Menshchikov, A., Somov, A., Bornemann, G., Hauslage, J., &Fedorov, M. (2019). Enabling precision agriculture through embedded sensing with artificial intelligence. IEEE Transactions on Instrumentation and Measurement, 69(7), 4103-4113.

Shafi, U., Mumtaz, R., Iqbal, N., Zaidi, S. M. H., Zaidi, S. A. R., Hussain, I., &Mahmood, Z. (2020). A multi-modal approach for crop health mapping using low altitude remote sensing, internet of things (IoT) and machine learning. IEEE Access, 8, 112708-112724.

Sharma, A., Jain, A., Gupta, P., &Chowdary, V. (2020). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9, 4843-4873.

Sun, J., Lai, Z., Di, L., Sun, Z., Tao, J., &Shen, Y. (2020). Multilevel deep learning network for county-level corn yield estimation in the us corn belt. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5048-5060.

Van Klompenburg, T., Kassahun, A., &Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709.

Yu, W., Li, J., Liu, Q., Zhao, J., Dong, Y., Wang, C., ...& Zhang, H. (2021). Spatial–temporal prediction of vegetation index with deep recurrent neural networks. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.