Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Abstract

Agriculture contributes a significant amount to the economy of India due to the dependence on human beings for their survival. The main obstacle to food security is population expansion leading to rising demand for food. Farmers must produce more on the same land to boost the supply. Through crop yield prediction, technology can assist farmers in producing more. This paper's primary goal is to predict crop yield utilizing the variables of rainfall, crop, meteorological conditions, area, production, and yield that have posed a serious threat to the long-term viability of agriculture. Crop yield prediction is a decisionsupport tool that uses machine learning and deep learning that can be used to make decisions about which crops to produce and what to do in the crop's growing season. It can decide which crops to produce and what to do in the crop's growing season. Regardless of the distracting environment, machine learning and deep learning algorithms are utilized in crop selection to reduce agricultural yield output losses. To estimate the agricultural yield, machine learning techniques: decision tree, random forest, and XGBoost regression; deep learning techniques -convolutional neural network and long-short term memory network have been used. Accuracy, root mean square error, mean square error, mean absolute error, standard deviation, and losses are compared. Other machine learning and deep learning methods fall short compared to the random forest and convolutional neural network. The random forest has a maximum accuracy of 98.96%, mean absolute error of 1.97, root mean square error of 2.45, and standard deviation of 1.23. The convolutional neural network has been evaluated with a minimum loss of 0.00060. Consequently, a model is developed that, compared to other algorithms, predicts the yield quite well. The findings are then analyzed using the root mean square error metric to understand better how the model's errors compare to those of the other methods.

A Review of State-of-Art Machine Learning Algorithms Used in Agricultural Yield Prediction

“…Learning", used machine learning techniques, such as decision trees, random forests, and XGBoost regression, as well as deep learning techniques, such as convolutional neural networks and long-short term memory networks, to estimate agricultural productivity [22].…”

Pengembangan Sistem Prediksi Waktu Penyiraman Optimal pada Perkebunan: Pendekatan Machine Learning untuk Peningkatan Produktivitas Pertanian

“…Di banyak daerah, sistem penyiraman pada perkebunan masih dilakukan secara manual dan tradisional. Petani sering kali hanya mengandalkan pengalaman dan pengetahuan mereka dalam menentukan waktu dan jumlah air yang diperlukan untuk menyiram tanaman (Sharma et al, 2021;Sharma et al, 2023). Pendekatan ini tidak efisien dan dapat menyebabkan pemborosan air serta penurunans produktivitas pertanian.…”