Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System

Table 1

Review of existing technologies.

S/N	Author(S)	Year	Problem	Method	Contribution
1	Priya et al. [3]	2018	This work was concerned with the use of the random forest algorithm to generate predictions for crop yield and improvement.	The random forest algorithm was used for yield production using a dataset with four features or parameters. A training set as used to train the algorithm rules which were then applied to the remaining datasets.	The results showed that we can attain an accurate crop yield prediction using the random forest algorithm.; Random forest algorithm achieves a largest number of crop yield models with lowest models. It is suitable for massive crop yield prediction in agricultural planning.
2	Jeong et. al. [4]	2016	This work aimed at examining the performance efficiency of the random forest algorithm in crop yield prediction for the wheat crop, potato crop, and maize crop.	The random forest algorithm was used to train the datasets, and the same datasets were applied to an MLR model as a benchmark for the random forest algorithm.	The work showed that the random forest algorithm is far more effective in crop yield prediction.
3	Liakos et. al. [2]	2018	This work involved a research into the use of machine learning agricultural production systems.	This work applied artificial neural networks.	This work showed that machine learning models have been used in several agriculture-related areas. Mainly in crop production and aiding management decision making processes.
4	Ming et. al. [5]	2016	This work involved classification of land cover based on image and remote sensing.	Random forest machine learning algorithm was used in the classification of image data.	Random forest is an efficient classification algorithm and performs effectively without using special selected features.
5	Nitze et al. [6]	2012	This work compared the effectiveness of several machine learning algorithms: support vector machine, artificial neural networks, and random forest.	several classifiers, Naïve Bayes for ML, random forest (RF), multilayer perceptron in case of ANN, and LibSVM for support vector machine, were used in this work for the classification of crops.	Even though classification results depended strongly on the number of images used, the SVM classifiers performed much better than the RF and ANN in most of the cases.
6	Chen and Cournede [7]	2017	This work focused on finding the most efficient way to predict the yield of corn based on meteorological records.	This work studied a new methodology named multiple scenarios parameter estimation and used the CORNFLO model.	Random forest regression was shown to be the most efficient for crop yield prediction.
7	Mitra et al. [8]	2017	This work focused on simulating and predicting crop yield for effective crop management and adequate results.	A three-layered artificial neural network (ANN) and R language were used in this work for prediction and simulation of crop yield.	The artificial neural network was effective for simulation and prediction.