Creating an ML-based Mobile App for Maintaining Crop Health

Agriculture is the backbone of our nation. Not only in India, but the growth of food and other crops is also an essential occupation all over the world. One of the major requirements of great yield in agriculture is the analysis of the soil and the atmosphere. This study aims in finding the best Machine Learning (ML) model that can be used to determine the right fertilizer based on all the factors like environmental conditions and crop requirements. For this purpose, three different ML models were developed using three different algorithms. The algorithms used in this study were the Neural Network (NN), Classification and Regression Tree (CART), and Linear Discriminant Analysis (LDA). Before the models were constructed, a dataset consisting of all necessary data about crops and the environment is obtained from Kaggle. This dataset then undergoes a couple of processes named encoding and upsampling to ensure the better performance of the ML models. The models are then trained using this preprocessed dataset. An unused part of the dataset is then used to test the efficiency of the models. This analysis is done using four parameters named the True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate (FPR), and False Negative Rate (FNR). By the end of this analysis, it is found that the NN algorithm is the best algorithm that can be used to predict the best fertilizer with the perfect score of 100 as a true negative rate. The LDA algorithm has the least right predictions and the maximum number of false predictions making it the worst algorithm among the three. The best algorithm, i.e., the model designed using the NN algorithm is deployed into a software application. Users can enter all the necessary details to find the most suitable fertilizer for crops based on environmental factors and crop requirements.