Plant Disease Prediction using Machine Learning Algorithms

Abstract

Machine learning is the one of the branch in Artificial Intelligence to work automatically or give the instructions to a particular system to perform a action. The goal of machine Learning is to understand the structure of the data and fit that data into models that can be understood and utilized by the people. The proposed research work is for analysis of various machine algorithms applying on plant disease prediction. A plant shows some visible effects of disease, as a response to the pathogen. The visible features such as shape, size, dryness, wilting, are very helpful to recognize the plant condition. The research paper deals with all such features and apply various machine learning technologies to find out the output. The research work deals with decision tree, Naive Bayes theorem, artificial neural network and k-mean clustering and random forest algorithms. Disease development depends on three conditions-host plants susceptible to disease, favorable environment and viable pathogen. The presence of all three conditions is must for a disease to occur.

Plant disease prediction using classification algorithms

“…In November 2018, a study was published in which 3 classification algorithms were tested to compare their accuracy in predicting diseases in plants, based on a dataset of plant leaf images. This study found that the decision tree algorithm performed better than ANN and naïve bayes [1]. Another study, published in March 2018, compared the classification accuracy of predicting loss caused by grass grub insect using the following techniques: decision tree, random forest, neural networks, gaussian naïve bayes, SVMs, and KNN [12].…”

“…The main goal of this paper is to test the accuracy and compare the results of existing classification algorithms in predicting edibility in mushrooms and classifying diseases in soybean plants. While the mushroom and soybean datasets used in this paper have many differences, they are similar in that they are datasets of either numerical or categorical attributes, while many similar studies have been conducted on datasets of images instead of raw measurements [1][2][3][4]. The objective of the analysis conducted in this paper is to make suggestions to agricultural researchers, or disease researchers in general, on classification methods that perform well, in terms of disease prediction and classification accuracy, on datasets with raw measurements.…”

A Review of Machine Learning Techniques in Agroclimatic Studies

“…Studies such as [18,19] highlight the accuracy of ML models in forecasting crop yields, facilitating informed decision making among farmers and stakeholders. The integration of ML in disease management, demonstrated by [20,21], showcases the potential of convolutional neural networks (CNNs) in early plant disease identification, thus mitigating losses and reducing chemical pesticide dependency. Furthermore, advancements in soil analyses through ML, as discussed in [22][23][24], have enabled more precise soil health assessments, leading to optimized nutrient management and soil conservation practices.…”

“…Agricultural Application [18,26] Decision Tree Crop Yield Prediction, Disease Detection, Soil Assessment [18][19][20] Random Forest Crop Yield Prediction, Disease Detection, Soil Assessment [18,27] Extreme Gradient Boosting Crop Yield Prediction, Soil Assessment [18,20] Naive Bayes Crop Yield Prediction, Disease Detection [18,21] K-Nearest Neighbors Crop Yield Prediction, Disease Detection [28] Ensemble Traditional ML Models Crop Yield Prediction [26] Multi-Linear Regressor Crop Yield Prediction [29] RNN Crop Yield Prediction [29] LSTM Crop Yield Prediction [29] Support Vector Regression Crop Yield Prediction [23,24,30,31] CNN Crop Yield Prediction, Disease Detection [30] GNN Crop Yield Prediction [30] U-Net Crop Yield Prediction [23,25,32] ANN Crop Yield Prediction, Disease Detection [25] DBSCAN Crop Yield Prediction [23,25] Support Vector Machine Crop Yield Prediction, Disease Detection, Smart Farming [33] Vision Transformers Disease Detection [22] VGG-RNN Hybrid Soil Assessment [23,24] MLP Soil Assessment…”

Improving prediction of plant disease using k-efficient clustering and classification algorithms

“…Also, early detection of plant disease is the greatest approach to enhance prediction, and this is where machine learning (ML) algorithms may help in the current work. The integration of clustering with classification facilitates the use of very smart frameworks which take account of the operational performance and efficiency of all ML-affiliated institutions [4], [5] and detect knowledge patterns that correlated to the nature of plant diseases. ML can address critical concerns connected to plant ISSN: 2252-8938 …”