Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers

Abstract

Agriculture is a growing field of research. In particular, crop prediction in agriculture is critical and is chiefly contingent upon soil and environment conditions, including rainfall, humidity, and temperature. In the past, farmers were able to decide on the crop to be cultivated, monitor its growth, and determine when it could be harvested. Today, however, rapid changes in environmental conditions have made it difficult for the farming community to continue to do so. Consequently, in recent years, machine learning techniques have taken over the task of prediction, and this work has used several of these to determine crop yield. To ensure that a given machine learning (ML) model works at a high level of precision, it is imperative to employ efficient feature selection methods to preprocess the raw data into an easily computable Machine Learning friendly dataset. To reduce redundancies and make the ML model more accurate, only data features that have a significant degree of relevance in determining the final output of the model must be employed. Thus, optimal feature selection arises to ensure that only the most relevant features are accepted as a part of the model. Conglomerating every single feature from raw data without checking for their role in the process of making the model will unnecessarily complicate our model. Furthermore, additional features which contribute little to the ML model will increase its time and space complexity and affect the accuracy of the model's output. The results depict that an ensemble technique offers better prediction accuracy than the existing classification technique.

XAI‐driven model for crop recommender system for use in precision agriculture

“…To better comprehend the performance of the optimization algorithm with RBF, the model is evaluated with RBF with SMO and RBF alone. The performance of the models is compared with the outcomes of the studies presented by Raja et al 44 and Thilakarathne et al 45 The results are discussed in the current section. The corresponding confusion matrix obtained on experimentation with RBF alone is shown in Figure 11A and class‐wise results are shown in Table 1, and the confusion matrix with experimental results of RBF with SMO are shown in Figure 11B and their class‐wise results are shown in Table 2.…”

Multi-Crop Classification Using Feature Selection-Coupled Machine Learning Classifiers Based on Spectral, Textural and Environmental Features

“…The main principle of recursive feature elimination (RFE) is to obtain the optimal feature set by repeatedly constructing a model [11,34]. Through iterative loops, the size of the feature set was continuously reduced to select the required features.…”

“…In addition, environmental characteristics remarkably affect the growth characteristics of crops. Therefore, environmental characteristic indicators can be used to identify crops considering the difference driven by the environment, thus improving classification accuracy [11]. Zhang et al [12] used spectral and environmental indexes for crop classification, and the results showed excellent accuracy.…”

Seleksi Fitur untuk Prediksi Hasil Produksi Agrikultur pada Algoritma K-Nearest Neighbor (KNN)

“…Teknik pada data mining merupakan proses dalam mengekstraksi informasi yang dapat diterapkan untuk memprediksi hasil produksi pertanian menggunakan teknik yang efisien dan bermanfaat [7] [8]. Dalam penerapan teknik data mining yang efisien dan bermanfaat terdapat permasalahan yang dikaitkan dengan kompleksitas parameter sehingga sangat penting untuk dilakukan seleksi fitur yakni membentuk fitur yang paling relevan pada penelitian yang bertujuan untuk memastikan model pembelajaran mesin memiliki tingkat performansi yang tinggi dan mengurangi redundasi pada model yang akan diterapkan [9] [10]. Beberapa teknik seleksi fitur yang pernah diterapkan untuk memprediksi berhasil meningkatkan performansi model yang dihasilkan dalam berbagai bidang khususnya dibidang pertanian [11][12][13][14] [15].…”