review · African Scientific Reports
Artificial intelligence methods increasingly assist farmers in selecting suitable crops based on soil, climate, and environmental data. A systematic review examining 129 studies published between 2020 and 2026 shows that ensemble learning techniques, notably Random Forest and Extreme Gradient Boosting, consistently achieve strong predictive performance across agricultural datasets. Traditional algorithms like Support Vector Machines and Decision Trees remain widely used, whereas Convolutional Neural Networks and Long Short-Term Memory models handle remote sensing and time-series data. Typical predictive inputs include soil nutrients, pH levels, weather metrics, and satellite indices. However, most existing models rely heavily on static repositories like Kaggle rather than dynamic environments. Critical research barriers include a lack of real-time deployment, weak integration of multi-source data, inadequate validation across different geographic regions, and limited model interpretability.
Selecting the right crop is vital for farm productivity and sustainable food supply. While artificial intelligence offers powerful decision-support tools, current algorithms are largely tested on historical or static data. Highlighting these limitations helps researchers and developers focus on creating transparent, real-time systems that adapt reliably to changing local weather and soil conditions across diverse agricultural landscapes.
The reviewed techniques could enable automated advisory software and precision farming tools for agricultural producers and agritech providers. Nevertheless, the domain remains at an early to intermediate research stage. Widespread commercial adoption requires moving beyond static data to build explainable, real-time platforms validated across varied geographic regions.
AI-generated from the published abstract. Always read the original work before citing.
Crop recommendation methods have become an essential component of modern agriculture, helping farmers identify the most suitable crops based on soil, climatic, and environmental conditions. As key applications of precision agriculture, these methods increasingly employ artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), to improve crop prediction, recommendations, productivity, and farming decision-making. This study presents a systematic literature review (SLR) of ML and DL techniques applied to crop recommendation and related agricultural applications. From 183 identified studies, 129 articles published between 2020 and 2026 were carefully selected through a structured screening process for comprehensive analysis. Relevant studies were retrieved from major academic literature databases and publishing platforms, including ScienceDirect, Scopus, SpringerLink, MDPI, Nature, Frontiers, IEEE, Wiley, and Google Scholar. The review used the PRISMA protocol and showed that ensemble learning-based approaches, particularly Random Forest and Extreme Gradient Boosting (XGBoost), are powerful for predictive performance on various agricultural datasets. Traditional ML approaches such as Support Vector Machines, Decision Trees, and k-Nearest Neighbors are still commonly used. At the same time, Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are used for remote sensing and time-dependent agricultural analysis. The most frequently used dataset source is Kaggle, and typical inputs include soil nutrients (NPK), soil pH, weather conditions, and satellite indices such as NDVI and EVI. Many studies have achieved high accuracy, but most are based on static datasets, which reduces their reliability in real-world scenarios. The main research gaps are limited real-time deployment, low integration of multiple data sources, low cross-regional validation, and low model interpretability. The review shows the importance of scalable and explainable AI systems for real applications in agriculture.
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DOI: 10.46481/asr.2026.5.3.590
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