Advancements in Precision Agriculture: A Literature Review of Machine Learning Applications for Crop Monitoring and Yield Prediction

Agriculture plays a critical role in the economic development of nations. However, with a growing global population, shifting climate conditions, and limited natural resources, meeting the increasing food demand is becoming a complex challenge. Precision agriculture, also referred to as smart farming, has emerged as a powerful solution to address these issues by promoting sustainable agricultural practices. At the heart of this innovation is machine learning (ML), ensemble learning, and deep learning, which enable systems to learn and adapt without explicit programming. Together with IoT-enabled farm equipment, ML is driving the next agricultural revolution. In this systematic literature review (SLR), we examine research from 2015 to 2023 on the diverse applications of ML in agriculture. A key area explored in this SLR is disease detection, focusing on identifying diseases. Yield prediction is another prominent area, where ML is used to forecast crop yields based on diverse environmental and agricultural data. The review also examines the prediction of soil properties, such as organic carbon content and moisture levels, which are crucial for optimizing crop management. Lastly, weather prediction is discussed, highlighting its importance in planning and adjusting farming activities in response to climatic conditions. Additionally, challenges such as data availability, model scalability, and the need for regional validation are explored.

An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development of an extremely effective model for the prediction of palm oil yields with the most minimal computational difficulty.

Agriculture plays a vital role in the Indian economy. Crop recommendation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters. At the same time, crop yield prediction was based on several features like area, irrigation type, temperature, etc. The latest breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) technologies pave the way to designing effective crop recommendation and prediction models. Despite the significant advancements of Deep Learning (DL) models in crop recommendation, hyperparameter tuning using metaheuristic algorithms becomes essential for enhanced performance. This tool allows users to anticipate appropriate crops and their expected yields for a provided year, assisting agriculturalists in choosing crops suitable for their area and period and anticipating productivity. This article introduces a Gorilla Troops Optimization with Deep Learning-based Crop Recommendation and Yield Prediction model (GTODL-CRYPM). The proposed GTODL-CRYPM model mainly focuses on two processes, namely, crop recommendation and crop prediction. Firstly, the GTO with Long Short-Term Memory (LSTM) technique is employed to make efficient crop recommendations. Besides, the GTO model is applied to adjust the LSTM parameters optimally. Next, the Deep Belief Network (DBN) technique was executed to predict crop yield accurately. A wide range of experiments have been conducted to report the improved performance of the GTODL-CRYPM model. The outcomes are examined under the Crop Recommendation Dataset and Crop Yield Prediction Dataset. Experimentation outcomes highlighted the significant performance of the GTODL-CRYPM approach on the compared approaches, with a maximum accuracy of 99.88% and an R2 score of 99.14%.

Agriculture plays a vital role in the Indian economy. Crop recommendation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters. At the same time, crop yield prediction was based on several features like area, irrigation type, temperature, etc. The recent advancements of artificial intelligence (AI) and machine learning (ML) models pave the way to design effective crop recommendation and crop prediction models. In this view, this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction (MMML-CRYP) technique. The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction. At the initial stage, equilibrium optimizer (EO) with kernel extreme learning machine (KELM) technique is employed for effectual recommendation of crops. Next, random forest (RF) technique was executed for predicting the crop yield accurately. For reporting the improved performance of the MMML-CRYP system, a wide range of simulations were carried out and the results are investigated using benchmark dataset. Experimentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.

The agricultural sector faces increasing pressure to meet global food demand due to population growth. Challenges such as climate change, resource scarcity, and environmental degradation will further increase this problem. These issues are particularly critical in the Mediterranean region, which is characterized by water-limited conditions and soils poor in organic matter and mineral nutrients. As a step toward ensuring food security, optimized resource utilization strategies and actionable plans for stakeholders are necessary. Reliable estimation of crop growth parameters and yield prediction under different climatic and agronomic scenarios have emerged as critical tools in driving these changes.Various conventional crop growth parameter estimation and yield prediction methods have emerged as methods for optimizing resource utilization, identifying risks, and enabling effective decision-making. However, conventional methods, including empirical, statistical, and process-based models, often face limitations such as co-linearity among predictor variables, assumptions of stationarity, and the inability to capture complex biophysical and biochemical interactions at large scales. These shortcomings highlight the need for more robust and adaptable approaches. Advanced technologies, particularly Artificial Intelligence (AI) and Remote Sensing (RS) have revolutionized agriculture by uncovering hidden patterns, enabling large-scale monitoring, and improving prediction accuracy. This research evaluates the state-of-the-art in the synergized use of AI and RS for crop growth parameter estimation and yield prediction in Mediterranean agroecosystems.A systematic literature review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Keywords and Boolean operators were used to search titles, abstracts, and keywords in selected databases, including Web of Science and Scopus. The review included English and French publications focusing on the Mediterranean region, encompassing Southern European, Middle Eastern, and North African countries bordering the Mediterranean Sea. Publications that were duplicated, unrelated to the study objectives, or outside the geographical focus were excluded. Out of 551 initial publications retrieved, 117 met the inclusion criteria and were selected for detailed review.The findings reveal a rising interest in integrating AI and RS for estimating crop growth parameters and predicting yield. Multispectral RS products, such as Landsat-8 and Sentinel-2, are the most frequently utilized data sources. Additionally, Sentinel-1 microwave sensors and Unmanned Aerial Vehicle (UAV)-based imagery are increasingly employed alongside ground-based sensors. Among AI methodologies, Machine Learning (ML) algorithms like Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) dominate, while Deep Learning (DL) techniques such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks have gained prominence since 2020. Most publications were produced between 2020 and 2024, with Italy, Spain, and France being the most studied regions.The study underscores the transformative potential of integrating AI and RS for crop growth parameter estimation and yield prediction in Mediterranean agroecosystems. By leveraging diverse data sources, algorithms, and sensor technologies, these advancements address the limitations of traditional models, enhance scalability and accuracy, and support sustainable agriculture in resource-limited environments.This research is performed in the framework of the PhD program in Agrobiosciences, Scuola Superiore Sant'Anna, scholarship: PNRR “Digital and environmental transitions” (M4C1, Inv. 3.4) ex MD 629/2024.

In recent years, Agriculture sector has been researched a lot with the advancements in technologies like machine learning and smart computing. With the dynamic economics of Agri-produce, it is becoming challenging for farmers to utilize the land efficiently to get maximum profit in the specific landscape. Crop Yield Prediction (CYP) is crucial and is greatly dependent on environmental factors like soil contents, humidity, rainfall as well as area under cultivation and other required metrics. Due to insufficient incorporation of the multiple environmental circumstances, a number of existing tools and techniques used for CYP, such as historical averages, tend to produce inaccurate findings. In such situation, with multiple options of crop, it is essential for farmers to plan the crop strategy in advance. If the farmer can get estimate of the crop yield in advance, cultivation can be done accordingly. To solve this problem, machine learning approach is implemented as a base for accurate predictions. Crop prediction is done by classification model and yield prediction uses regression models to learn from the data. Multiple ML models are analyzed based on performance metrics. Best performer model is incorporated in backend. Among the used models for yield prediction, Random Forest Regression gives best results with MAE of 0.64 and R2 score of 0.96. For crop prediction, Naïve Bayes classifier gives most accurate results with accuracy of 99.39. The study emphasizes how machine learning could revolutionize crop management techniques by giving farmers insights about optimizing resource allocation and boost overall crop yield.

- Agriculture plays a critical role in sustaining economies and feeding populations, especially in countries like India, where a significant portion of the workforce depends on it. However, crop yield is highly influenced by numerous environmental and soil-related factors, making prediction and decision-making challenging for farmers. This study presents a data-driven crop yield prediction and market analysis system that leverages machine learning and real-world agricultural data to provide accurate yield forecasts. Using a cleaned dataset comprising key features such as temperature, rainfall, soil nutrients (N, P, K), pH, and crop type, a regression-based predictive model was developed to estimate the yield in tons per hectare. Additionally, an economic perspective was integrated through the analysis of market price trends from regional data, aiding in crop selection based on both productivity and profitability. The proposed system, supported by an interactive interface, empowers stakeholders to make informed agricultural decisions, thereby enhancing productivity and economic returns. The experimental results demonstrate the effectiveness and potential of the model to support smart agriculture initiatives. Key Words: Machine Learning Crop Yield Prediction, Machine Learning, XGBoost Regressor, Random Forest, Gradient Boosting, Precision Agriculture, Soil Analysis, Agricultural Forecasting, Market Price Analysis, Sustainable Farming

Machine learning, a subset of artificial intelligence, is revolutionizing agriculture by enabling enhanced crop monitoring, disease detection, and yield prediction. Its application extends to precision farming, where it aids in optimizing irrigation, fertilization, and harvesting by analyzing large datasets from mobile phones, sensors and satellite images. It has played a crucial role in identifying and categorizing different types of plants. Specifically, Convolutional Neural Networks (CNN), a deep learning technology within ML, are employed to classify plant species based on images. In the present research work, we have employed machine learning for the classification of citrus species using Convolutional Neural Networks (CNNs). A dataset has been developed with images of fruits and leaves from ten citrus species. We have utilized transfer learning with architectures like Mobile Net, Alex Net, and Goog Le Net. The study demonstrates that combining multiple plant components in CNN analysis improves classification accuracy, with leaves providing more reliable results than fruits. This approach signifies a major advancement in agricultural technology, allowing for more precise and efficient farming practices. The findings indicate that expanding the dataset and incorporating more plant structures could further refine these models. This research highlights the potential of machine learning in agriculture, particularly in enhancing species classification, which is crucial for sustainable and productive farming.

Agriculture, a critical sector in India's economy, is deeply influenced by various factors such as climate, soil quality, irrigation, and economic conditions. Accurate prediction of crop yield is essential for both farmers and businesses operating in the agricultural supply chain. Historical data on crop yields can inform decision-making, reduce risks, and enhance the efficiency of supply chain operations, including the scheduling of production and the marketing of agricultural products. However, existing methods for predicting crop yield often face challenges in accuracy and generalizability.This study proposes an ensemble-based clustering and classification framework to improve the prediction of agricultural crop yield. The framework includes three main components: (i) pre-processing of agricultural datasets, (ii) ensemble clustering using two optimization algorithms—Enhanced Artificial Bee Colony Optimization (EABCO) and Shuffled Frog Leaping Algorithm (SFLA), and (i) classification of clustered datasets using Enhanced K-Nearest Neighbor (KNN) classification. The proposed method aims to reduce the limitations farmers face in selecting suitable crops for specific regions and to enhance the accuracy of crop yield predictions by identifying key indicators of agricultural field heterogeneity.The framework was implemented in a Java environment and tested on datasets related to paddy crop yield. The performance of the proposed method was evaluated using accuracy, precision, recall, F-measure, and percentage error. The results demonstrate that the proposed EABCO-SFLA clustering ensemble, combined with Enhanced KNN classification, achieves higher accuracy (92.7%), improved precision (91.2%), better recall (90.6%), and a reduced percentage error (5.3%) compared to existing classifiers. This approach provides a more reliable prediction of crop yield, enabling better decision-making in agriculture and supply chain management.

Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

Precision agriculture utilizes information technologies to efficiently manage water and soil resources in agriculture. This technological revolution has been sparked by the big data analytics, machine learning and Internet of Things (IoT)in numerous industries. This game-changing technology has influence on houses, grids, smart cities, and health. Machine learning and data-intensive decision-making have been made possible, which has created new possibilities for efficiency and innovation in various fields. In several fields, the convergence of IoT, big data analytics, and machine learning has opened the way for cutting-edge solutions and enhanced results.The application of machine learning in the agricultural sector can increase production quantity and quality to meet increasing food demand. These advancements are transforming current agrarian approaches and generating new opportunities, despite some limitations. This paper presents a systematic appraisal of the role of IoT, big data analytics, and machine learning in precision agriculture, highlighting the potential benefits and challenges in this rapidly evolving field.

Developing an accurate crop yield predicting system at a large scale is of paramount importance for agricultural resource management and global food security. Earth observation provides a unique source of information to monitor crops from a diversity of spectral ranges. However, the integrated use of these data and their values in crop yield prediction is still understudied. Here we proposed the combination of environmental data (climate, soil, geography, and topography) with multiple satellite data (optical-based vegetation indices, solar-induced fluorescence (SIF), land surface temperature (LST), and microwave vegetation optical depth (VOD)) into the framework to estimate crop yield for maize, rice, and soybean in northeast China, and their unique value and relative influence on yield prediction was assessed. Two linear regression methods, three machine learning (ML) methods, and one ML ensemble model were adopted to build yield prediction models. Results showed that the individual ML methods outperformed the linear regression methods, the ML ensemble model further improved the single ML models. Moreover, models with more inputs achieved better performance, the combination of satellite data with environmental data, which explained 72%, 69%, and 57% of maize, rice, and soybean yield variability, respectively, demonstrated higher yield prediction performance than individual inputs. While satellite data contributed to crop yield prediction mainly at the early-peak of the growing season, climate data offered extra information mainly at the peak-late season. We also found that the combined use of EVI, LST and SIF has improved the model accuracy compared to the benchmark EVI model. However, the optical-based vegetation indices shared similar information and did not provide much extra information beyond EVI. The within-season yield forecasting showed that crop yields can be satisfactorily forecasted at two to three months prior to harvest. Geography, topography, VOD, EVI, soil hydraulic and nutrient parameters are more important for crop yield prediction.

Crop yield prediction at regional levels is an essential task for the decision-makers for rapid decision making. Pre-harvest prediction of a crop yield can prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. With the advent in digital world, various advanced techniques are employed for crop yield prediction. Remote Sensing (RS) data with its capability to provide the synoptic view of the Earth’s surface, has numerous returns in the area of crop monitoring and yield prediction. This study provides as a review for the advanced techniques for crop yield prediction in India with RS data as a base. The advanced techniques like RS based statistical yield modelling, machine learning based yield modelling, semi-physical yield modelling are described in the current study. The assessment of the studies related to integration of RS data in crop simulation model is also described in a section. All the techniques involved in the current study show significant improvements in crop yield prediction, enabling the development of new agricultural applications in India.

Reliable and timely crop-yield prediction and crop mapping are crucial for food security and decision making in the food industry and in agro-environmental management. The global coverage, rich spectral and spatial information and repetitive nature of remote sensing (RS) data have made them effective tools for mapping crop extent and predicting yield before harvesting. Advanced machine-learning methods, particularly deep learning (DL), can accurately represent the complex features essential for crop mapping and yield predictions by accounting for the nonlinear relationships between variables. The DL algorithm has attained remarkable success in different fields of RS and its use in crop monitoring is also increasing. Although a few reviews cover the use of DL techniques in broader RS and agricultural applications, only a small number of references are made to RS-based crop-mapping and yield-prediction studies. A few recently conducted reviews attempted to provide overviews of the applications of DL in crop-yield prediction. However, they did not cover crop mapping and did not consider some of the critical attributes that reveal the essential issues in the field. This study is one of the first in the literature to provide a thorough systematic review of the important scientific works related to state-of-the-art DL techniques and RS in crop mapping and yield estimation. This review systematically identified 90 papers from databases of peer-reviewed scientific publications and comprehensively reviewed the aspects related to the employed platforms, sensors, input features, architectures, frameworks, training data, spatial distributions of study sites, output scales, evaluation metrics and performances. The review suggests that multiple DL-based solutions using different RS data and DL architectures have been developed in recent years, thereby providing reliable solutions for crop mapping and yield prediction. However, challenges related to scarce training data, the development of effective, efficient and generalisable models and the transparency of predictions should be addressed to implement these solutions at scale for diverse locations and crops.

This review paper provides an overview of the applications of machine learning in the agriculture field. Machine learning, a subfield of artificial intelligence, has been successfully applied to various domains, and agriculture is no exception. The paper starts with a brief introduction to machine learning and its various algorithms. It then presents various applications of machine learning in agriculture, including crop yield prediction, precision agriculture, plant disease detection, and soil moisture prediction. The paper highlights the advantages of using machine learning in agriculture, including increased efficiency, reduced costs, and improved decision-making. It also discusses the challenges faced in the application of machine learning in agriculture, including the need for large amounts of data and the difficulty in collecting high-quality data in remote and rural areas. Finally, the paper concludes with future directions for research and the potential impact of machine learning on the agriculture industry. The review shows that machine learning has the potential to revolutionize the way we approach agriculture and food production, leading to a more sustainable and efficient future for the industry.