This review paper aims to explore recent studies on the application of AI techniques in agriculture, addressing specific questions through a two-fold approach (Fig. 1). Firstly, it provides a comprehensive overview of key AI concepts in agriculture and discusses a sustainable perspective for optimizing input use efficiency. Second, it carries out a desk research to provide a comprehensive literature analysis, focusing on the state of AI-driven automation in agriculture, encompassing nutrient stress, weed control, and irrigation. The review utilizes secondary documents to analyze the limitations and potential of AI for achieving sustainability in the context of agronomic management practices. The issue overview, literature sourcing, synthesis and discussion of the findings, and technique used in previous research are the three iterative phases of the desk study [3,36].

We used reliable online resources like PubMed, Scopus, Google Scholar, Web of Science, and Science Direct to do a thorough internet search in order to conduct the literature survey. Other sources, such as the Food and Agriculture Organization of the United Nations (FAO) are also used for this study. The search employed specific key terms, including “artificial intelligence”, “concept of machine learning”, “deep learning in agriculture”, “nutrient stress detection using AI”, “irrigation management strategies using AI in different crops”, “weed identification using AI”, “weed control through AI”, “challenges of AI” and “prospects”. The articles included in this review were mainly selected based on the significance of their titles and abstracts of the research topic.

The inclusion criteria for this study were research that particularly studied advances in the use of AI to improve resource usage efficiency through real-time insights into fertiliser, water, and weed management. Papers that did not include AI applications in agriculture in their abstract, introduction, or conclusion were rejected at the eligibility stage. On the other hand, the exclusion criteria included articles that addressed topics other than the main three pillars of crop management such as nutrient, irrigation, and weed management and were written in any other language, contained incomplete or irrelevant data, irrelevant and duplicate articles or for which full-text access was unavailable.

We conducted a comprehensive literature search to identify studies that elucidate the potential of AI to increase agricultural input use efficiency by optimizing agricultural practices and minimizing resource wastage through sensors, drones, and satellite imagery, AI algorithms. This review included 180 relevant papers from the 327 articles that were initially collected. To address the existing knowledge gap in this subject, we collected studies ranging from 1999 to 2024, including both recent and historical data.

There are mainly two subsets of AI namely ML and DL [58]. ML is a branch of AI that enables machines to learn from experiences and make more accurate predictions [59]. It uses multiple algorithms or the same algorithm multiple times to achieve better performance [60]. Through ML, computer programs can improve their performance by learning from problem-specific training data, allowing them to perform tasks such as object detection and natural language translation [61]. With machine learning algorithms, hidden insights and complex patterns can be identified without the need for explicit programming [62]. In order to make reliable and repeatable decisions, ML relies on previous computations and extracts patterns from large databases. AI is deployed using dedicated machines or systems that rely on ML technology. ML entails discovering patterns and characteristics within the machine through direct training using data. Computers learn from specific data provided by humans and conduct assessments and predictions based on the acquired knowledge during the learning process [63].

ML can be categorized into three primary learning methods: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, both the data and corresponding labels (answers) are provided to the learning algorithm. The objective of utilizing algorithms in machine learning is to enable them to acquire knowledge from labeled data and make accurate predictions for unlabelled data. This approach is frequently employed in various tasks, including object recognition, probability estimation, and regression analysis [64]. In contrast, unsupervised learning involves working with unlabelled data to identify inherent patterns, characteristics, and classes using learning algorithms. This type of learning is particularly useful for tasks like clustering, feature extraction, and dimensionality reduction. Lastly, reinforcement learning entails an agent interacting with an environment, perceiving its current state, and selecting actions or action sequences to maximize rewards or compensations based on available behaviours. This type of learning is often employed in fields like robotics and game-playing. Each of these learning methods has its unique applications and it is utilized depending on the specific problem and the available data.

DL is a specialized branch of machine learning that utilizes DCNN or CNNs. Unlike simple neural networks, deep neural networks have multiple hidden layers arranged in nested architectures. They also use advanced neurons and procedures like convolutions or multiple activations in a single neuron. These qualities allow deep neural networks to handle raw input data and automatically identify the representations required for the specific learning task [65]. The main distinction between ML and DL lies in their respective approaches. Research in machine learning typically involves identifying key features from the data using the researcher’s experience or domain expertise. These features are extracted using manual or image processing algorithms, and then utilized for subsequent classification or regression analysis [66]. The DL algorithm, on the other hand, automatically extracts features from raw image data and performs classification and regression training without the need for explicit feature engineering.

Drones, also known as unmanned aerial vehicles (UAVs), have become indispensable tools for farmers in addressing various challenges and monitoring their fields [102]. These UAVs assist in observing crop health, detecting nutrient and water stress, and identifying diseases. They utilize a range of sensors including thermal, multispectral, hyperspectral, and RGB sensors. The integration of UAVs-based IoT technology is considered the future of remote sensing in precision agriculture. By flying at low altitudes, UAVs may gather pictures with ultra-high spatial resolution of up to a few centimetres, considerably boosting system performance. Furthermore, UAVs are less costly and easier to operate than manned aircraft, and they are more efficient than ground systems since they can cover large areas in less time and cause less damage. UAVs are now widely used for monitoring crop fields [103]. UAVs are equipped with multiple sensors that enable farmers to identify areas within their crops that require timely action for improvement. UAVs finds extensive applications in precision agriculture [104]. The deployment of multispectral and hyperspectral sensors on drones enables the collection of visible and non-visible wavelengths, such as near-infrared radiation (NIR) and short-wave infrared radiation (SWIR) with wavelengths ranging from 800 to 2500 nm and 1400 to 3000 nm, respectively [105]. These sensors are useful in managing plant health by identifying nutrient deficiencies; weed and drought stress [106].

Additionally, the utilization of drones enables the collection of data for crop cultivation assistance. Subsequently, the acquired data can be analyzed through AI in farming, allowing farmers to make well-informed decisions. Drones equipped with sensors and capable of capturing multispectral photos facilitate crop health monitoring. The assistance of ML algorithms expedites and simplifies the process of data analysis. This integration enhances agricultural output efficiency and concurrently reduces crop loss.

Multispectral and hyperspectral imagery differ in the number and width of bands of light detected by each camera. Multispectral imagery typically refers to around five to ten bands, while hyperspectral imagery can have hundreds of bands. Hyperspectral sensors create images using much narrower bands in the range of 10–20 nm, employing an imaging spectrometer. This broader spectrum coverage makes hyperspectral sensors more sensitive and capable of capturing images in bandwidths that are not possible with multispectral sensors. The reflectance in the SWIR region, combined with reflectance in the visible or near-infrared (NIR) regions, has been found to monitor the status of nitrogen (N), phosphorus (P), sulphur (S), and potassium (K) in plants [107]. New spectral algorithms specifically developed and validated for N, P, S, and K can be utilized for site-specific management in wheat crops [108]. Additionally, drones can be used to observe and assess serious soil degradation, which poses a threat to soil productivity [109]. The introduction of drones in agriculture has revolutionized big-area inspections, smart targeted irrigation, and fertilization [110]. The ability to detect areas requiring significant irrigation or affected by weeds through drone-based infrared cameras helps agronomists save time, conserve water resources, and reduce the use of agrochemicals. Moreover, these advanced farming techniques have the potential to increase crop productivity and improve the overall quality of the produce [111].

AI-powered drones play an important role in assisting farmers with agricultural production and harvesting procedures. The incorporation of predictive analysis shows useful in early problem detection in the field [112]. This enables farmers and organizations to address concerns, reducing the likelihood of substantial crop loss or damage. AI technology has the ability to forecast and detect approaching flood or drought conditions before they occur [113,114]. Furthermore, AI simplifies the analysis of weedicide and pesticide requirements, allowing precise application in the field. The software aids in the timely detection of pest attacks and plant health concerns in real time [115], optimizes soil fertility management [116], and reduces the demand for pesticides and herbicides in specific fields [89]. In the domain of pesticide and weedicide application, AI contributes to the efficiency of spraying operations and crop monitoring. The utilization of drones for chemical spraying not only enhances effectiveness but also reduces human efforts and workforce demands [117].

AI presents an innovative approach to identifying nutrient deficiencies in plants, particularly through the rapid fluorescence of chlorophyll a. Kalaji et al. [118] conducted a study investigating the impact of deficiencies in certain macro (Ca, S, Mg, K, N, P) and micro (Fe) nutrients on the photosynthetic machinery of hydroponically grown tomato (Solanum lycopersicum L.) and maize (Zea mays L.) plants. A comparison was done between plants grown in a full nutrient solution (control) and those grown in a medium deficient in either a macro-or microelement. The physiological state of the photosynthetic apparatus in vivo was evaluated after 14 days of food shortage using JIP-test parameters generated from rapid chlorophyll fluorescence measurements. Most cases of nutritional insufficiency resulted in a decrease in photochemical efficiency, an increase in non-photochemical dissipation, and a reduction in the number of active photosystem II (PSII) reaction centers. However, individual nutrient deficits have a nutrient-specific influence on photochemical processes. Plants deficient in magnesium and calcium demonstrated a significant decrease in electron donation via the oxygen-evolving complex (OEC). Sulphur deficiency inhibited electron transport beyond PSI, most likely due to a decrease in PSI concentration or the activity of PSI electron acceptors. Conversely, Ca deficiency had an opposite effect, impacting PSII activity more than PSI. While distinct responses to nutrient deficiencies were noted between tomato and maize plants, the study’s findings suggest that certain fluorescence parameters could serve as markers for the fluorescence phenotype [119]. The principal component analysis of selected JIP-test parameters was offered as a potential species-specific technique for diagnosing or forecasting nutritional deficiencies using fast chlorophyll fluorescence measurements.

Similarly, Aleksandrov [120] proposed a technique that evaluates the photosynthetic activity of leaves to assess mineral deficiency in nutrient solutions. Chlorophyll fluorescence is analyzed using the Joint Imaging Platform (JIP) test, which provides information regarding the function of photosystems I and II and the overall physiological condition of the photosynthetic apparatus. To detect nutritional deficits in bean plants, the researchers measured fluorescence transients from plants grown in nutrient solutions missing N, P, K, Ca, or Fe. These fluorescence transients served as input data for an artificial neural network trained to identify and forecast N, P, K, Ca, and Fe deficiencies in bean plants. The results indicated the potential of the ANN as a useful tool for recognizing and forecasting nutritional deficiencies in bean plants based on the rapid fluorescence of chlorophyll a [121].

In another study, Waheed et al. [26] utilized an Artificial Neural Network (ANN) to classify ginger plants with nutrient deficiencies, achieving a validation accuracy of 97% when compared to healthy plants. Furthermore, CNN models like MobileNetV2 and VGG16 demonstrated promising results with validation accuracies of 96% and 95%, respectively. The performance of the classification models was assessed using the Receiver Operating Characteristic (ROC) curve, with the ANN exhibiting a faster convergence rate in comparison to other techniques. These findings highlight the potential of the proposed deficiency detection methods to improve ginger yield and their relevance in developing real-time disease detection applications [122]. Kiratiratanapruk et al. [123] conducted a study focusing on the detection and classification of six major rice diseases using pre-trained models, including Faster R-CNN, RetinaNet, YOLOv3, and Mask R-CNN. The testing findings showed that YOLOv3 outperformed the other models for rice leaf disease detection and classification, with a mean average precision (mAP) of 79.19%. Mask R-CNN, Faster R-CNN, and RetinaNet attained accuracy values of 75.92%, 70.96%, and 36.11%, respectively. Furthermore, machine vision combined with DL techniques were evaluated for real-time identification of early blight disease in potatoes by creating a comprehensive database capturing images of healthy and diseased plants under various lighting conditions. Three Convolutional Neural Network (CNN) models, GoogleNet, VGGNet, and EfficientNet, were trained using the PyTorch framework. The CNNs and DL frameworks exhibited accurate classification of early blight disease at different stages [124].

Drought is a period of abnormally dry weather sufficiently prolonged for the lack of water to cause a serious hydrologic imbalance in the affected area. Unlike short-term natural disasters such as floods, earthquakes, and cyclones, drought can persist for extended periods, necessitating effective monitoring and forecasting using meteorological and remote sensing data for planning and decision-making. The accuracy and efficiency of drought forecasting models or methods are crucial for effective mitigation planning and adaptation strategies [125]. ML has emerged as a valuable tool for more accurate and efficient drought forecasting, contributing to drought disaster risk management [126,127]. To detect water stress in crops like maize, okra, and soybean, several deep-learning models were employed, including AlexNet, GoogLeNet, and InceptionV3. GoogLeNet had the highest accuracy rates at 98.3%, 97.5%, and 94.1% for maize, okra, and soybean, respectively [128].

Drought stress has a significant impact on the growth, development, and production of crops. While traditional machine learning techniques have made progress in detecting and diagnosing drought stress, their reliance on manual feature extraction processes limits their accuracy. To evaluate the accuracy of DCNN, a comparative experiment with standard machine learning on the same dataset was conducted. The overall identification and classification accuracies for drought stress were found to be 98.14% and 95.95%, respectively, for the entire dataset. Even in sub-datasets at the seedling and jointing stages, high accuracies were achieved, with color images outperforming grayscale images [129]. These comparative experiments on the same dataset demonstrate the superiority of DCNN over traditional machine-learning methods. The suggested deep learning-based technique shows potential in recognizing and categorizing drought stress in field maize using digital images [25]. Furthermore, the utilization of UAV imagery enables stress detection. Butte et al. [27] employed the Retina-UNet-Ag deep learning model to analyze aerial images of potato plants. The model achieved an average dice score coefficient (DSC) of 0.723 and 0.756 for healthy and stressed plants, respectively, demonstrating its ability to differentiate between the two in-field images captured by a Parrot Sequoia camera. Other studies have also addressed the localization of crop stress in aerial images using deep learning techniques [130,131].

Thermal imaging of plants has emerged as a non-destructive method for remotely monitoring water status. Melo et al. [132] conducted a study to predict the moisture status of sugarcane crops using thermal images. They employed an ANN model called Inception-ResNet-v2, which combines deep learning with transfer learning techniques to achieve high accuracy in a shorter time and at a lower cost compared to traditional methods [133]. A comparison was made between the recommended model’s performance and a human evaluation of the identical set of thermal photos. The results demonstrated that the developed technique outperformed human evaluations and enabled non-destructive classification of water stress in plant thermal images. The deep learning model exhibited superior accuracy in differentiating between different levels of thermal stress, with accuracies of 23%, 17%, and 14% for the available water capacity classes of 25%, 50%, and 100%, respectively [134]. Modelling the hyperspectral response of vegetables is essential for assessing water stress using a non-invasive method. Osco et al. [135] conducted a study on water-stressed lettuce (Lactuca sativa L.) using hyperspectral data and ANN. The performance of the ANN technique was evaluated in comparison to other machine learning algorithms. The results revealed that the ANN technique obtained up to 80% accuracy in discriminating water-stressed lettuce from the non-stressed group at the start of the trial. The accuracy gradually increased, reaching 93% at the end of the trial. Absorbance data outperformed reflectance data in terms of water stress modelling [136,137].

Arif et al. [138] created two artificial neural networks (ANNs) to evaluate soil moisture in paddy fields. The first model used minimum, average, and maximum air temperature data to predict evapotranspiration, whereas the second model used solar radiation, precipitation, and air temperature information. These models offered accurate and dependable soil moisture predictions using minimum meteorological data, labour, and time. Furthermore, Behmann et al. [139] claimed that close range hyperspectral imaging can identify stress-related processes non-destructively in their early stages, which are invisible to the naked eye. Their method combines unsupervised and supervised techniques to identify progressive stress buildup in barley (Hordeum vulgare) during drought conditions. These fingerprints can appear in both well-watered and drought-stressed plants, but their distribution varies. Ordinal classification using Support Vector Machines (SVM) quantifies and visualizes the distribution of senescence stages, distinguishing between well-watered and drought-stressed plants. Distinctive sets of relevant Vegetation Indices (VIs) are identified for each senescence stage. The method, applied to potted barley plants in greenhouse experiments, detects drought stress up to ten days earlier than NDVI. Additionally, certain VIs exhibit general relevance, while others are stage-specific. The study demonstrates the method’s effectiveness in visualizing leaf senescence and its efficiency in the early detection of drought stress.

Hinnell et al. [140] addressed the usage of neuro-drip irrigation systems, which used artificial neural networks (ANNs) to forecast the geographical distribution of subsurface water. The ANNs enabled fast decision-making processes, leading to efficient water management. The combination of precision agriculture and wireless sensor network (WSN) applications represents a promising area of research that can improve crop production, precision irrigation, and cost reduction. WSN systems offer easy deployment, system maintenance, and monitoring, which can contribute to the widespread adoption of precision agriculture.

The presence of weeds poses a significant threat to crop growth as they compete with crops for essential resources and space. Weeds have the potential to reduce crop production and quality by competing for resources [141]. Farmers employ various weed control strategies to mitigate yield reduction. However, current approaches primarily rely on chemical herbicides, leading to the rapid evolution of herbicide-resistant weeds and posing serious environmental risks. Due to manpower constraints, high human weeding expenses, and rising demand for organic food, the development of automated weed control devices for real-time field management has gained attention. Large-scale cultivation necessitates cost-effective and labor-saving solutions for efficient weed management. Automated weeding is an excellent approach for ensuring the sustainability of agricultural production systems. While herbicides are commonly employed for weed control in agriculture, their usage leads to environmental pollution along with risks of poisoning for individuals involved in their application [142,143]. To address the negative impacts mentioned, advancements in spraying technologies have been made to improve efficiency and safety. These advancements incorporate developments in electronics, AI, and automation [144–146]. However, it is important to note that most agrochemicals, including herbicides, are still applied uniformly across fields, regardless of the uneven distribution of pests, pathogens, and weeds. This uniform application leads to the wastage of agrochemicals in areas where there is little or no issue, resulting in increased costs, and risk of crop damage, pest resistance, environmental pollution, and contamination of edible products [147]. Weeds are known to grow quickly and spread over the field, competing with crops for important resources like space, nutrients, sunshine, and water. While herbicides are commonly used in agriculture to suppress weeds, improved sensor-based herbicide spraying can provide a long-term solution to offset the negative effects of indiscriminate herbicide use. Sensor-based spraying is categorized into two categories based on the application place [148] (Table 2). Spraying systems are divided into two categories: “green on brown” (GoB) and “green on green” (GoG). The GoB technique uses spectral information in the near-infrared and visible wavelengths in order to discriminate among green vegetation and soil or agricultural leftovers. The GoG technique employs powerful image algorithms to distinguish between green crops and green weeds, allowing plant species to be classed as crops, grass weeds, broadleaved weeds, and perennial weeds [149].

Using patch spraying or spot spraying techniques, these systems use precise sprayers to target areas with high levels of weed infestations [150]. Patch spraying involves the utilization of georeferenced weed maps to identify regions with significant weed infestations. Herbicides are selectively sprayed in these infested areas, while the boom sprayer is kept off in areas with reduced weed infestations that do not surpass the economic weed threshold. Spot spraying systems, on the other hand, have a narrower field of application and aim to target individual weed plants or smaller weed patches. This approach reduces the number of sprayed areas, allowing for more precise and targeted weed control [151]. Patch and spot spraying both require sensor-controlled spraying systems, which incorporate sensors to detect weeds and crops, expert systems to determine herbicide dosages and weed control requirements, and application systems to apply the herbicides [152]. Patch spraying uses georeferenced weed maps to determine areas for herbicide application based on weed infestation levels, while spot spraying can target even smaller areas for precise weed control. These targeted spraying techniques help minimize herbicide usage and reduce the environmental impact associated with excessive application [153].

Patch spraying and spot spraying are viable options for reducing herbicide usage and enhancing weed control in crops, serving as alternatives to uniform herbicide applications. Patch spraying is typically employed in extensive arable crops like cereals, maize, and soybeans, utilizing large boom sprayers for implementation [154]. Conversely, spot spraying is better suited for high-value crops like vegetables and sugar beets. However, spot spraying may have lower speeds due to the complexity of weed/crop classification using CNNs, but commercial robot systems have successfully implemented spot spraying [155].

The automation of weed sprayers has gained significant interest in recent years [156]. Incorporating computer vision technologies in agricultural operations has been found to reduce operator stress levels. An effective smart sprayer system should be capable of real-time weed spot detection and precise application of chemicals to the intended locations. Various sensors and techniques, including machine vision, spectral analysis, remote sensing and thermal imaging, have been analyzed for weed detection [157]. Machine vision, which enables the differentiation of vegetation from the soil background based on colour differences, has been used for weed detection, but earlier systems were limited in distinguishing between crop plants and weeds [158].

Researchers have developed and evaluated smart sprayer systems capable of distinguishing between weed leaves and crop plants. For example, Lee et al. [159] developed a system that could differentiate between weed leaves and tomato plants, although the processing speeds were slower at that time. Recent advancements in commercial spraying technologies have integrated AI to differentiate between crop plants and various weeds. Examples include the H-Sensor by Agricon GmbH and the See and Spray system by Blue River Technology, both designed for row crops. These precision spray technologies significantly reduce herbicide usage compared to traditional broadcast sprayers that treat the entire field regardless of weed presence [147].

In [160], three distinct deep-learning image-processing methods were employed to estimate weed presence in lettuce crops. These methods were compared to visual estimations made by experts. The first method employed was support vector machines (SVM) with histograms of oriented gradients (HOG) as feature descriptors, while the other or the second method used was YOLOV3 in order to detect objects. The third approach used Mask R-CNN for segmenting individual weeds. To remove non-photosynthetic items, a normalized difference vegetation index (NDVI) was utilized as a background subtraction. For crop detection, both machine learning and deep learning approaches received high F1 scores of 88%, 94%, and 94%, respectively. The observed crops were paired with the NDVI background subtractor to indirectly identify weeds. The coverage percentage of weeds was determined using classical image processing methods, resulting in improved accuracy compared to human-estimated data [161]. Furthermore, a method was created to distinguish weeds from crops using image analysis and neural networks, with an accuracy of more than 75% without prior plant knowledge. Shahzadi et al. [162] created an expert systems-based smart agricultural system that used IoT technology to relay data to a server, allowing implements in the field to make informed decisions. The system used temperature, humidity, leaf wetness, and soil sensors to provide information about the field, but it was not actively involved in processing.

The key weed control time has been identified for a variety of crops, notably annual species, taking into account crop type, cultivar, production system, management approaches, and environmental variables. Numerous research have looked at the interactions between crops, weed communities, and the environment. However, the applicability of these models as decision-making tools is limited due to their low generalization capacity, as they often struggle to interpret scenarios beyond experimental conditions. Complex relationships exist between agricultural systems and weed infestations, but recent advancements in computational development, particularly in machine learning models, have facilitated the understanding of these complex relationships. Machine learning models, such as ANNs, have exhibited the ability to learn and comprehend correlations between dependent and independent variables, enabling pattern detection and prediction under various conditions [163,164]. ANNs have been used effectively in weed research to identify weed species, determine spatial distribution for herbicide administration, and estimate herbicide sorption and desorption in agricultural soils [165]. ANNs offer great prediction accuracy for novel instances, but they must be thoroughly trained and evaluated to guarantee generalizability during validation and testing. The selection of appropriate inputs is crucial for generating high-performance models, which can be challenging when studying the weed control period due to the numerous variables influencing weed-crop competition [166,167]. Table 3 shows the identification of weed patches by different types of multispectral, RGB, and hyperspectral cameras.

The imperative task of weed monitoring and control in agriculture is underscored by Wakchaure et al. [175]. In the context of precision farming, Cho et al. [176], and Dorrer et al. [177] have harnessed standalone ANN for vision intelligence. However, the existing classification system encounters limitations, notably its need for individual testing in each operational field. Addressing this challenge, Hall et al. [178] have proposed a classification model incorporating low-dimensional features, utilizing DCNN algorithms for data collection. Their application on cotton plants via a mobile platform successfully delineated cotton and weeds.