Conservation agriculture is increasingly being promoted as sustainable farming based on a high emphasis on soil security, low use of external inputs and an increase in long-term productivity (
FAO, 2017).
Such practices enhance the soil structure, erosion and resource management, Climate variability, soil degradation, water and food shortages are some of the significant issues that agriculture is currently experiencing due to decreased soil fertility, water shortages and increased food demand worldwide. The conventional methods of farming are in most cases not able to mitigate these problems.
Artificial Intelligence as a terminology can be considered a set of calculation techniques (machine learning, deep learning and computer vision) that enable machines to process large volumes of data, identify trends and support decision-making. Agriculture is under pressure because of climatic changes, soil erosion, water scarcity and the increasing world population. The major features of its practices include minimal mechanical disturbance on soil, cover on the soil and crop rotations. The AI has found use in the agricultural sector to manage crops, predict crop yield, identify diseases and pests and augment climate resilience (
Kamilaris and Prenafeta-Boldu, 2018).
Agricultural applications of AI, such as yield prediction, disease detection, intelligent irrigation and others are already starting to provide agricultural systems with a sustainable existence
(Mohanty et al., 2016). These technologies are quite aligned with CA ideas because of the higher efficiency and the reduction of the effect on the environment.
Secondly, recent studies state that AI-driven agriculture can be used to improve the soil and crop productivity and assist in supporting agricultural methods to get sustainable yield. (
Selvaraj and Vinod, 2023;
Mohan, 2023).
The combination of AI and CA will help to offer efficient, sustainable and accurate solutions to current farming problems (Fig 1).
Research gap
Irrespective of these extraordinary advances in AI and CA there has been little attempt to unite the two. The possibilities of learni ng such areas are widely researched in separate spheres, yet not combined in a comprehensive way.
Objective
This review will look at the ways AI can be applied in enhancing Conservation Agriculture and areas where future studies ought to be based.
This paper uses a systematic literature review methodology.
Data sources
Literature was collected from Scopus, Google Scholar, Science Direct and Web of Science.
Search strategy
Keywords included:
• Artificial Intelligence in agriculture
• Conservation Agriculture
• Precision farming
• Machine learning in crop management
Inclusion criteria
• Peer-reviewed articles
• Publications from 2015–2024
• Studies related to AI or CA
Exclusion criteria
• Irrelevant or duplicate records
Data analysis
A thematic analysis was conducted focusing on:
• AI applications in CA
• Benefits and limitations
• Future research opportunities
The AI systems analyse data with the sensors, drones and satellites to make decisions in agriculture. The prediction of crop harvest, moisture of soil and pests’ infestations is conducted through the machine learning, but model-based disease detection is carried out with assistance of deep learning strategy, like convolutional neural - networks
(Mohanty et al., 2016). Remote sensing technologies also enhance real-time monitoring of crops
(Liakos et al., 2018; Wolfert et al., 2017).
These insights are pooled together by decision support tools and are used to suggest sustainable and conservation focused interventions
(Wolfert et al., 2017) (Fig 2).
AI applications in conservation agriculture
Precision resource management
AI-based decision support systems optimize irrigation, fertilization and pesticide use by analyzing soil, crop and weather data. This reduces resource wastage and improves efficiency (
Kamilaris and Prenafeta-Boldu, 2018;
Ramesh et al., 2020; Manonmani, 2022).
Table 1 presents a detailed overview of these AI applications in the context of soil/ resource optimization. It quantifies the application of new technologies, like machine learning, remote sensing and decision-support tools for tracking soil health conditions; managing irrigation; optimizing nutrient inputs, etc. These strategies together promote input efficiency with sustainable soil management.
Monitoring of crop health and crop disease detection
The deep learning processing of images provides an opportunity to detect diseases in plants early enough, to restore crops when it is still in time, to minimize losses
(Mohanty et al., 2016; Singh et al., 2023; Mohan, 2023).
An organized overview of the AI-based solutions used for crop monitoring and protection is illustrated in Table 2
. It illustrates the use of methods like deep learning and computer vision to evaluate crop status, detect pests and diseases and estimate yield. These tools result in timely and informed decision-making processes, vital in order to minimize damage and enhance the general crop performance.
Soil health and moisture management
AI tools monitor soil moisture, nutrient levels and organic matter to support CA practices such as mulching and reduced tillage, improving soil fertility and water retention
(Pathak et al., 2019; Sahil and Bishnoi, 2023).
These practices increase soil stability, minimize erosion and enhance the capacity to retain water (
Sahil and Bishnoi, 2023;
Pathak et al., 2019) (Fig 3).
Weed and pest management
AI-enabled robotics can identify crops, weeds and pests accurately so that herbicides and pesticides can be applied precisely in the exact locations. This targeted method lowers chemical-intensive agriculture in the field and facilitates sustainable agricultural methods
(Slaughter et al., 2008; Basa 2024).
AI-supported field operations and automation is illustrated in Table 3. The technologies listed encompass robotic systems, autonomous vehicles, precision application methods for inputs that can have a role in effective weed and pest control. These innovations allow them to rely less on chemical treatments and practice more sustainable agriculture.
Climate-adaptive farming
Climatic variability presents a serious threat to the agricultural industry. Predictor models used by AI use historical and real-time weather data to help in crop selection, planting and rotation of crop decisions. This enhances the strength of CA systems with dynamic environmental conditions
(Pathak et al., 2019; Wolfert et al., 2017).
The overview of AI-based decision-making tools for climate resilience is given in the Table 4. These range from predictive weather analysis and risk assessment models to crop planning systems and market forecasting tools. These enable farmers to adapt through the environmental data wave, while ensuring that conservation agriculture becomes sustainable and economically viable.
Data-driven decision support systems
The use of AI can integrate data at farms and regions to facilitate decision-making and policy generation
(Ramesh et al., 2020; Bharvey and Sharma, 2024).
The implementation of AI in Conservation Agriculture will be a paradigm shift to the sustainable agricultural system. AI is helping to use resources more efficiently, minimize the environmental effects and be more productive, as it makes interventions more precise and data-driven.
However, there are other aspects such as high cost of implementation, data and technical skills, particularly in developing countries and a host of others that have also contributed to non-adoption. The AI systems need strong datasets and infrastructure, which is not always available.
The future research has shown that farmers, the development of infrastructure and favourable policy frameworks should be the key to success (
Manonmani, 2022;
Bharvey and Sharma, 2024). These limitations are to be addressed to size AI-based CA systems.
