Submit to Special Issue Submit Abstract to Special Issue Review for Agronomy Propose a Special Issue

Journal Menu

Journal Browser

Innovations in Agriculture for Sustainable Agro-Systems

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 47134

Share This Special Issue

Special Issue Editors

Dr. Ioannis Vagelas

E-Mail Website
Guest Editor

Department of Agriculture Crop Production and Rural Environment, University of Thessaly, 382 21 Volos, Greece
Interests: plant pathology; biological control; applied statistics
Special Issues, Collections and Topics in MDPI journals

Dr. Christos Lykas

E-Mail Website
Guest Editor

Department of Agriculture Crop Production and Rural Environment, University of Thessaly, 382 21 Volos, Greece
Interests: plant growth models; soilless culture; biodiversity; biostimulants
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture has changed dramatically and has been improved due to new technologies. Smart technologies, such as artificial intelligence, robotics, and the Internet of Things, play an important role in achieving enhanced productivity. However, their implications on the ecosystem are unknown or underestimated.

In addition to favoring production, innovations in agriculture may have many positive environmental impacts such as reductions in agrochemicals application, saving water and energy, waste reduction, and preventing water, soil, and air pollution.

Undoubtedly, there are no shortages of uses for these technologies; multispectral cameras, sensors, and drones are combined with appropriate software and robotic or conventional systems to remove weeds or for the precise application of herbicides and fertilizers. Smart agriculture approaches already include disease prediction models to adjust the greenhouse environment or reduce infections to aid growers in early disease detection.

However, some of the smart technologies that are already in use may have undesirable impacts on the environment, as well as on wider society. For this, a responsible innovation should be further developed in order provide the most benefits in agriculture, while at the same time, it should be environmentally friendly. In light of this method of development, the possibilities and limitations of innovations should be explored.

This Special Issue aims to highlight responsible innovation and contribute to the further development of new ideas for the establishment of sustainable agro-systems.

Dr. Ioannis Vagelas
Dr. Christos Lykas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

precision and smart agriculture
environmental impact
process-based crop model
climate change
open and big data
predictive and decision support systems
IoT for biodiversity and sustainability
Internet of Things
robot–plant interaction
remote sensing
monitoring and control of diseases, pests and weeds
plant detection and monitoring

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Editorial

Jump to: Research, Review

7 pages, 1987 KB

Open AccessEditorial

Innovations in Agriculture for Sustainable Agro-Systems

by Christos Lykas and Ioannis Vagelas

Agronomy 2023, 13(9), 2309; https://doi.org/10.3390/agronomy13092309 - 1 Sep 2023

Cited by 5 | Viewed by 2874

Abstract

Agriculture has changed dramatically and has been improved due to new technologies [...] Full article

(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)

► Show Figures

Figure 1

Research

Jump to: Editorial, Review

21 pages, 5485 KB

Open AccessArticle

Efficient Olive Leaf Disease Detection Using Composite Feature Selection and Ensemble Learning

by Hakan Gunduz

Agronomy 2026, 16(11), 1057; https://doi.org/10.3390/agronomy16111057 - 27 May 2026

Viewed by 79

Abstract

Early and reliable detection of plant diseases is critical for sustaining agricultural productivity and reducing economic losses. In olive cultivation, peacock eye disease poses a significant threat by adversely affecting leaf health and crop yield. While deep learning models have demonstrated strong performance in plant disease detection, their reliance on high-dimensional feature representations often leads to increased computational cost and limited deployability in real-world agricultural settings. This study proposes an efficient and robust olive leaf disease classification framework that integrates deep feature extraction, devised composite filter-based feature selection, and ensemble learning. Deep features are extracted from olive leaf images using transfer learning with ResNet101 and MobileNet architectures. To address feature redundancy and computational inefficiency, multiple filter-based selection strategies—including mutual information, Chi-square, F-score, and five devised composite selectors (score fusion, union, intersection, hybrid, and class-wise filtering)—are employed to generate compact and informative feature subsets of fixed sizes (32, 64, and 128 features). The selected features are evaluated using k-NN, SVM, and LightGBM classifiers under stratified 5-fold cross-validation. Experimental results demonstrate that competitive and near-baseline performance can be achieved with substantially reduced feature dimensionality. In particular, using only 128 selected features, the proposed approach attains up to 0.988 accuracy and 0.976 MCC, closely matching the performance obtained with full deep feature vectors. Furthermore, voting-based ensemble strategies, including iterative majority voting and hybrid GA–BO fusion, further enhance robustness, achieving the highest mean accuracy of 0.9916 among the evaluated ensemble configurations. These findings highlight the effectiveness of the proposed composite filter-based selection and ensemble framework as a practical, lightweight, and accurate solution for olive leaf disease detection, suitable for deployment in precision agriculture and resource-constrained environments. Full article

(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)

► Show Figures

Figure 1

23 pages, 10699 KB

Open AccessArticle

YOLOv11-IMP: Anchor-Free Multiscale Detection Model for Accurate Grape Yield Estimation in Precision Viticulture

by Shaoxiong Zheng, Xiaopei Yang, Peng Gao, Qingwen Guo, Jiahong Zhang, Shihong Chen and Yunchao Tang

Agronomy 2026, 16(3), 370; https://doi.org/10.3390/agronomy16030370 - 2 Feb 2026

Viewed by 862

Abstract

Estimating grape yields in viticulture is hindered by persistent challenges, including strong occlusion between grapes, irregular cluster morphologies, and fluctuating illumination throughout the growing season. This study introduces YOLOv11-IMP, an improved multiscale anchor-free detection framework extending YOLOv11, tailored to vineyard environments. Its architecture comprises five specialized components: (i) a viticulture-oriented backbone employing cross-stage partial fusion with depthwise convolutions for enriched feature extraction, (ii) a bifurcated neck enhanced by large-kernel attention to expand the receptive field coverage, (iii) a scale-adaptive anchor-free detection head for robust multiscale localization, (iv) a cross-modal processing module integrating visual features with auxiliary textual descriptors to enable fine-grained cluster-level yield estimation, and (v) aross multiple scales. This work evaluated YOLOv11-IMP on five grape varieties collecten augmented spatial pyramid pooling module that aggregates contextual information acd under diverse environmental conditions. The framework achieved 94.3% precision and 93.5% recall for cluster detection, with a mean absolute error (MAE) of 0.46 kg per vine. The robustness tests found less than 3.4% variation in accuracy across lighting and weather conditions. These results demonstrate that YOLOv11-IMP can deliver high-fidelity, real-time yield data, supporting decision-making for precision viticulture and sustainable agricultural management. Full article

(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)

► Show Figures

Figure 1

21 pages, 7841 KB

Open AccessArticle

Study on Predicting Cotton Boll Opening Rate Based on UAV Multispectral Imagery

by Chen Xue, Lingbiao Kong, Shengde Chen, Changfeng Shan, Lechun Zhang, Cancan Song, Yubin Lan and Guobin Wang

Agronomy 2026, 16(2), 162; https://doi.org/10.3390/agronomy16020162 - 8 Jan 2026

Viewed by 495

Abstract

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing. Full article

(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)

► Show Figures

Figure 1

22 pages, 8136 KB

Open AccessArticle

Application of Drone Surveillance for Advance Agriculture Monitoring by Android Application Using Convolution Neural Network

by Sabab Ali Shah, Ghulam Mustafa Lakho, Hareef Ahmed Keerio, Muhammad Nouman Sattar, Gulzar Hussain, Mujahid Mehdi, Rahim Bux Vistro, Eman A. Mahmoud and Hosam O. Elansary

Agronomy 2023, 13(7), 1764; https://doi.org/10.3390/agronomy13071764 - 29 Jun 2023

Cited by 62 | Viewed by 8580

Abstract

Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health. Full article

(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)

► Show Figures

Figure 1

13 pages, 9110 KB

Open AccessArticle

Monitoring Patch Expansion Amends to Evaluate the Effects of Non-Chemical Control on the Creeping Perennial Cirsium arvense (L.) Scop. in a Spring Wheat Crop

by Marian Malte Weigel, Sabine Andert and Bärbel Gerowitt

Agronomy 2023, 13(6), 1474; https://doi.org/10.3390/agronomy13061474 - 26 May 2023

Cited by 8 | Viewed by 2721

Abstract

The creeping perennial weed species Cirsium arvense (L.) Scop. occurs in patches. Expanding creeping roots allow these patches to increase their covered area. This characteristic has rarely been addressed when investigating the effects of control options in arable fields. We designed a three-year field experiment (2019–2021) in north-eastern Germany, accounting for existing patch patterns. The experimental setup included an untreated control, a competition treatment (cover crop, CC), two disturbance treatments by mouldboard ploughing (PL), root cutting (RC), and four combined applications (RC + CC, PL + CC, PL + RC, PL + RC + CC). Root cutting was performed by a prototype tillage machine produced by “Kverneland”. Plots were defined by the species growth pattern and mapped by GPS and UAV. The experiment investigates the thistle response variables: “Expansion”, “Density”, “Coverage”, and “Height”. Treatments including disturbance by ploughing (PL, PL + CC, PL + RC, PL + RC + CC) reduced “Density” by the factor 0.15 and “Expansion” by 0.25, while those without ploughing (CC, RC, RC + CC) only reduced “Density” by the factor 0.68 and “Expansion” by 0.71. Adding root cuttings or cover crops did not further increase the reduction effect of ploughing. Treatments with competition by cover crops impacted “Expansion” more clearly than “Density”. When cover crops were combined with root cutting (RC + CC), “Expansion” was almost additively reduced, resulting in a reduction comparable to that of ploughing. The “Height” of the shoots was significantly reduced in four treatments (PL, RC + CC, PL + RC, PL + RC + CC), while “Coverage” did not change significantly. UAV patch monitoring proved to be accurate enough for thistle “Expansion” but not for thistle “Density” within the patch. The results of this study demand innovative research when controlling patch-forming creeping perennial weeds. The need for patches will limit small-scale experimental set ups. Full article

(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

30 pages, 3838 KB

Open AccessReview

Overview of Agricultural Machinery Automation Technology for Sustainable Agriculture

by Li Jiang, Boyan Xu, Naveed Husnain and Qi Wang

Agronomy 2025, 15(6), 1471; https://doi.org/10.3390/agronomy15061471 - 16 Jun 2025

Cited by 35 | Viewed by 14011

Abstract

Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production. Full article

(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)

► Show Figures

Figure 1

26 pages, 2925 KB

Open AccessReview

Crops Disease Detection, from Leaves to Field: What We Can Expect from Artificial Intelligence

by Youssef Lebrini and Alicia Ayerdi Gotor

Agronomy 2024, 14(11), 2719; https://doi.org/10.3390/agronomy14112719 - 18 Nov 2024

Cited by 16 | Viewed by 8711

Abstract

Agriculture is dealing with numerous challenges of increasing production while decreasing the amount of chemicals and fertilizers used. The intensification of agricultural systems has been linked to the use of these inputs which nevertheless have negative consequences for the environment. With new technologies, and progress in precision agriculture associated with decision support systems for farmers, the objective is to optimize their use. This review focused on the progress made in utilizing machine learning and remote sensing to detect and identify crop diseases that may help farmers to (i) choose the right treatment, the most adapted to a particular disease, (ii) treat diseases at early stages of contamination, and (iii) maybe in the future treat only where it is necessary or economically profitable. The state of the art has shown significant progress in the detection and identification of disease at the leaf scale in most of the cultivated species, but less progress is done in the detection of diseases at the field scale where the environment is complex and applied only in some field crops. Full article

(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)

► Show Figures

Graphical abstract

27 pages, 2820 KB

Open AccessReview

Agroclimatic and Phytosanitary Events and Emerging Technologies for Their Identification in Avocado Crops: A Systematic Literature Review

by Tomas Ramirez-Guerrero, Maria Isabel Hernandez-Perez, Marta S. Tabares, Alejandro Marulanda-Tobon, Eduart Villanueva and Alejandro Peña

Agronomy 2023, 13(8), 1976; https://doi.org/10.3390/agronomy13081976 - 26 Jul 2023

Cited by 11 | Viewed by 6169

Abstract

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. Full article

(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)

► Show Figures

Journal Menu

Journal Browser

Innovations in Agriculture for Sustainable Agro-Systems

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI