Digital Agriculture, Smart Farming and Crop Monitoring

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.6	6.3	2011	18 Days	CHF 2600	Submit
AgriEngineering agriengineering	3.0	4.7	2019	20.6 Days	CHF 1600	Submit
Agronomy agronomy	3.4	6.7	2011	17.2 Days	CHF 2600	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Automation automation	2.0	4.1	2020	23.4 Days	CHF 1200	Submit
Crops crops	1.9	2.4	2021	23.5 Days	CHF 1200	Submit
Robotics robotics	3.3	7.7	2012	21.8 Days	CHF 1800	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit

18 pages, 2886 KiB

Open AccessArticle

Hybrid LSTM Method for Multistep Soil Moisture Prediction Using Historical Soil Moisture and Weather Data

by Deus F. Kandamali, Erin Porter, Wesley M. Porter, Alex McLemore, Denis O. Kiobia, Ali P. Tavandashti and Glen C. Rains

AgriEngineering 2025, 7(8), 260; https://doi.org/10.3390/agriengineering7080260 - 12 Aug 2025

Viewed by 41

Abstract

Soil moisture prediction is a key parameter for effective irrigation scheduling and water use efficiency. However, accurate long-term prediction remains challenging, as most existing models excel in short- to medium-term prediction but struggle to capture the complex temporal dependencies and non-linear interactions of [...] Read more.

Soil moisture prediction is a key parameter for effective irrigation scheduling and water use efficiency. However, accurate long-term prediction remains challenging, as most existing models excel in short- to medium-term prediction but struggle to capture the complex temporal dependencies and non-linear interactions of soil moisture variables over extended horizons. This study proposes a hybrid soil moisture prediction method, integrating a long short-term memory (LSTM) network and extreme gradient boosting (XGBoost) model for multistep soil moisture prediction at 24 h, 72 h, and 168 h horizons. The LSTM captures temporal dependencies and extracts high-level features from the dataset, which are then used by XGBoost for final predictions. The study uses real-world data from the D.A.T.A (Demonstrating Applied Technology in Agriculture) research farm at ABAC (Abraham Baldwin Agricultural College) Tifton, GA, USA, utilizing watermark soil moisture sensors and weather station’s data installed on the farm. Results show that the proposed method outperforms other hybrid models, achieving R² values of 98.67%, 98.54%, and 98.56% for 24, 72, and 168 h predictions, respectively. The study findings highlight that LSTM-XGBoost offers a precise long-term soil moisture prediction, making it a practical tool for real-time irrigation scheduling, enhancing water use efficiency in precision agriculture. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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18 pages, 2151 KiB

Open AccessArticle

Drone-Assisted Plant Stress Detection Using Deep Learning: A Comparative Study of YOLOv8, RetinaNet, and Faster R-CNN

by Yousef-Awwad Daraghmi, Waed Naser, Eman Yaser Daraghmi and Hacene Fouchal

AgriEngineering 2025, 7(8), 257; https://doi.org/10.3390/agriengineering7080257 - 11 Aug 2025

Viewed by 167

Abstract

Drones have been widely used in precision agriculture to capture high-resolution images of crops, providing farmers with advanced insights into crop health, growth patterns, nutrient deficiencies, and pest infestations. Although several machine and deep learning models have been proposed for plant stress and [...] Read more.

Drones have been widely used in precision agriculture to capture high-resolution images of crops, providing farmers with advanced insights into crop health, growth patterns, nutrient deficiencies, and pest infestations. Although several machine and deep learning models have been proposed for plant stress and disease detection, their performance regarding accuracy and computational time still requires improvement, particularly under limited data. Therefore, this paper aims to address these challenges by conducting a comparative analysis of three State-of-the-Art object detection deep learning models: YOLOv8, RetinaNet, and Faster R-CNN, and their variants to identify the model with the best performance. To evaluate the models, the research uses a real-world dataset from potato farms containing images of healthy and stressed plants, with stress resulting from biotic and abiotic factors. The models are evaluated under limited conditions with original data of size 360 images and expanded conditions with augmented data of size 1560 images. The results show that YOLOv8 variants outperform the other models by achieving larger mAP@50 values and lower inference times on both the original and augmented datasets. The YOLOv8 variants achieve mAP@50 ranging from 0.798 to 0.861 and inference times ranging from 11.8 ms to 134.3 ms, while RetinaNet variants achieve mAP@50 ranging from 0.587 to 0.628 and inference times ranging from 118.7 ms to 158.8 ms, and Faster R-CNN variants achieve mAP@50 ranging from 0.587 to 0.628 and inference times ranging from 265 ms to 288 ms. These findings highlight YOLOv8’s robustness, speed, and suitability for real-time aerial crop monitoring, particularly in data-constrained environments. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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25 pages, 2915 KiB

Open AccessArticle

Multi-Model Identification of Rice Leaf Diseases Based on CEL-DL-Bagging

by Zhenghua Zhang, Rufeng Wang and Siqi Huang

AgriEngineering 2025, 7(8), 255; https://doi.org/10.3390/agriengineering7080255 - 7 Aug 2025

Viewed by 219

Abstract

This study proposes CEL-DL-Bagging (Cross-Entropy Loss-optimized Deep Learning Bagging), a multi-model fusion framework that integrates cross-entropy loss-weighted voting with Bootstrap Aggregating (Bagging). First, we develop a lightweight recognition architecture by embedding a salient position attention (SPA) mechanism into four base networks (YOLOv5s-cls, EfficientNet-B0, [...] Read more.

This study proposes CEL-DL-Bagging (Cross-Entropy Loss-optimized Deep Learning Bagging), a multi-model fusion framework that integrates cross-entropy loss-weighted voting with Bootstrap Aggregating (Bagging). First, we develop a lightweight recognition architecture by embedding a salient position attention (SPA) mechanism into four base networks (YOLOv5s-cls, EfficientNet-B0, MobileNetV3, and ShuffleNetV2), significantly enhancing discriminative feature extraction for disease patterns. Our experiments show that these SPA-enhanced models achieve consistent accuracy gains of 0.8–1.7 percentage points, peaking at 97.86%. Building on this, we introduce DB-CEWSV—an ensemble framework combining Deep Bootstrap Aggregating (DB) with adaptive Cross-Entropy Weighted Soft Voting (CEWSV). The system dynamically optimizes model weights based on their cross-entropy performance, using SPA-augmented networks as base learners. The final integrated model attains 98.33% accuracy, outperforming the strongest individual base learner by 0.48 percentage points. Compared with single models, the ensemble learning algorithm proposed in this study led to better generalization and robustness of the ensemble learning model and better identification of rice diseases in the natural background. It provides a technical reference for applying rice disease identification in practical engineering. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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20 pages, 9066 KiB

Open AccessArticle

Dynamic Modeling of Poultry Litter Composting in High Mountain Climates Using System Identification Techniques

by Alvaro A. Patiño-Forero, Fabian Salazar-Caceres, Harrynson Ramirez-Murillo, Fabiana F. Franceschi, Ricardo Rincón and Geraldynne Sierra-Rueda

Automation 2025, 6(3), 36; https://doi.org/10.3390/automation6030036 - 5 Aug 2025

Viewed by 267

Abstract

Poultry waste composting is a necessary technique for agricultural farm sustainability. Composting is a dynamic process influenced by multiple variables. Humidity and temperature play fundamental roles in analyzing its different phases according to the environment and composting technique. Current developments for monitoring these [...] Read more.

Poultry waste composting is a necessary technique for agricultural farm sustainability. Composting is a dynamic process influenced by multiple variables. Humidity and temperature play fundamental roles in analyzing its different phases according to the environment and composting technique. Current developments for monitoring these variables include automation via intelligent Internet of Things (IoT)-based sensor networks for variable tracking. These advancements serve as efficient tools for modeling that facilitate the simulation and prediction of composting process variables to improve system efficiency. Therefore, this paper presents the dynamic modeling of composting via forced aeration processes in high-mountain climates, with the intent of estimating biomass temperature dynamics in different phases using system identification techniques. To this end, four dynamic model estimation structures are employed: transfer function (TF), state space (SS), process (P), and Hammerstein–Wiener (HW). The and model quality, fitting results, and standard error metrics of the different models found in each phase are assessed through residual analysis from each structure by validation with real system data. Our results show that the second-order underdamped multiple-input–single-output (MISO) process model with added noise demonstrates the best fit and validation performance. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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20 pages, 2990 KiB

Open AccessArticle

Examination of Interrupted Lighting Schedule in Indoor Vertical Farms

by Dafni D. Avgoustaki, Vasilis Vevelakis, Katerina Akrivopoulou, Stavros Kalogeropoulos and Thomas Bartzanas

AgriEngineering 2025, 7(8), 242; https://doi.org/10.3390/agriengineering7080242 - 1 Aug 2025

Viewed by 337

Abstract

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial [...] Read more.

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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18 pages, 4389 KiB

Open AccessArticle

Acoustic Wave Propagation Characteristics of Maize Seed and Surrounding Region with the Double Media of Seed–Soil

by Yadong Li, Caiyun Lu, Hongwen Li, Jin He, Zhinan Wang and Chengkun Zhai

Agriculture 2025, 15(14), 1540; https://doi.org/10.3390/agriculture15141540 - 17 Jul 2025

Viewed by 373

Abstract

When monitoring seed positions in soil using ultrasonic waves, the main challenge is obtaining acoustic wave characteristics at the seed locations. This study developed a three-dimensional ultrasonic model with the double media of seed–soil using the discrete element method to visualize signal variations [...] Read more.

When monitoring seed positions in soil using ultrasonic waves, the main challenge is obtaining acoustic wave characteristics at the seed locations. This study developed a three-dimensional ultrasonic model with the double media of seed–soil using the discrete element method to visualize signal variations and analyze propagation characteristics. The effects of the compression ratio (0/6/12%), excitation frequency (20/40/60 kHz), and amplitude (5/10/15 μm) on signal variation and attenuation were analyzed. The results show consistent trends: time/frequency domain signal intensity increased with a higher compression ratio and amplitude but decreased with frequency. Comparing ultrasonic signals at soil particles before and after the seed along the propagation path shows that the seed significantly absorbs and attenuates ultrasonic waves. Time domain intensity drops 93.99%, and first and residual wave frequency peaks decrease by 88.06% and 96.39%, respectively. Additionally, comparing ultrasonic propagation velocities in the double media of seed–soil and the single soil medium reveals that the velocity in the seed is significantly higher than that in the soil. At compression ratios of 0%, 6%, and 12%, the sound velocity in the seed is 990.47%, 562.72%, and 431.34% of that in the soil, respectively. These findings help distinguish seed presence and provide a basis for ultrasonic seed position monitoring after sowing. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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17 pages, 8639 KiB

Open AccessArticle

Route Optimization for UGVs: A Systematic Analysis of Applications, Algorithms and Challenges

by Dario Fernando Yépez-Ponce, William Montalvo, Ximena Alexandra Guamán-Gavilanes and Mauricio David Echeverría-Cadena

Appl. Sci. 2025, 15(12), 6477; https://doi.org/10.3390/app15126477 - 9 Jun 2025

Viewed by 679

Abstract

This research focuses on route optimization for autonomous ground vehicles, with key applications in precision agriculture, logistics and surveillance. Its goal is to create planning techniques that increase productivity and flexibility in changing settings. To achieve this, a PRISMA-based systematic literature review was [...] Read more.

This research focuses on route optimization for autonomous ground vehicles, with key applications in precision agriculture, logistics and surveillance. Its goal is to create planning techniques that increase productivity and flexibility in changing settings. To achieve this, a PRISMA-based systematic literature review was carried out, encompassing works published during the last five years in databases like IEEE Xplore, ScienceDirect and Scopus. The search focused on topics related to route optimization, unmanned ground vehicles and heuristic algorithms. From the analysis of 56 selected articles, trends, technologies and challenges in real-time route planning were identified. Fifty-seven percent of the recent studies focus on UGV optimization, with prominent applications in agriculture, aiming to maximize efficiency and reduce costs. Heuristic algorithms, such as Humpback Whale Optimization, Firefly Search and Particle Swarm Optimization, are commonly employed to solve complex search problems. The findings underscore the need for more flexible planning techniques that integrate spatiotemporal and curvature constraints, allowing systems to respond effectively to unforeseen changes. By increasing their effectiveness and adaptability in practical situations, our research helps to provide more reliable autonomous navigation solutions for crucial applications. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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19 pages, 3355 KiB

Open AccessArticle

RLDD-YOLOv11n: Research on Rice Leaf Disease Detection Based on YOLOv11

by Kui Fang, Rui Zhou, Nan Deng, Cheng Li and Xinghui Zhu

Agronomy 2025, 15(6), 1266; https://doi.org/10.3390/agronomy15061266 - 22 May 2025

Cited by 1 | Viewed by 1092

Abstract

Rice disease identification plays a critical role in ensuring yield stability, enabling precise prevention and control, and promoting agricultural intelligence. However, existing approaches rely heavily on manual inspection, which is labor-intensive and inefficient. Moreover, the significant variability in disease features poses further challenges [...] Read more.

Rice disease identification plays a critical role in ensuring yield stability, enabling precise prevention and control, and promoting agricultural intelligence. However, existing approaches rely heavily on manual inspection, which is labor-intensive and inefficient. Moreover, the significant variability in disease features poses further challenges to accurate recognition. To address these issues, this paper proposes a novel rice leaf disease detection model—RLDD-YOLOv11n. First, the improved RLDD-YOLOv11n integrates the SCSABlock residual attention module into the neck layer to enhance multi-semantic information fusion, thereby improving the detection capability for small disease targets. Second, recognizing the limitations of the native upsampling module in YOLOv11n in reconstructing rice-disease-related features, the CARAFE upsampling module is incorporated. Finally, a rice leaf disease dataset focusing on three common diseases—Bacterial Blight, Rice Blast, and Brown Spot—was constructed. The experimental results demonstrate the effectiveness of the proposed improvements. RLDD-YOLOv11n achieved a mean Average Precision (mAP) of 88.3%, representing a 2.8% improvement over the baseline model. Furthermore, compared with existing mainstream lightweight YOLO models, RLDD-YOLOv11n exhibits a superior detection performance and robustness. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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19 pages, 359 KiB

Open AccessReview

Applicability of Technological Tools for Digital Agriculture with a Focus on Estimating the Nutritional Status of Plants

by Bianca Cavalcante da Silva, Renato de Mello Prado, Cid Naudi Silva Campos, Fábio Henrique Rojo Baio, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro and Dthenifer Cordeiro Santana

AgriEngineering 2025, 7(5), 161; https://doi.org/10.3390/agriengineering7050161 - 19 May 2025

Viewed by 1558

Abstract

The global transition to a digital era is crucial for society, as most daily activities are driven by digital technologies aimed at enhancing productivity and efficiency in the production of food, fibers, and bioenergy. However, the segregation of digital techniques and equipment in [...] Read more.

The global transition to a digital era is crucial for society, as most daily activities are driven by digital technologies aimed at enhancing productivity and efficiency in the production of food, fibers, and bioenergy. However, the segregation of digital techniques and equipment in both rural and urban areas poses significant obstacles to technological efforts aimed at combating hunger, ensuring sustainable agriculture, and fostering innovations aligned with the United Nations Sustainable Development Goals (SDGs 02 and 09). Rural regions, which are often less connected to technological advancements, require digital transformation to shift from subsistence farming to market-integrated production. Recent efforts to expand digitalization in these areas have shown promising results. Digital agriculture encompasses terms such as artificial intelligence (AI), the Internet of Things (IoT), big data, and precision agriculture integrating information and communication with geospatial and satellite technologies to manage and visualize natural resources and agricultural production. This digitalization involves both internal and external property management through data analysis related to location, climate, phytosanitary status, and consumption. By utilizing sensors integrated into unmanned aerial vehicles (UAVs) and connected to mobile devices and machinery, farmers can monitor animals, soil, water, and plants, facilitating informed decision-making. An important limitation in studies on nutritional diagnostics is the lack of accuracy validation based on plant responses, particularly in terms of yield. This issue is observed even in conventional leaf tissue analysis methods. The absence of such validation raises concerns about the reliability of digital tools under real field conditions. To ensure the effectiveness of spectral reflectance-based diagnostics, it is essential to conduct additional studies in commercial fields across different regions. These studies are crucial to confirm the accuracy of these methods and to strengthen the development of digital and precision agriculture. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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27 pages, 8920 KiB

Open AccessArticle

Advancing Rice Disease Detection in Farmland with an Enhanced YOLOv11 Algorithm

by Hongxin Teng, Yudi Wang, Wentao Li, Tao Chen and Qinghua Liu

Sensors 2025, 25(10), 3056; https://doi.org/10.3390/s25103056 - 12 May 2025

Cited by 1 | Viewed by 1239

Abstract

Smart rice disease detection is a key part of intelligent agriculture. To address issues like low efficiency, poor accuracy, and high costs in traditional methods, this paper introduces an enhanced lightweight version of the YOLOv11-RD algorithm, enhancing multi-scale feature extraction through the integration [...] Read more.

Smart rice disease detection is a key part of intelligent agriculture. To address issues like low efficiency, poor accuracy, and high costs in traditional methods, this paper introduces an enhanced lightweight version of the YOLOv11-RD algorithm, enhancing multi-scale feature extraction through the integration of the enhanced LSKAC attention mechanism and the SPPF module. It also lowers computational complexity and enhances local feature capture through the C3k2-CFCGLU block. The C3k2-CSCBAM block in the neck region reduces the training overhead and boosts target learning in complex backgrounds. Additionally, a lightweight 320 × 320 LSDECD detection head improves small-object detection. Experiments on a rice disease dataset extracted from agricultural operation videos demonstrate that, compared to YOLOv11n, the algorithm improves mAP50 and mAP50-95 by 2.7% and 11.5%, respectively, while reducing the model parameters by 4.58 M and the computational load by 1.1 G. The algorithm offers significant advantages in lightweight design and real-time performance, outperforming other classical object detection algorithms and providing an optimal solution for real-time field diagnosis. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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20 pages, 3955 KiB

Open AccessArticle

Lightweight Pepper Disease Detection Based on Improved YOLOv8n

by Yuzhu Wu, Junjie Huang, Siji Wang, Yujian Bao, Yizhe Wang, Jia Song and Wenwu Liu

AgriEngineering 2025, 7(5), 153; https://doi.org/10.3390/agriengineering7050153 - 12 May 2025

Viewed by 823

Abstract

China is the world’s largest producer of chili peppers, which occupy particularly important economic and social values in various fields such as medicine, food, and industry. However, during its production process, chili peppers are affected by pests and diseases, resulting in significant yield [...] Read more.

China is the world’s largest producer of chili peppers, which occupy particularly important economic and social values in various fields such as medicine, food, and industry. However, during its production process, chili peppers are affected by pests and diseases, resulting in significant yield reduction due to the temperature and environment. In this study, a lightweight pepper disease identification method, DD-YOLO, based on the YOLOv8n model, is proposed. First, the deformable convolutional module DCNv2 (Deformable ConvNetsv2) and the inverted residual mobile block iRMB (Inverted Residual Mobile Block) are introduced into the C2F module to improve the accuracy of the sampling range and reduce the computational amount. Secondly, the DySample sampling operator (Dynamic Sample) is integrated into the head network to reduce the amount of data and the complexity of computation. Finally, we use Large Separable Kernel Attention (LSKA) to improve the SPPF module (Spatial Pyramid Pooling Fast) to enhance the performance of multi-scale feature fusion. The experimental results show that the accuracy, recall, and average precision of the DD-YOLO model are 91.6%, 88.9%, and 94.4%, respectively. Compared with the base network YOLOv8n, it improves 6.2, 2.3, and 2.8 percentage points, respectively. The model weight is reduced by 22.6%, and the number of floating-point operations per second is improved by 11.1%. This method provides a technical basis for intensive cultivation and management of chili peppers, as well as efficiently and cost-effectively accomplishing the task of identifying chili pepper pests and diseases. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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20 pages, 29897 KiB

Open AccessArticle

Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM

by Yong Dong, Hongyan Wang, Yuan Zhang, Xin Du, Qiangzi Li, Yueting Wang, Yunqi Shen, Sichen Zhang, Jing Xiao, Jingyuan Xu, Sifeng Yan, Shuguang Gong and Haoxuan Hu

Agriculture 2025, 15(9), 976; https://doi.org/10.3390/agriculture15090976 - 30 Apr 2025

Viewed by 753

Abstract

Accurately extracting parcels from satellite images is crucial in precision agriculture. Traditional edge detection fails in complex scenes with difficult post-processing, and deep learning models are time-consuming in terms of sample preparation and less transferable. Based on this, we designed a method combining [...] Read more.

Accurately extracting parcels from satellite images is crucial in precision agriculture. Traditional edge detection fails in complex scenes with difficult post-processing, and deep learning models are time-consuming in terms of sample preparation and less transferable. Based on this, we designed a method combining multi-resolution remote sensing images based on the Segment Anything Model (SAM). Using cropland masking, overlap prediction and post-processing, we achieved 10 m-resolution parcel extraction with SAM, with performance in plain areas comparable to existing deep learning models (P: 0.89, R: 0.91, F1: 0.91, IoU: 0.87). Notably, in hilly regions with fragmented cultivated land, our approach even outperformed these models (P: 0.88, R: 0.76, F1: 0.81, IoU: 0.69). Subsequently, the 10 m parcels results were utilized to crop the high-resolution image. Based on the histogram features and internal edge features of the parcels, used to determine whether to segment downward or not, and at the same time, by setting the adaptive parameters of SAM, sub-meter parcel extraction was finally realized. Farmland boundaries extracted from high-resolution images can more accurately characterize the actual parcels, which is meaningful for farmland production and management. This study extended the application of deep learning large models in remote sensing, and provided a simple and fast method for accurate extraction of parcels boundaries. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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23 pages, 4175 KiB

Open AccessArticle

Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco

by Mohamed Arame, Issam Meftah Kadmiri, Francois Bourzeix, Yahya Zennayi, Rachid Boulamtat and Abdelghani Chehbouni

Agronomy 2025, 15(5), 1106; https://doi.org/10.3390/agronomy15051106 - 30 Apr 2025

Viewed by 741

Abstract

This study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. It is motivated by the potential of hyperspectral imaging, once properly combined with machine learning, to enhance the accuracy of pest detection. Originality consists [...] Read more.

This study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. It is motivated by the potential of hyperspectral imaging, once properly combined with machine learning, to enhance the accuracy of pest detection. Originality consists of the application of these techniques to chickpea plants in controlled laboratory conditions using a natural infestation protocol, something not previously explored. The two major methodologies adopted in the approach are as follows: (1) spectral feature-based classification using hyperspectral data within the 400–1000 nm range, wherein a random forest classifier is trained to classify a plant as healthy or infested with eggs or larvae. Dimensionality reduction methods such as principal component analysis (PCA) and kernel principal component analysis (KPCA) were tried, and the best classification accuracies (over 80%) were achieved. (2) VI-based classification, leveraging indices associated with plant health, such as NDVI, EVI, and GNDVI. A support vector machine and random forest classifiers effectively classified healthy and infested plants based on these indices, with over 81% classification accuracies. The main objective was to design an integrated early pest detection framework using advanced imaging and machine learning techniques. Results show that both approaches have resulted in high classification accuracy, highlighting the potential of this approach in precision agriculture for timely pest management interventions. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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15 pages, 7102 KiB

Open AccessArticle

Non-Contact Detection of Wine Grape Load Volume in Hopper During Mechanical Harvesting

by Haowei Liu, Xiu Wang, Jian Song, Mingzhou Chen, Cuiling Li and Changyuan Zhai

Agriculture 2025, 15(9), 918; https://doi.org/10.3390/agriculture15090918 - 23 Apr 2025

Viewed by 489

Abstract

Issues of poor real-time performance and low accuracy in the detection of load volume in the hopper during the mechanized harvesting of wine grapes are addressed in this study through the development of a proposed volume detection method based on ultrasonic sensors. First, [...] Read more.

Issues of poor real-time performance and low accuracy in the detection of load volume in the hopper during the mechanized harvesting of wine grapes are addressed in this study through the development of a proposed volume detection method based on ultrasonic sensors. First, the ultrasonic sensor beamwidth and detection height were determined through calibration tests. Next, a test bench was used to explore the influence of the number of ultrasonic sensors and conveying speed on the detected grape pile height. Data-based regression and hopper configuration-based geometric models correlating grape load volume with detected pile height were subsequently constructed; their accuracies were compared using test bench experiments to identify the optimal detection scheme. The regression model was more accurate than the geometric model under the considered conveying speeds with a maximum relative error of 8.0% for the former. Finally, field tests determined that the average grape load volume detection error during actual harvesting was 14.4%. Therefore, this study provides an effective solution for the detection of grape load volume in the hopper during mechanized harvesting and establishes a theoretical basis for the development of intelligent grape harvesting methods. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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