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34 pages, 14344 KiB

Open AccessArticle

FedBirdAg: A Low-Energy Federated Learning Platform for Bird Detection with Wireless Smart Cameras in Agriculture 4.0

by Samy Benhoussa, Gil De Sousa and Jean-Pierre Chanet

AI 2025, 6(4), 63; https://doi.org/10.3390/ai6040063 - 21 Mar 2025

Viewed by 183

Abstract

Birds can cause substantial damage to crops, directly affecting farmers’ productivity and profitability. As a result, detecting bird presence in crop fields is crucial for effective crop management. Traditional agricultural practices have used various tools and techniques to deter pest birds, while digital [...] Read more.

Birds can cause substantial damage to crops, directly affecting farmers’ productivity and profitability. As a result, detecting bird presence in crop fields is crucial for effective crop management. Traditional agricultural practices have used various tools and techniques to deter pest birds, while digital agriculture has advanced these efforts through Internet of Things (IoT) and artificial intelligence (AI) technologies. With recent advancements in hardware and processing chips, connected devices can now utilize deep convolutional neural networks (CNNs) for on-field image classification. However, training these models can be energy-intensive, especially when large amounts of data, such as images, need to be transmitted for centralized model training. Federated learning (FL) offers a solution by enabling local training on edge devices, reducing data transmission costs and energy demands while also preserving data privacy and achieving shared model knowledge across connected devices. This paper proposes a low-energy federated learning framework for a compact smart camera network designed to perform simple image classification for bird detection in crop fields. The results demonstrate that this decentralized approach achieves performance comparable to a centrally trained model while consuming at least 8 times less energy. Further efficiency improvements, with a minimal tradeoff in performance reduction, are explored through early stopping. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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27 pages, 7182 KiB

Open AccessArticle

Detection of Leaf Diseases in Banana Crops Using Deep Learning Techniques

by Nixon Jiménez, Stefany Orellana, Bertha Mazon-Olivo, Wilmer Rivas-Asanza and Iván Ramírez-Morales

AI 2025, 6(3), 61; https://doi.org/10.3390/ai6030061 - 17 Mar 2025

Viewed by 309

Abstract

Leaf diseases, such as Black Sigatoka and Cordana, represent a growing threat to banana crops in Ecuador. These diseases spread rapidly, impacting both leaf and fruit quality. Early detection is crucial for effective control measures. Recently, deep learning has proven to be a [...] Read more.

Leaf diseases, such as Black Sigatoka and Cordana, represent a growing threat to banana crops in Ecuador. These diseases spread rapidly, impacting both leaf and fruit quality. Early detection is crucial for effective control measures. Recently, deep learning has proven to be a powerful tool in agriculture, enabling more accurate analysis and identification of crop diseases. This study applied the CRISP-DM methodology, consisting of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. A dataset of 900 banana leaf images was collected—300 of Black Sigatoka, 300 of Cordana, and 300 of healthy leaves. Three pre-trained models (EfficientNetB0, ResNet50, and VGG19) were trained on this dataset. To improve performance, data augmentation techniques were applied using TensorFlow Keras’s ImageDataGenerator class, expanding the dataset to 9000 images. Due to the high computational demands of ResNet50 and VGG19, training was performed with EfficientNetB0. The models—EfficientNetB0, ResNet50, and VGG19—demonstrated the ability to identify leaf diseases in bananas, with accuracies of 88.33%, 88.90%, and 87.22%, respectively. The data augmentation increased the performance of EfficientNetB0 to 87.83%, but did not significantly improve its accuracy. These findings highlight the value of deep learning techniques for early disease detection in banana crops, enhancing diagnostic accuracy and efficiency. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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25 pages, 4169 KiB

Open AccessArticle

Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices?

by Efrain Noa-Yarasca, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari and Douglas R. Smith

AI 2025, 6(3), 58; https://doi.org/10.3390/ai6030058 - 13 Mar 2025

Viewed by 196

Abstract

Accurate and reliable crop yield prediction is essential for optimizing agricultural management, resource allocation, and decision-making, while also supporting farmers and stakeholders in adapting to climate change and increasing global demand. This study introduces an innovative approach to crop yield prediction by incorporating [...] Read more.

Accurate and reliable crop yield prediction is essential for optimizing agricultural management, resource allocation, and decision-making, while also supporting farmers and stakeholders in adapting to climate change and increasing global demand. This study introduces an innovative approach to crop yield prediction by incorporating spatially lagged spectral data (SLSD) through the spatial-lagged machine learning (SLML) model, an enhanced version of the spatial lag X (SLX) model. The research aims to show that SLSD improves prediction compared to traditional vegetation index (VI)-based methods. Conducted on a 19-hectare cornfield at the ARS Grassland, Soil, and Water Research Laboratory during the 2023 growing season, this study used five-band multispectral image data and 8581 yield measurements ranging from 1.69 to 15.86 Mg/Ha. Four predictor sets were evaluated: Set 1 (spectral bands), Set 2 (spectral bands + neighborhood data), Set 3 (spectral bands + VIs), and Set 4 (spectral bands + top VIs + neighborhood data). These were evaluated using the SLX model and four decision-tree-based SLML models (RF, XGB, ET, GBR), with performance assessed using R² and RMSE. Results showed that incorporating spatial neighborhood data (Set 2) outperformed VI-based approaches (Set 3), emphasizing the importance of spatial context. SLML models, particularly XGB, RF, and ET, performed best with 4–8 neighbors, while excessive neighbors slightly reduced accuracy. In Set 3, VIs improved predictions, but a smaller subset (10–15 indices) was sufficient for optimal yield prediction. Set 4 showed slight gains over Sets 2 and 3, with XGB and RF achieving the highest R² values. Key predictors included spatially lagged spectral bands (e.g., Green_lag, NIR_lag, RedEdge_lag) and VIs (e.g., CREI, GCI, NCPI, ARI, CCCI), highlighting the value of integrating neighborhood data for improved corn yield prediction. This study underscores the importance of spatial context in corn yield prediction and lays the foundation for future research across diverse agricultural settings, focusing on optimizing neighborhood size, integrating spatial and spectral data, and refining spatial dependencies through localized search algorithms. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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24 pages, 23486 KiB

Open AccessArticle

Influence of Model Size and Image Augmentations on Object Detection in Low-Contrast Complex Background Scenes

by Harman Singh Sangha and Matthew J. Darr

AI 2025, 6(3), 52; https://doi.org/10.3390/ai6030052 - 5 Mar 2025

Viewed by 304

Abstract

Background: Bigger and more complex models are often developed for challenging object detection tasks, and image augmentations are used to train a robust deep learning model for small image datasets. Previous studies have suggested that smaller models provide better performance compared to bigger [...] Read more.

Background: Bigger and more complex models are often developed for challenging object detection tasks, and image augmentations are used to train a robust deep learning model for small image datasets. Previous studies have suggested that smaller models provide better performance compared to bigger models for agricultural applications, and not all image augmentation methods contribute equally to model performance. An important part of these studies was also to define the scene of the image. Methods: A standard definition was developed to describe scenes in real-world agricultural datasets by reviewing various image-based machine-learning applications in the agriculture literature. This study primarily evaluates the effects of model size in both one-stage and two-stage detectors on model performance for low-contrast complex background applications. It further explores the influence of different photo-metric image augmentation methods on model performance for standard one-stage and two-stage detectors. Results: For one-stage detectors, a smaller model performed better than a bigger model. Whereas in the case of two-stage detectors, model performance increased with model size. In image augmentations, some methods considerably improved model performance and some either provided no improvement or reduced the model performance in both one-stage and two-stage detectors compared to the baseline. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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27 pages, 39555 KiB

Open AccessArticle

Development and Comparison of Artificial Neural Networks and Gradient Boosting Regressors for Predicting Topsoil Moisture Using Forecast Data

by Miriam Zambudio Martínez, Larissa Haringer Martins da Silveira, Rafael Marin-Perez and Antonio Fernando Skarmeta Gomez

AI 2025, 6(2), 41; https://doi.org/10.3390/ai6020041 - 19 Feb 2025

Viewed by 367

Abstract

Introduction: The Earth’s growing population is increasing resource consumption, heavily pressuring agriculture, which, currently, uses 70% of the world’s freshwater from rivers and lakes, which, themselves, comprise only 1% of the Earth’s water reserves. Combined with climate change, the situation is alarming. [...] Read more.

Introduction: The Earth’s growing population is increasing resource consumption, heavily pressuring agriculture, which, currently, uses 70% of the world’s freshwater from rivers and lakes, which, themselves, comprise only 1% of the Earth’s water reserves. Combined with climate change, the situation is alarming. These challenges drive Agriculture 4.0, which is focused on sustainable agricultural processes to optimise water use. Objective: Given this context, this study proposes a model, based on Artificial Intelligence (AI) techniques to predict topsoil moisture in a study area located in the south of the Iberian Peninsula, primarily an agricultural region facing recurrent droughts and water scarcity. Methods: To develop the model, a comparison between Artificial Neural Networks (ANNs) and Gradient Booster Regressors (GBRs) was conducted, and topsoil moisture data from seven probes distributed over the study area were used, in addition to several variables (temperature, relative humidity, solar radiation, wind speed, precipitation and evapotranspiration) from a selection of weather stations and ensemble forecasts from meteorological models. Results: The final GBR model, with a 0.01 learning rate, 5 max depth, and 350 estimators, predicted topsoil moisture with an average mean squared error (MSE) of 0.027 and a maximum difference between observed and predicted data of 20.09% in a two-year series (May 2022–June 2024). Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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18 pages, 11587 KiB

Open AccessArticle

The Detection and Counting of Olive Tree Fruits Using Deep Learning Models in Tacna, Perú

by Erbert Osco-Mamani, Oliver Santana-Carbajal, Israel Chaparro-Cruz, Daniel Ochoa-Donoso and Sylvia Alcazar-Alay

AI 2025, 6(2), 25; https://doi.org/10.3390/ai6020025 - 1 Feb 2025

Viewed by 1021

Abstract

Predicting crop performance is key to decision making for farmers and business owners. Tacna is the main olive-producing region in Perú, with an annual yield of 6.4 t/ha, mainly of the Sevillana variety. Recently, olive production levels have fluctuated due to severe weather [...] Read more.

Predicting crop performance is key to decision making for farmers and business owners. Tacna is the main olive-producing region in Perú, with an annual yield of 6.4 t/ha, mainly of the Sevillana variety. Recently, olive production levels have fluctuated due to severe weather conditions and disease outbreaks. These climatic phenomena are expected to continue in the coming years. The objective of the study was to evaluate the performance of the model in natural and specific environments of the olive grove and counting olive fruits using CNNs from images. Among the models evaluated, YOLOv8m proved to be the most effective (94.960), followed by YOLOv8s, Faster R-CNN and RetinaNet. For the mAP50-95 metric, YOLOv8m was also the most effective (0.775). YOLOv8m achieved the best performance with an RMSE of 402.458 and a coefficient of determination R² of (0.944), indicating a high correlation with the actual fruit count. As part of this study, a novel olive fruit dataset was developed to capture the variability under different fruit conditions. Concluded that the predicting crop from images requires consideration of field imaging conditions, color tones, and the similarity between olives and leaves. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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22 pages, 686 KiB

Open AccessArticle

AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases

by Oluwatoyin Joy Omole, Renata Lopes Rosa, Muhammad Saadi and Demóstenes Zegarra Rodriguez

AI 2024, 5(4), 2945-2966; https://doi.org/10.3390/ai5040142 - 16 Dec 2024

Viewed by 1031

Abstract

Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead [...] Read more.

Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead to substantial production losses. To address this challenge, we propose AgriNAS, a method that integrates a Neural Architecture Search (NAS) framework with an adaptive convolutional architecture specifically designed for plant pathology. AgriNAS employs a novel data augmentation strategy and a Spatial–Time Augmentation (STA) method, and it utilizes a multi-stage convolutional network that dynamically adapts to the complexity of the input data. The proposed AgriNAS leverages powerful GPU resources to handle the intensive computational tasks involved in NAS and model training. The framework incorporates a bi-level optimization strategy and entropy-based regularization to enhance model robustness and prevent overfitting. AgriNAS achieves classification accuracies superior to VGG-19 and a transfer learning method using convolutional neural networks. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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15 pages, 5569 KiB

Open AccessArticle

Comparative Analysis of Machine Learning Techniques Using RGB Imaging for Nitrogen Stress Detection in Maize

by Sumaira Ghazal, Namratha Kommineni and Arslan Munir

AI 2024, 5(3), 1286-1300; https://doi.org/10.3390/ai5030062 - 28 Jul 2024

Cited by 3 | Viewed by 2212

Abstract

Proper nitrogen management in crops is crucial to ensure optimal growth and yield maximization. While hyperspectral imagery is often used for nitrogen status estimation in crops, it is not feasible for real-time applications due to the complexity and high cost associated with it. [...] Read more.

Proper nitrogen management in crops is crucial to ensure optimal growth and yield maximization. While hyperspectral imagery is often used for nitrogen status estimation in crops, it is not feasible for real-time applications due to the complexity and high cost associated with it. Much of the research utilizing RGB data for detecting nitrogen stress in plants relies on datasets obtained under laboratory settings, which limits its usability in practical applications. This study focuses on identifying nitrogen deficiency in maize crops using RGB imaging data from a publicly available dataset obtained under field conditions. We have proposed a custom-built vision transformer model for the classification of maize into three stress classes. Additionally, we have analyzed the performance of convolutional neural network models, including ResNet50, EfficientNetB0, InceptionV3, and DenseNet121, for nitrogen stress estimation. Our approach involves transfer learning with fine-tuning, adding layers tailored to our specific application. Our detailed analysis shows that while vision transformer models generalize well, they converge prematurely with a higher loss value, indicating the need for further optimization. In contrast, the fine-tuned CNN models classify the crop into stressed, non-stressed, and semi-stressed classes with higher accuracy, achieving a maximum accuracy of 97% with EfficientNetB0 as the base model. This makes our fine-tuned EfficientNetB0 model a suitable candidate for practical applications in nitrogen stress detection. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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14 pages, 31064 KiB

Open AccessArticle

Enhancing Tuta absoluta Detection on Tomato Plants: Ensemble Techniques and Deep Learning

by Nikolaos Giakoumoglou, Eleftheria-Maria Pechlivani, Nikolaos Frangakis and Dimitrios Tzovaras

AI 2023, 4(4), 996-1009; https://doi.org/10.3390/ai4040050 - 20 Nov 2023

Cited by 6 | Viewed by 2960

Abstract

Early detection and efficient management practices to control Tuta absoluta (Meyrick) infestation is crucial for safeguarding tomato production yield and minimizing economic losses. This study investigates the detection of T. absoluta infestation on tomato plants using object detection models combined with ensemble techniques. [...] Read more.

Early detection and efficient management practices to control Tuta absoluta (Meyrick) infestation is crucial for safeguarding tomato production yield and minimizing economic losses. This study investigates the detection of T. absoluta infestation on tomato plants using object detection models combined with ensemble techniques. Additionally, this study highlights the importance of utilizing a dataset captured in real settings in open-field and greenhouse environments to address the complexity of real-life challenges in object detection of plant health scenarios. The effectiveness of deep-learning-based models, including Faster R-CNN and RetinaNet, was evaluated in terms of detecting T. absoluta damage. The initial model evaluations revealed diminishing performance levels across various model configurations, including different backbones and heads. To enhance detection predictions and improve mean Average Precision (mAP) scores, ensemble techniques were applied such as Non-Maximum Suppression (NMS), Soft Non-Maximum Suppression (Soft NMS), Non-Maximum Weighted (NMW), and Weighted Boxes Fusion (WBF). The outcomes shown that the WBF technique significantly improved the mAP scores, resulting in a 20% improvement from 0.58 (max mAP from individual models) to 0.70. The results of this study contribute to the field of agricultural pest detection by emphasizing the potential of deep learning and ensemble techniques in improving the accuracy and reliability of object detection models. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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19 pages, 3076 KiB

Open AccessArticle

A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features

by Ioannis D. Apostolopoulos, Mpesi Tzani and Sokratis I. Aznaouridis

AI 2023, 4(4), 812-830; https://doi.org/10.3390/ai4040041 - 27 Sep 2023

Cited by 15 | Viewed by 11804

Abstract

Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial intelligence can aid in assessing the quality of fruit using [...] Read more.

Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial intelligence can aid in assessing the quality of fruit using images. This paper presents a general machine learning model for assessing fruit quality using deep image features. This model leverages the learning capabilities of the recent successful networks for image classification called vision transformers (ViT). The ViT model is built and trained with a combination of various fruit datasets and taught to distinguish between good and rotten fruit images based on their visual appearance and not predefined quality attributes. The general model demonstrated impressive results in accurately identifying the quality of various fruits, such as apples (with a 99.50% accuracy), cucumbers (99%), grapes (100%), kakis (99.50%), oranges (99.50%), papayas (98%), peaches (98%), tomatoes (99.50%), and watermelons (98%). However, it showed slightly lower performance in identifying guavas (97%), lemons (97%), limes (97.50%), mangoes (97.50%), pears (97%), and pomegranates (97%). Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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14 pages, 3156 KiB

Open AccessArticle

Comparison of Various Nitrogen and Water Dual Stress Effects for Predicting Relative Water Content and Nitrogen Content in Maize Plants through Hyperspectral Imaging

by Hideki Maki, Valerie Lynch, Dongdong Ma, Mitchell R. Tuinstra, Masanori Yamasaki and Jian Jin

AI 2023, 4(3), 692-705; https://doi.org/10.3390/ai4030036 - 18 Aug 2023

Cited by 2 | Viewed by 2494

Abstract

Water and nitrogen (N) are major factors in plant growth and agricultural production. However, these are often confounded and produce overlapping symptoms of plant stress. The objective of this study is to verify whether the different levels of N treatment influence water status [...] Read more.

Water and nitrogen (N) are major factors in plant growth and agricultural production. However, these are often confounded and produce overlapping symptoms of plant stress. The objective of this study is to verify whether the different levels of N treatment influence water status prediction and vice versa with hyperspectral modeling. We cultivated 108 maize plants in a greenhouse under three-level N treatments in combination with three-level water treatments. Hyperspectral images were collected from those plants, then Relative Water Content (RWC), as well as N content, was measured as ground truth. A Partial Least Squares (PLS) regression analysis was used to build prediction models for RWC and N content. Then, their accuracy and robustness were compared according to the different N treatment datasets and different water treatment datasets, respectively. The results demonstrated that the PLS prediction for RWC using hyperspectral data was impacted by N stress difference (Ratio of Performance to Deviation; RPD from 0.87 to 2.27). Furthermore, the dataset with water and N dual stresses improved model accuracy and robustness (RPD from 1.69 to 2.64). Conversely, the PLS prediction for N content was found to be robust against water stress difference (RPD from 2.33 to 3.06). In conclusion, we suggest that water and N dual treatments can be helpful in building models with wide applicability and high accuracy for evaluating plant water status such as RWC. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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13 pages, 3794 KiB

Open AccessArticle

Application of Machine Learning for Insect Monitoring in Grain Facilities

by Querriel Arvy Mendoza, Lester Pordesimo, Mitchell Neilsen, Paul Armstrong, James Campbell and Princess Tiffany Mendoza

AI 2023, 4(1), 348-360; https://doi.org/10.3390/ai4010017 - 22 Mar 2023

Cited by 18 | Viewed by 8061

Abstract

In this study, a basic insect detection system consisting of a manual-focus camera, a Jetson Nano—a low-cost, low-power single-board computer, and a trained deep learning model was developed. The model was validated through a live visual feed. Detecting, classifying, and monitoring insect pests [...] Read more.

In this study, a basic insect detection system consisting of a manual-focus camera, a Jetson Nano—a low-cost, low-power single-board computer, and a trained deep learning model was developed. The model was validated through a live visual feed. Detecting, classifying, and monitoring insect pests in a grain storage or food facility in real time is vital to making insect control decisions. The camera captures the image of the insect and passes it to a Jetson Nano for processing. The Jetson Nano runs a trained deep-learning model to detect the presence and species of insects. With three different lighting situations: white LED light, yellow LED light, and no lighting condition, the detection results are displayed on a monitor. Validating using F1 scores and comparing the accuracy based on light sources, the system was tested with a variety of stored grain insect pests and was able to detect and classify adult cigarette beetles and warehouse beetles with acceptable accuracy. The results demonstrate that the system is an effective and affordable automated solution to insect detection. Such an automated insect detection system can help reduce pest control costs and save producers time and energy while safeguarding the quality of stored products. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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15 pages, 1696 KiB

Open AccessArticle

Data Synthesis for Alfalfa Biomass Yield Estimation

by Jonathan Vance, Khaled Rasheed, Ali Missaoui and Frederick W. Maier

AI 2023, 4(1), 1-15; https://doi.org/10.3390/ai4010001 - 21 Dec 2022

Cited by 1 | Viewed by 2485

Abstract

Alfalfa is critical to global food security, and its data is abundant in the U.S. nationally, but often scarce locally, limiting the potential performance of machine learning (ML) models in predicting alfalfa biomass yields. Training ML models on local-only data results in very [...] Read more.

Alfalfa is critical to global food security, and its data is abundant in the U.S. nationally, but often scarce locally, limiting the potential performance of machine learning (ML) models in predicting alfalfa biomass yields. Training ML models on local-only data results in very low estimation accuracy when the datasets are very small. Therefore, we explore synthesizing non-local data to estimate biomass yields labeled as high, medium, or low. One option to remedy scarce local data is to train models using non-local data; however, this only works about as well as using local data. Therefore, we propose a novel pipeline that trains models using data synthesized from non-local data to estimate local crop yields. Our pipeline, synthesized non-local training (SNLT pronounced like sunlight), achieves a gain of 42.9% accuracy over the best results from regular non-local and local training on our very small target dataset. This pipeline produced the highest accuracy of 85.7% with a decision tree classifier. From these results, we conclude that SNLT can be a useful tool in helping to estimate crop yields with ML. Furthermore, we propose a software application called Predict Your CropS (PYCS pronounced like Pisces) designed to help farmers and researchers estimate and predict crop yields based on pretrained models. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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11 pages, 337 KiB

Open AccessArticle

Dimensionality Reduction Statistical Models for Soil Attribute Prediction Based on Raw Spectral Data

by Marcelo Chan Fu Wei, Ricardo Canal Filho, Tiago Rodrigues Tavares, José Paulo Molin and Afrânio Márcio Corrêa Vieira

AI 2022, 3(4), 809-819; https://doi.org/10.3390/ai3040049 - 30 Sep 2022

Cited by 4 | Viewed by 2712

Abstract

To obtain a better performance when modeling soil spectral data for attribute prediction, researchers frequently resort to data pretreatment, aiming to reduce noise and highlight the spectral features. Even with the awareness of the existence of dimensionality reduction statistical approaches that can cope [...] Read more.

To obtain a better performance when modeling soil spectral data for attribute prediction, researchers frequently resort to data pretreatment, aiming to reduce noise and highlight the spectral features. Even with the awareness of the existence of dimensionality reduction statistical approaches that can cope with data sparse dimensionality, few studies have explored its applicability in soil sensing. Therefore, this study’s objective was to assess the predictive performance of two dimensionality reduction statistical models that are not widespread in the proximal soil sensing community: principal components regression (PCR) and least absolute shrinkage and selection operator (lasso). Here, these two approaches were compared with multiple linear regressions (MLR). All of the modelling strategies were applied without employing pretreatment techniques for soil attribute determination using X-ray fluorescence spectroscopy (XRF) and visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR) data. In addition, the achieved results were compared against the ones reported in the literature that applied pretreatment techniques. The study was carried out with 102 soil samples from two distinct fields. Predictive models were developed for nine chemical and physical soil attributes, using lasso, PCR and MLR. Both Vis-NIR and XRF raw spectral data presented a great performance for soil attribute prediction when modelled with PCR and the lasso method. In general, similar results were found comparing the root mean squared error (RMSE) and coefficient of determination (R²) from the literature that applied pretreatment techniques and this study. For example, considering base saturation (V%), for Vis-NIR combined with PCR, in this study, RMSE and R² values of 10.60 and 0.79 were found compared with 10.38 and 0.80, respectively, in the literature. In addition, looking at potassium (K), XRF associated with lasso yielded an RMSE value of 0.60 and R² of 0.92, and in the literature, RMSE and R² of 0.53 and 0.95, respectively, were found. The major discrepancy was observed for phosphorus (P) and organic matter (OM) prediction applying PCR in the XRF data, which showed R² of 0.33 (for P) and 0.52 (for OM) without using pretreatment techniques in this study, and R² of 0.01 (for P) and 0.74 (for OM) when using preprocessing techniques in the literature. These results indicate that data pretreatment can be disposable for predicting some soil attributes when using Vis-NIR and XRF raw data modeled with dimensionality reduction statistical models. Despite this, there is no consensus on the best way to calibrate data, as this seems to be attribute and area specific. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

20 pages, 11177 KiB

Open AccessArticle

A Spatial AI-Based Agricultural Robotic Platform for Wheat Detection and Collision Avoidance

by Sujith Gunturu, Arslan Munir, Hayat Ullah, Stephen Welch and Daniel Flippo

AI 2022, 3(3), 719-738; https://doi.org/10.3390/ai3030042 - 30 Aug 2022

Cited by 9 | Viewed by 4776

Abstract

To obtain more consistent measurements through the course of a wheat growing season, we conceived and designed an autonomous robotic platform that performs collision avoidance while navigating in crop rows using spatial artificial intelligence (AI). The main constraint the agronomists have is to [...] Read more.

To obtain more consistent measurements through the course of a wheat growing season, we conceived and designed an autonomous robotic platform that performs collision avoidance while navigating in crop rows using spatial artificial intelligence (AI). The main constraint the agronomists have is to not run over the wheat while driving. Accordingly, we have trained a spatial deep learning model that helps navigate the robot autonomously in the field while avoiding collisions with the wheat. To train this model, we used publicly available databases of prelabeled images of wheat, along with the images of wheat that we have collected in the field. We used the MobileNet single shot detector (SSD) as our deep learning model to detect wheat in the field. To increase the frame rate for real-time robot response to field environments, we trained MobileNet SSD on the wheat images and used a new stereo camera, the Luxonis Depth AI Camera. Together, the newly trained model and camera could achieve a frame rate of 18–23 frames per second (fps)—fast enough for the robot to process its surroundings once every 2–3 inches of driving. Once we knew the robot accurately detects its surroundings, we addressed the autonomous navigation of the robot. The new stereo camera allows the robot to determine its distance from the trained objects. In this work, we also developed a navigation and collision avoidance algorithm that utilizes this distance information to help the robot see its surroundings and maneuver in the field, thereby precisely avoiding collisions with the wheat crop. Extensive experiments were conducted to evaluate the performance of our proposed method. We also compared the quantitative results obtained by our proposed MobileNet SSD model with those of other state-of-the-art object detection models, such as the YOLO V5 and Faster region-based convolutional neural network (R-CNN) models. The detailed comparative analysis reveals the effectiveness of our method in terms of both model precision and inference speed. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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Review

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17 pages, 2210 KiB

Open AccessReview

A Systematic Literature Review on Parameters Optimization for Smart Hydroponic Systems

by Umar Shareef, Ateeq Ur Rehman and Rafiq Ahmad

AI 2024, 5(3), 1517-1533; https://doi.org/10.3390/ai5030073 - 27 Aug 2024

Cited by 1 | Viewed by 4442

Abstract

Hydroponics is a soilless farming technique that has emerged as a sustainable alternative. However, new technologies such as Industry 4.0, the internet of things (IoT), and artificial intelligence are needed to keep up with issues related to economics, automation, and social challenges in [...] Read more.

Hydroponics is a soilless farming technique that has emerged as a sustainable alternative. However, new technologies such as Industry 4.0, the internet of things (IoT), and artificial intelligence are needed to keep up with issues related to economics, automation, and social challenges in hydroponics farming. One significant issue is optimizing growth parameters to identify the best conditions for growing fruits and vegetables. These parameters include pH, total dissolved solids (TDS), electrical conductivity (EC), light intensity, daily light integral (DLI), and nutrient solution/ambient temperature and humidity. To address these challenges, a systematic literature review was conducted aiming to answer research questions regarding the optimal growth parameters for leafy green vegetables and herbs and spices grown in hydroponic systems. The review selected a total of 131 papers related to indoor farming, hydroponics, and aquaponics. The review selected a total of 123 papers related to indoor farming, hydroponics, and aquaponics. The majority of the articles focused on technology description (38.5%), artificial illumination (26.2%), and nutrient solution composition/parameters (13.8%). Additionally, remaining 10.7% articles focused on the application of sensors, slope, environment and economy. This comprehensive review provides valuable information on optimized growth parameters for smart hydroponic systems and explores future prospects and the application of digital technologies in this field. Full article

(This article belongs to the Special Issue Artificial Intelligence in Agriculture)

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