AgriEngineering

In precision agriculture (PA), monitoring individual plant health is crucial for optimizing yields and minimizing resources. The normalized difference vegetation index (NDVI), a widely used health indicator, typically relies on expensive multispectral cameras. This study introduces a method for predicting the NDVI of blueberry plants using RGB images and deep learning, offering a cost-effective alternative. To identify individual plant bushes, K-means and Gaussian Mixture Model (GMM) clustering were applied. RGB images were transformed into the HSL (hue, saturation, lightness) color space, and the hue channel was constrained using percentiles to exclude extreme values while preserving relevant plant hues. Further refinement was achieved through adaptive pixel-to-pixel distance filtering combined with the Davies–Bouldin Index (DBI) to eliminate pixels deviating from the compact cluster structure. This enhanced clustering accuracy and enabled precise NDVI calculations. A convolutional neural network (CNN) was trained and tested to predict NDVI-based health indices. The model achieved strong performance with mean squared losses of 0.0074, 0.0044, and 0.0021 for training, validation, and test datasets, respectively. The test dataset also yielded a mean absolute error of 0.0369 and a mean percentage error of 4.5851. These results demonstrate the NDVI prediction method’s potential for cost-effective, real-time plant health assessment, particularly in agrobotics.

A. G. M. Zaman

Kallol Roy

Jüri Olt

Normalized Difference Vegetation Index Prediction for Blueberry Plant Health from RGB Images: A Clustering and Deep Learning Approach

基于RGB图像的蓝莓植物健康归一化植被指数预测：聚类与深度学习方法

The Eurasian grapevine (Vitis vinifera L.) is one of the most extensively cultivated horticultural crop worldwide, with significant economic relevance, particularly in wine production. Accurate grapevine variety identification is essential for ensuring product authenticity, quality control, and regulatory compliance. Traditional identification methods have inherent limitations limitations; ampelography is subjective and dependent on skilled experts, while molecular analysis is costly and time-consuming. To address these challenges, recent research has focused on applying deep learning (DL) and machine learning (ML) techniques for grapevine variety identification. This study systematically analyses 37 recent studies that employed DL and ML models for this purpose. The objective is to provide a detailed analysis of classification pipelines, highlighting the strengths and limitations of each approach. Most studies use DL models trained on leaf images captured in controlled environments at distances of up to 1.2 m. However, these studies often fail to address practical challenges, such as the inclusion of a broader range of grapevine varieties, using data directly acquired in the vineyards, and the evaluation of models under adverse conditions. This review also suggests potential directions for advancing research in this field.

Gabriel A. Carneiro

António Cunha

Thierry J. Aubry

Joaquim Sousa

Advancing Grapevine Variety Identification: A Systematic Review of Deep Learning and Machine Learning Approaches

推进葡萄品种识别：深度学习与机器学习方法的系统综述

Excimer fluorescent ultraviolet (UV) lamps (UV–228) function as mercury–free sources that use excimer emissions as excitation light sources. First, we evaluated the effects of UV–228–based irradiation (λmax = 228 nm) on the viability of fungi, using Penicillium digitatum, and compared these effects with those of other light sources, such as OEL–222 (λmax = 222 nm) and GL–6 (λmax = 254 nm), to evaluate the effectiveness of the excimer fluorescent lamp. Next, we investigated the effects of UV–228–based irradiation on strawberry storage. Although UV–228 affected weight loss, Brix, ascorbic acid, polyphenol, and DPPH, the effects of UV irradiation for 5 min on strawberry fruit quality were minimal; i.e., only weight loss and ascorbic acid content were higher than those in the non–irradiated treatment. We found that 5 min of UV–C irradiation using UV–228 prevented mold emergence for up to 11 days of storage in strawberries These results indicate that UV–228 contributes to long–term strawberry storage.

Yujiro Takano

Ryuta Ninohei

Ayano Koike

Izumi Serizawa

Yuya Mochizuki

Effects of Excimer Fluorescent UV Lamps on Mold and Fruit Quality in Strawberries

准分子荧光紫外灯对草莓霉菌及果实品质的影响

This study investigates Miscanthus biochar’s potential to reduce ammonia (NH3) emissions in poultry production. Biochar from lignocellulosic biomass has proven a versatile tool in environmental remediation for water, soil, and air quality applications with ample opportunity for inclusion in agricultural systems. Ammonia emissions present a concern for animal/human health and the environment. The impacts of biochar production temperature (400 and 700 °C), organic acid activation (acetic acid, citric acid), and application rate (0.24 and 0.49 kg m−2) on broiler litter NH3 emissions were evaluated. Biochar production parameters, i.e., temperature, and acid type were found to significantly impact its performance as an NH3 control measure. The following factors, ranked by magnitude of impact, were found to statistically impact the NH3 emission rate: biochar application rate (p &lt; 0.001), biochar production temperature (p = 0.003), and lastly acid type (p = 0.007). The best performing biochar was produced at 400 °C, activated with acetic acid, and applied at a high addition rate (0.49 kg m−2). This treatment reduced cumulative NH3 volatilization after 2 weeks by 19.7%.

Carly Graves

Mahmoud Sharara

Sanjay Shah

Praveen Kolar

Jesse Grimes

Engineered Miscanthus Biochar Performance as a Broiler Litter Amendment

工程化芒草生物炭作为肉鸡垫料改良剂的性能研究

Deep learning-based methods have proven to be effective for various purposes in the agricultural sector. However, these methods require large amounts of labelled data, which are difficult to prepare and preprocess. To overcome this problem, we propose the use of a latent diffusion model for plant disease detection by generating unseen class images. In this study, we used images of healthy and diseased grape leaves as training datasets and utilized the latent diffusion model, known for its superior performance in image generation, to generate images of diseased apple leaves that were not included in this dataset. Image-to-image generation was utilized to preserve the original healthy leaf features, which enabled the appropriate image generation of diseased apple leaves. To ascertain whether the generated diseased apple leaf images could be used to detect leaf diseases, a deep learning-based classification model was trained to discriminate between diseased and healthy apple leaves from a dataset with a mixture of actual and generated images. Results showed that leaves were accurately classified, indicating that diseased apple leaves not included in the training data could be used to identify the actual diseased apple leaves. Our approach opens up new avenues for improving plant disease detection methods.

Noriyuki Mori

Hiroki Naito

Fumiki Hosoi

Application of a Latent Diffusion Model to Plant Disease Detection by Generating Unseen Class Images

应用潜在扩散模型生成未见类别图像进行植物病害检测

Seed analysis is a cornerstone in advancing agriculture, with vigor tests playing a critical role in evaluating the physiological quality of seeds. However, monitoring seed vigor over time poses a significant challenge for the seed industry, as traditional methods are time-consuming and heavily reliant on subjective human judgment. Concerning these limitations, optical-based techniques have emerged as promising alternatives. Among them, the biological laser speckle phenomenon, rooted in optical interferometry, has proven effective in sensitively detecting and monitoring activity levels in living tissues. Known as the biospeckle laser (BSL) technique, this approach offers reliable results in assessing seed vigor. The BSL technique stands out due to its simplicity, rapid analysis, objectivity, and potential for automation, making it a valuable tool for seed analysis. This paper explores the state-of-the-art application of the BSL technique for evaluating seed vigor, highlighting successful approaches, identifying current challenges, and outlining areas for future research. It delves into the experimental setup for seed illumination and discusses the associated image processing methods. Furthermore, the paper examines the numerical and graphical outcomes, underscoring the BSL technique’s ability to carry out seed analysis by addressing the limitations of traditional methods and enhancing efficiency in the agricultural sector.

Roberto A. Braga

José Luís Contado

Karina Renostro Ducatti

Edvaldo A. Amaral da Silva

Analysis of Seed Vigor Using the Biospeckle Laser Technique

利用生物散斑激光技术分析种子活力

Cannabis sativa, often called hemp, is a medicinal plant belonging to the Cannabaceae family and is widely recognized for its therapeutic applications. After the industrial supercritical CO2 extraction method, hemp residue biomass was recovered, and a significant quantity of bioactive compounds was identified. Therefore, it is of paramount importance to study whether they can be further exploited using green techniques. In the present work, hemp leaf residues were treated using two extraction techniques: one conventional stirring extraction (STE) and one green pressurized liquid extraction (PLE). The latter technique is a promising and swift method for the efficient extraction of valuable molecules from natural sources. The two techniques were optimized through Response Surface Methodology, and the optimized parameters were the appropriate solvent, temperature, and extraction duration. The aim was to maximize the yield of bioactive compounds (polyphenols, flavonoids, and ascorbic acid) from hemp leaf residue and evaluate their antioxidant activity using the most appropriate technique. The results showed that after three 5 min PLE cycles, the recovered individual polyphenols were comparable (p &gt; 0.05) to a 45 min STE (19.34 and 20.84 mg/g, respectively), as well as in antioxidant capacity assays and other bioactive compounds. These findings emphasize the efficacy of the rapid PLE approach as an effective extraction technique to enhance the value of hemp leaf residues while maximizing the concentration of high-added value molecules.

Vassilis Athanasiadis

Martha Mantiniotou

Dimitrios Kalompatsios

Ioannis Makrygiannis

Aggeliki Alibade

Stavros I. Lalas

Evaluation of Antioxidant Properties of Residual Hemp Leaves Following Optimized Pressurized Liquid Extraction

优化加压液体萃取后剩余大麻叶抗氧化性能的评估

In plasticulture production systems, the conventional practice involves broadcasting pre-emergent herbicides over the entire surface of raised beds before laying plastic mulch. However, weed emergence predominantly occurs through the transplant punch-holes in the mulch, leaving most of the applied herbicide beneath the plastic, where weeds cannot grow. To address this issue, we developed and evaluated a precision spraying system designed to target herbicide application to the transplant punch-holes. A dataset of 3378 images was manually collected and annotated during a tomato experimental trial at the University of Florida. A YOLOv8x model with a p2 output layer was trained, converted to TensorRT® to improve the inference time, and deployed on a custom-built computer. A Python-based graphical user interface (GUI) was developed to facilitate user interaction and the control of the smart sprayer system. The sprayer utilized a global shutter camera to capture real-time video input for the YOLOv8x model, which activates or disactivates a TeeJet solenoid for precise herbicide application upon detecting a punch-hole. The model demonstrated excellent performance, achieving precision, recall, mean average precision (mAP), and F1score exceeding 0.90. Field tests showed that the smart sprayer reduced herbicide use by up to 69% compared to conventional broadcast methods. The system achieved an 86% punch-hole recognition rate, with a 14% miss rate due to challenges such as plant occlusion and variable lighting conditions, indicating that the dataset needs to be improved. Despite these limitations, the smart sprayer effectively minimized off-target herbicide application without causing crop damage. This precision approach reduces chemical inputs and minimizes the potential environmental impact, representing a significant advancement in sustainable plasticulture weed management.

Renato Herrig Furlanetto

Ana Claudia Buzanini

Arnold Walter Schumann

Nathan Shawn Boyd

Developing AI Smart Sprayer for Punch-Hole Herbicide Application in Plasticulture Production System

开发用于塑料栽培生产系统中孔洞除草剂喷洒的AI智能喷雾器

Coffee is a global commodity, with Brazil being a major producer, particularly in the Minas Gerais state. This study applied machine learning to predict the Arabica coffee yield in the region, analyzing two groups of cultivars (G1 and G2) using data from 1993 to 2020. The Factor Analysis of Mixed Data (FAMD) was employed to explore the relationships between climatic factors, management practices, and the coffee yield. Four machine learning models, such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost (XGB), and Support Vector Machines (SVM) were calibrated and evaluated for yield prediction. The FAMD revealed complex interactions among variables, requiring four principal components to explain approximately 64.6% of the total variance. Management practices, such as the planting density and pruning, had a stronger influence on G1 cultivars, while G2 cultivars were more sensitive to climatic conditions, particularly the air temperature. Among the machine learning models, RF and XGB performed best in the yield estimation, whereas MLR and SVM were less effective, particularly for values above 60 bags ha−1 (1 bag = 60 kg). These findings underscore the variability in the yield across cultivars and demonstrate the potential of machine learning to guide tailored management strategies for different coffee cultivars.

Abbreviation
Journal Impact	2.98
Quartiles(Global)	AGRICULTURAL ENGINEERING(Q2)

ISSN	2624-7402
h-index	18

Publication Information	Publisher: Multidisciplinary Digital Publishing Institute (MDPI)，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2019，Proportion of original research papers: 91.89%，Self Citation Rate:10.00%， Gold OA Rate: 100.00%
Journal Fees	$Details
Average review cycle	网友分享经验：11 Weeks

AgriEngineering

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Normalized Difference Vegetation Index Prediction for Blueberry Plant Health from RGB Images: A Clustering and Deep Learning Approach

Advancing Grapevine Variety Identification: A Systematic Review of Deep Learning and Machine Learning Approaches

Effects of Excimer Fluorescent UV Lamps on Mold and Fruit Quality in Strawberries

Engineered Miscanthus Biochar Performance as a Broiler Litter Amendment

Application of a Latent Diffusion Model to Plant Disease Detection by Generating Unseen Class Images

Analysis of Seed Vigor Using the Biospeckle Laser Technique

Evaluation of Antioxidant Properties of Residual Hemp Leaves Following Optimized Pressurized Liquid Extraction

Developing AI Smart Sprayer for Punch-Hole Herbicide Application in Plasticulture Production System

Smart Coffee: Machine Learning Techniques for Estimating Arabica Coffee Yield

Engineered Miscanthus Biochar Performance as a Broiler Litter Amendment