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Agricultural productivity is one of the critical factors towards ensuring food security across the globe. However, some of the main crops, such as potato, tomato, and mango, are usually infested by leaf diseases, which considerably lower yield and quality. The traditional practice of diagnosing disease through visual inspection is labor-intensive, time-consuming, and can lead to numerous errors. To address these challenges, this study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification. The Excess Green Index (ExG) is a novel approach that is a specified vegetation index that can further the ability of the model to distinguish and detect vegetative properties even in scenarios with minimal labeled data, demonstrating the tremendous potential for this application. AgirLeafNet demonstrates outstanding accuracy, with 100% accuracy for potato detection, 92% for tomato, and 99.8% for mango leaves, producing incredibly accurate results compared to the models already in use, as described in the literature. By demonstrating the viability of a deep learning/IoT system architecture, this study goes beyond the current state of multi-crop disease detection. It provides practical, effective, and efficient deep-learning solutions for sustainable agricultural production systems. The innovation of the model emphasizes its multi-crop capability, precision in results, and the suggested use of ExG to generate additional robust disease detection methods for new findings. The AgirLeafNet model is setting an entirely new standard for future research endeavors.

With the continuous advancement of information technology in the agricultural field, a large amount of unstructured agricultural textual information has been generated. This information is crucial for supporting the development of smart agriculture, making the application of named entity recognition in the agricultural field more urgent. In order to enhance the accuracy of agricultural entity recognition, this study utilizes the pre-trained BERT-wwm model for word embedding into the text. Additionally, a channel attention mechanism (CA) is introduced in the BILSTM-CRF downstream feature extraction network to comprehensively capture the contextual features of the text. Experimental results demonstrate that the proposed method significantly improves the performance of named entity recognition, with increased accuracy, recall, and F1 value. The successful implementation of this method provides reliable support for downstream tasks such as agricultural knowledge graph construction and question and answer systems and establishes a foundation for better understanding and utilization of agricultural textual information.

Rural buildings represent the functional relationship between rural communities and agricultural land. Therefore, research on rural buildings has practical repercussions on environmental and socio-economic sustainability. Comprehensive state-of-the-art research on rural buildings may address research activities. We present a systematic review of the scientific research between 2000 and 2022 based on the PRISMA protocol. Five main topics were identified. The results showed that the primary research focus was production (25.1%) and environmental management issues (23.2%). However, construction and efficiency are rapidly taking centre stage (20.6%). Regarding sustainability (20.8%), life cycle assessment, green buildings, recycling and global warming should be the future research focus. Energy efficiency will benefit from studies on thermal energy. More research on engineering and technologies (10.3%), specifically remote and automatic detection and transport in rural areas, will increase cost efficiency. The results may help improve the global efficiency of rural buildings in a modern farming system.

Agricultural productivity is one of the critical factors towards ensuring food security across the globe. However, some of the main crops, such as potato, tomato, and mango, are usually infested by leaf diseases, which considerably lower yield and quality. The traditional practice of diagnosing disease through visual inspection is labor-intensive, time-consuming, and can lead to numerous errors. To address these challenges, this study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification. The Excess Green Index (ExG) is a novel approach that is a specified vegetation index that can further the ability of the model to distinguish and detect vegetative properties even in scenarios with minimal labeled data, demonstrating the tremendous potential for this application. AgirLeafNet demonstrates outstanding accuracy, with 100% accuracy for potato detection, 92% for tomato, and 99.8% for mango leaves, producing incredibly accurate results compared to the models already in use, as described in the literature. By demonstrating the viability of a deep learning/IoT system architecture, this study goes beyond the current state of multi-crop disease detection. It provides practical, effective, and efficient deep-learning solutions for sustainable agricultural production systems. The innovation of the model emphasizes its multi-crop capability, precision in results, and the suggested use of ExG to generate additional robust disease detection methods for new findings. The AgirLeafNet model is setting an entirely new standard for future research endeavors. [ABSTRACT FROM AUTHOR]

Rice production has faced numerous challenges in recent years, and traditional methods are still being used to detect rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The public dataset consists of 2000 images; about 1200 images belong to the leaf blast class, and 800 to the healthy leaf class. The modified connection-skipping ResNet 50 had the highest accuracy of 99.75% with a loss rate of 0.33, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. Furthermore, ResNet 50 achieved a validation accuracy of 99.69%, precision of 99.50%, F1-score of 99.70, and AUC of 99.83%. In conclusion, the study demonstrated a superior performance and disease prediction using the Gradio web application.

Several researchers stated that climate change effects are arising quickly in the Mediterranean region. Temperature increasing and droughty summers are two of the most common patterns threatening sparkling wines’ grape quality. The present study investigated nitrogen nutrition to enhance acidity and preserve the aromatic compound on Vitis vinifera var. Glera for producing white sparkling wine. Half of the one-hectare vineyard placed in northeast Italy was fertigated with nitrogen during summer, while the control half received only mineral fertilization in spring as usual in the area. The trial lasted three years. The grapes’ quality was monitored and compared at harvest. The statistical analysis proved an affordable trend among treatments in which the fertigated grapes showed, on average, more free amino acids (+32%), more yeast assimilable nitrogen (+71%), more acidity (+21%), and lower total soluble solids concentration (−3%) than the control grapes. Energy storage, fruit yield, and wood mass were measured too. The study proved the nitrogen supply did not affect either fruit yield or plant vigor. Therefore, nitrogen fertigation has been confirmed to be a reasonable growing practice to preserve wine’s aroma and acidity against climate change.

The projected increase in temperature and water scarcity represents a challenge for winegrowers due to changing climatic conditions. Although heat and drought often occur concurrently in nature, there is still little known about the effects of water stress (WS) on grapevines in hot environments. This study aimed to assess whether the grapevine’s physiological and spectral responses to WS in hot environments differ from those expected under lower temperatures. Therefore, we propose an integrated approach to assess the physiological, thermal, and spectral response of two grapevine varieties (Vitis vinifera L.), Grenache and Shiraz, to WS in a hot environment. In a controlled environment room (CER), we imposed high-temperature conditions (TMIN 30 °C–TMAX 40 °C) and compared the performance of well-watered (WW) and WS-ed potted own-rooted Shiraz and Grenache grapevines (SH_WW, SH_WS, GR_WW, and GR_WS, respectively). We monitored the vines’ physiological, spectral, and thermal trends from the stress imposition to the recovery after re-watering. Then, we performed a correlation analysis between the physiological parameters and the spectral and thermal vegetation indices (VIs). Finally, we looked for the best-fitting models to predict the physiological parameters based on the spectral VIs. The results showed that GR_WS was more negatively impacted than SH_WS in terms of net photosynthesis (Pn, GR-WS = 1.14 μmol·CO2 m−2·s−1; SH-WS = 3.64 μmol·CO2 m−2·s−1), leaf transpiration rate (E, GR-WS = 1.02 mmol·H2O m−2·s−1; SH-WS = 1.75 mmol·H2O m−2·s−1), and stomatal conductance (gs, GR-WS = 0.04 mol·H2O m−2·s−1; SH-WS = 0.11 mol·H2O m−2·s−1). The intrinsic water-use efficiency (WUEi = Pn/gs) of GR_WS (26.04 μmol·CO2 mol−1 H2O) was lower than SH_WS (34.23 μmol·CO2 mol−1 H2O) and comparable to that of SH_WW (26.31 μmol·CO2 mol−1 H2O). SH_WS was not unaffected by water stress except for E. After stress, Pn, gs, and E of GR_WS did not recover, as they were significantly lower than the other treatments. The correlation analysis showed that the anthocyanin Gitelson (AntGitelson) and the green normalised difference vegetation index (GNDVI) had significant negative correlations with stem water potential (Ψstem), Pn, gs, and E and positive correlation with WUEi. In contrast, the photochemical reflectance index (PRI), the water index (WI), and the normalised difference infrared index (NDII) showed an opposite trend. Finally, the crop water stress (CWSI) had significant negative correlations with the Ψstem in both varieties. Our findings help unravel the behaviour of vines under WS in hot environments and suggest instrumental approaches to help the winegrowers managing abiotic stress.

Phytosanitary treatment is one of the most critical operations in vineyard management. Ideally, the spraying system should treat only the canopy, avoiding drift, leakage and wasting of product where leaves are not present: variable rate distribution can be a successful approach, allowing the minimization of losses and improving economic as well as environmental performances. The target of this paper is to realize a smart control system to spray phytosanitary treatment just on the leaves, optimizing the overall costs/benefits ratio. Four different optical-based systems for leaf recognition are analyzed, and their performances are compared using a synthetic vineyard model. In the paper, we consider the usage of three well-established methods (infrared barriers, LIDAR 2-D and stereoscopic cameras), and we compare them with an innovative low-cost real-time solution based on a suitable computer vision algorithm that uses a simple monocular camera as input. The proposed algorithm, analyzing the sequence of input frames and exploiting the parallax property, estimates the depth map and eventually reconstructs the profile of the vineyard’s row to be treated. Finally, the performances obtained by the new method are evaluated and compared with those of the other methods on a well-controlled artificial environment resembling an actual vineyard setup while traveling at standard tractor forward speed.

Over the last few years, several Convolutional Neural Networks for object detection have been proposed, characterised by different accuracy and speed. In viticulture, yield estimation and prediction is used for efficient crop management, taking advantage of precision viticulture techniques. Convolutional Neural Networks for object detection represent an alternative methodology for grape yield estimation, which usually relies on manual harvesting of sample plants. In this paper, six versions of the You Only Look Once (YOLO) object detection algorithm (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv5x, and YOLOv5s) were evaluated for real-time bunch detection and counting in grapes. White grape varieties were chosen for this study, as the identification of white berries on a leaf background is trickier than red berries. YOLO models were trained using a heterogeneous dataset populated by images retrieved from open datasets and acquired on the field in several illumination conditions, background, and growth stages. Results have shown that YOLOv5x and YOLOv4 achieved an F1-score of 0.76 and 0.77, respectively, with a detection speed of 31 and 32 FPS. Differently, YOLO5s and YOLOv4-tiny achieved an F1-score of 0.76 and 0.69, respectively, with a detection speed of 61 and 196 FPS. The final YOLOv5x model for bunch number, obtained considering bunch occlusion, was able to estimate the number of bunches per plant with an average error of 13.3% per vine. The best combination of accuracy and speed was achieved by YOLOv4-tiny, which should be considered for real-time grape yield estimation, while YOLOv3 was affected by a False Positive–False Negative compensation, which decreased the RMSE.

Defining the most profitable remote sensing platforms is a difficult decision-making process, as it requires agronomic and economic considerations. In this paper, the price and profitability of three levels of remote sensing platforms were evaluated to define a decision-making process. Prices of satellite, plane and UAV-acquired vegetation indices were collected in Italy during 2020 and compared to the economic benefits resulting from variable rate nitrogen application, according to a bibliographic meta-analysis performed on grains. The quality comparison of these three technologies was performed considering the error propagation in the NDVI formula. The errors of the single bands were used to assess the optical properties of the sensors. Results showed that medium-resolution satellite data with good optical properties could be profitably used for variable rate nitrogen applications starting from 2.5 hectares, in case of medium resolution with good optical properties. High-resolution satellites with lower optical quality were profitable starting from 13.2 hectares, while very high-resolution satellites with good optical properties could be profitably used starting from 76.8 hectares. Plane-acquired images, which have good optical properties, were profitable starting from 66.4 hectares. Additionally, a reference model for satellite image price is proposed.

Heat stress (HS) and water stress (WS) pose severe threats to viticulture, and effective management solutions to counter their effects on grapevine performance must be examined. In this study, we evaluated the physiological and spectral responses of Vitis vinifera L. cv. Sauvignon blanc to individual (HS) and combined (HS + WS) stress under four different cooling and irrigation strategies. The treatments were: standard drip irrigation (SI), extra drip irrigation (SI+), extra sprinklers irrigation (SPRI), and sustained deficit irrigation (SDI; 50% of SI). Compared to the other treatments, in the early stages after the occurrence of HS, the vine water status of SPRI and SI+ improved, with high stomatal conductance (gs) (SPRI) and stem water potential (Ψstem; SPRI and SI+). All the physiological indicators measured were significantly lower after the end of HS in the SDI treatment. We also identified the spectral response of grapevine to HS and combined HS and WS (resulting from SDI). Consistent with the physiological analysis, the proximal spectral responses of leaves identified SPRI and SI+ as putative cooling strategies to minimize vine HS. The vines undergoing combined stress (SDI) showed greenness amelioration 10 days after stress, as revealed by the greenness vegetation indices (VIs), i.e., Green Index (GI), Normalized Difference Greenness Vegetation Index (NDGI), and Visible Atmospherically Resistant Index (VARI). However, their physiological recovery was not achieved within this time, as shown by the Simple Ratio Index (SRI), Transformed Chlorophyll Absorption Ratio Index (TCARI), and TCARI/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI). A three-step band selection process allowed the identification of the spectral traits’ responsive to HS and combined stress, i.e., 1336–1340 nm, 1967–1971 nm, and 600–604 nm.