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As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this "land gap" and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle. [ABSTRACT FROM AUTHOR]

Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology's prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture. [ABSTRACT FROM AUTHOR]

Continuing progress in machine learning (ML) has led to significant advancements in agricultural tasks. Due to its strong ability to extract high-dimensional features from fruit images, deep learning (DL) is widely used in fruit detection and automatic harvesting. Convolutional neural networks (CNN) in particular have demonstrated the ability to attain accuracy and speed levels comparable to those of humans in some fruit detection and automatic harvesting fields. This paper presents a comprehensive overview and review of fruit detection and recognition based on DL for automatic harvesting from 2018 up to now. We focus on the current challenges affecting fruit detection performance for automatic harvesting: the scarcity of high-quality fruit datasets, fruit detection of small targets, fruit detection in occluded and dense scenarios, fruit detection of multiple scales and multiple species, and lightweight fruit detection models. In response to these challenges, we propose feasible solutions and prospective future development trends. Future research should prioritize addressing these current challenges and improving the accuracy, speed, robustness, and generalization of fruit vision detection systems, while reducing the overall complexity and cost. This paper hopes to provide a reference for follow-up research in the field of fruit detection and recognition based on DL for automatic harvesting. [ABSTRACT FROM AUTHOR]

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network's attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network's ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops. [ABSTRACT FROM AUTHOR]

Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model. [ABSTRACT FROM AUTHOR]

Strawberry (Fragaria × ananassa Duch.) has been widely accepted as the "Queen of Fruits". It has been identified as having high levels of vitamin C and antioxidants that are beneficial for maintaining cardiovascular health and maintaining blood sugar levels. The implementation of advanced techniques like precision agriculture (PA) is crucial for enhancing production compared to conventional farming methods. In recent years, the successful application of deep learning models was represented by convolutional neural networks (CNNs) in a variety of disciplines of computer vision (CV). Due to the dearth of a comprehensive and detailed discussion on the application of deep learning to strawberry cultivation, a particular review of recent technologies is needed. This paper provides an overview of recent advancements in strawberry cultivation utilizing Deep Learning (DL) techniques. It provides a comprehensive understanding of the most up-to-date techniques and methodologies used in this field by examining recent research. It also discusses the recent advanced variants of the DL model, along with a fundamental overview of CNN architecture. In addition, techniques for fine-tuning DL models have been covered. Besides, various strawberry-planting-related datasets were examined in the literature, and the limitations of using research models for real-time research have been discussed. [ABSTRACT FROM AUTHOR]

Accurately estimating soil nutrient content, including soil organic matter (OM), nitrogen (N), phosphorus (P), and potassium (K) levels, is crucial for optimizing agricultural practices and ensuring sustainable crop production. This paper proposes a model based on a fusion attention mechanism that combines bidirectional gated recurrent units (BiGRU) and recurrent neural networks (RNN) to estimate soil nutrient content. The proposed model integrates the fused attention mechanism with BiGRU and RNN to enhance the accuracy and effectiveness of soil nutrient prediction. The fused attention mechanism captures key features in the input data, while the BiGRU architecture captures both forward and backward contextual information, enabling the model to capture long-term dependencies in the data. The results demonstrate that the proposed Att-BiGRU-RNN model outperforms other constructed models, exhibiting a higher prediction accuracy and robustness. The model shows good estimation capabilities for soil OM, N, P, and K with estimation accuracies (R2) of 0.959, 0.907, 0.921, and 0.914, respectively. The application of this model in soil nutrient estimation has the potential to optimize fertilizer management, enhance soil fertility, and ultimately improve crop yield. Further research can explore the applicability of this model in precision agriculture and sustainable soil management practices, benefiting the agricultural sector and contributing to food security and environmental sustainability. [ABSTRACT FROM AUTHOR]

Paddy leaf diseases encompass a range of ailments affecting rice plants' leaves, arising from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages technologies for enhanced crop production, with disease detection being a vital element. Prompt identification of diseases in paddy leaves is critical for curtailing their propagation and reducing crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited for widespread application. These ML/DL methods cannot ensure data privacy, as they involve sharing training data with a central server, overlooking competitive and regulatory considerations. As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified limitations of centralized training. This paper utilizes the FL approach for the classification of rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized client-server setup. Both IID (independent and identically distributed) and non-IID datasets were utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them, MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently, we trained these models using both IID and non-IID datasets in a federated learning environment. The framework's performance was assessed through diverse scenarios, comparing it with traditional and federated learning models. The evaluation considered metrics like validation accuracy, loss as well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving 99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation, EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved 90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the F-TL framework's superiority over traditional distributed deep-learning classifiers, demonstrating its effectiveness in both single and multiclient instances. Notably, the framework's strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as a valuable alternative for rice-leaf disease classification compared to existing tools. [ABSTRACT FROM AUTHOR]

Accurate pear sorting plays a crucial role in ensuring the quality of pears and increasing the sales of them. In the domain of intelligent pear sorting, precise target detection of pears is imperative. However, practical implementation faces challenges in achieving adequate accuracy in pear target detection due to the limitations of computational resources in embedded devices and the occurrence of occlusion among pears. To solve this problem, we built an image acquisition system based on pear sorting equipment and created a pear dataset containing 34,598 pear images under laboratory conditions. The dataset was meticulously annotated using the LabelImg software, resulting in a total of 154,688 precise annotations for pears, pear stems, pear calyxes, and pear defects. Furthermore, we propose an Extremely Compressed Lightweight Model for Pear Object Detection (ECLPOD) based on YOLOv7's pipeline to assist in the pear sorting task. Firstly, the Hierarchical Interactive Shrinking Network (HISNet) was proposed, which contributed to efficient feature extraction with a limited amount of computation and parameters. The Bulk Feature Pyramid (BFP) module was then proposed to enhance pear contour information extraction during feature fusion. Finally, the Accuracy Compensation Strategy (ACS) was proposed to improve the detection capability of the model, especially for identification of the calyces and stalks of pears. The experimental results indicate that the ECLPOD achieves 90.1% precision (P) and 85.52% mAP50 with only 0.58 million parameters and 1.3 GFLOPs of computation in the homemade pear dataset in this paper. Compared with YOLOv7, the number of parameters and the amount of computation for the ECLPOD are compressed to 1.5% and 1.3%, respectively. Compared with other mainstream methods, the ECLPOD achieves an optimal trade-off between accuracy and complexity. This suggests that the ECLPOD is superior to these existing approaches in the field of object detection for assisting pear sorting tasks with good potential for embedded device deployment. [ABSTRACT FROM AUTHOR]

Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing. [ABSTRACT FROM AUTHOR]

The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%. [ABSTRACT FROM AUTHOR]

Rice, the world's most extensively cultivated cereal crop, serves as a staple food and energy source for over half of the global population. A variety of abiotic and biotic factors such as weather conditions, soil quality, temperature, insects, pathogens, and viruses can greatly impact the quantity and quality of rice grains. Studies have established that plant infections have a significant impact on rice crops, resulting in substantial financial losses in agriculture. To accurately diagnose and manage the diseases affecting rice plants, plant pathologists are seeking efficient and reliable methods. Traditional disease detection techniques, employed by farmers, involve time-consuming visual inspections and result in inadequate farming practices. With advancements in agricultural technology, the identification of pathogenic organisms in rice plants has become significantly more manageable through techniques such as machine learning and deep learning, which are receiving significant attention in crop disease research. In this paper, we used the transfer learning approach on 15 pre-trained CNN models for the automatic identification of Rice leave diseases. Results showed that the InceptionV3 model is outperforming with an average accuracy of 99.64% with Precision, Recall, F1-Score, and Specificity as 98.23, 98.21, 98.20, and 99.80, and the AlexNet model resulted in poor performance with average accuracy of 97.35% among others. [ABSTRACT FROM AUTHOR]

The efficiency of agricultural practices depends on the timing of their execution. Environmental conditions, such as rainfall, and crop-related traits, such as plant phenology, determine the success of practices such as irrigation. Moreover, plant phenology, the seasonal timing of biological events (e.g., cotyledon emergence), is strongly influenced by genetic, environmental, and management conditions. Therefore, assessing the timing the of crops' phenological events and their spatiotemporal variability can improve decision making, allowing the thorough planning and timely execution of agricultural operations. Conventional techniques for crop phenology monitoring, such as field observations, can be prone to error, labour-intensive, and inefficient, particularly for crops with rapid growth and not very defined phenophases, such as vegetable crops. Thus, developing an accurate phenology monitoring system for vegetable crops is an important step towards sustainable practices. This paper evaluates the ability of computer vision (CV) techniques coupled with deep learning (DL) (CV_DL) as tools for the dynamic phenological classification of multiple vegetable crops at the subfield level, i.e., within the plot. Three DL models from the Single Shot Multibox Detector (SSD) architecture (SSD Inception v2, SSD MobileNet v2, and SSD ResNet 50) and one from You Only Look Once (YOLO) architecture (YOLO v4) were benchmarked through a custom dataset containing images of eight vegetable crops between emergence and harvest. The proposed benchmark includes the individual pairing of each model with the images of each crop. On average, YOLO v4 performed better than the SSD models, reaching an F1-Score of 85.5%, a mean average precision of 79.9%, and a balanced accuracy of 87.0%. In addition, YOLO v4 was tested with all available data approaching a real mixed cropping system. Hence, the same model can classify multiple vegetable crops across the growing season, allowing the accurate mapping of phenological dynamics. This study is the first to evaluate the potential of CV_DL for vegetable crops' phenological research, a pivotal step towards automating decision support systems for precision horticulture. [ABSTRACT FROM AUTHOR]

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic. [ABSTRACT FROM AUTHOR]

Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because the sensing data has noise and complex nonlinearity, it is still an open topic to improve its performance. This paper proposes a Reversible Automatic Selection Normalization (RASN) network, integrating the normalization and renormalization layer to evaluate and select the normalization module of the prediction model. The prediction accuracy has been improved effectively by scaling and translating the input with learnable parameters. The application results of the prediction show that the model has good prediction ability and adaptability for the greenhouse in the Smart Agriculture System. [ABSTRACT FROM AUTHOR]

Computer vision is currently experiencing success in various domains due to the harnessing of deep learning strategies. In the case of precision agriculture, computer vision is being investigated for detecting fruits from orchards. However, such strategies limit too-high complexity computation that is impossible to embed in an automated device. Nevertheless, most investigation of fruit detection is limited to a single fruit, resulting in the necessity of a one-to-many object detection system. This paper introduces a generic detection mechanism named FruitDet, designed to be prominent for detecting fruits. The FruitDet architecture is designed on the YOLO pipeline and achieves better performance in detecting fruits than any other detection model. The backbone of the detection model is implemented using DenseNet architecture. Further, the FruitDet is packed with newer concepts: attentive pooling, bottleneck spatial pyramid pooling, and blackout mechanism. The detection mechanism is benchmarked using five datasets, which combines a total of eight different fruit classes. The FruitDet architecture acquires better performance than any other recognized detection methods in fruit detection. [ABSTRACT FROM AUTHOR]

Agricultural advancements have significantly impacted people's lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop's conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China's National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance. [ABSTRACT FROM AUTHOR]