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Diseases of tomato leaves can seriously damage crop yield and financial rewards. The timely and accurate detection of tomato diseases is a major challenge in agriculture. Hence, the early and accurate diagnosis of tomato diseases is crucial. The emergence of deep learning has dramatically helped in plant disease detection. However, the accuracy of deep learning models largely depends on the quantity and quality of training data. To solve the inter-class imbalance problem and improve the generalization ability of the classification model, this paper proposes a cycle-consistent generative-adversarial-network-based Transformer model to generate diseased tomato leaf images for data augmentation. In addition, this paper uses a Transformer model and densely connected CNN architecture to extract multilevel local features. The Transformer module is utilized to capture global dependencies and contextual information accurately to expand the sensory field of the model. Experiments show that the proposed model achieved 99.45% accuracy on the PlantVillage dataset. The 2018 Artificial Intelligence Challenger dataset and the private dataset attained accuracies of 98.30% and 95.4%, and the proposed classification model achieved a higher accuracy and smaller model size compared to previous deep learning models. The classification model is generalizable and robust and can provide a stable theoretical framework for crop disease prevention and control. [ABSTRACT FROM AUTHOR]

The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model's learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model's ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities. [ABSTRACT FROM AUTHOR]

Computer vision provides a real-time, non-destructive, and indirect way of horticultural crop yield estimation. Deep learning helps improve horticultural crop yield estimation accuracy. However, the accuracy of current estimation models based on RGB (red, green, blue) images does not meet the standard of a soft sensor. Through enriching more data and improving the RGB estimation model structure of convolutional neural networks (CNNs), this paper increased the coefficient of determination (R2) by 0.0284 and decreased the normalized root mean squared error (NRMSE) by 0.0575. After introducing a novel loss function mean squared percentage error (MSPE) that emphasizes the mean absolute percentage error (MAPE), the MAPE decreased by 7.58%. This paper develops a lettuce fresh weight estimation method through the multi-modal fusion of RGB and depth (RGB-D) images. With the multimodal fusion based on calibrated RGB and depth images, R2 increased by 0.0221, NRMSE decreased by 0.0427, and MAPE decreased by 3.99%. With the novel loss function, MAPE further decreased by 1.27%. A MAPE of 8.47% helps to develop a soft sensor for lettuce fresh weight estimation. [ABSTRACT FROM AUTHOR]

Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health. [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]

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]

Accurate estimation of wheat leaf nitrogen concentration (LNC) is critical for characterizing ecosystem and plant physiological processes; it can further guide fertilization and other field management operations, and promote the sustainable development of agriculture. In this study, a wheat LNC test method based on multi-source spectral data and a convolutional neural network is proposed. First, interpolation reconstruction was performed on the wheat spectra data collected by different spectral instruments to ensure that the number of spectral channels and spectral range were consistent, and multi-source spectral data were constructed using interpolated, reconstructed imaging spectral data and non-imaging spectral data. Afterwards, the convolutional neural network DshNet and machine learning methods (PLSR, SVR, and RFR) were compared under various scenarios (non-imaging spectral data, imaging spectral data, and multi-source spectral data). Finally, the competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the LNC detection model. The results show that the model based on DshNet has the highest test accuracy. The CARS method is more suitable for DshNet model optimization than SPA. In the modeling scenario with non-imaging spectral, imaging spectral, and multi-source spectral, the optimized R2 is 0.86, 0.82, and 0.82, and the RMSE is 0.29, 0.31, and 0.31, respectively. The LNC visualization results show that DshNet modeling using multi-source spectral data is conducive to the visualization expansion of non-imaging spectral data. Therefore, the method presented in this paper provides new considerations for spectral data from different sources and is helpful for related research on the chemometric task of multi-source spectral data. [ABSTRACT FROM AUTHOR]

With the development and progress of uncrewed farming technology, uncrewed rice transplanters have gradually become an indispensable part of modern agricultural production; however, in the actual production, the working quality of uncrewed rice transplanters have not been effectively detected. In order to solve this problem, a detection method of uncrewed transplanter omission is proposed in this paper. In this study, the RGB images collected in the field were inputted into a convolutional neural network, and the bounding box center of the network output was used as the approximate coordinates of the rice seedlings, and the horizontal and vertical crop rows were fitted by the least square method, so as to detect the phenomenon of rice omission. By adding atrous spatial pyramid pooling and a convolutional block attention module to YOLOv5, the problem of image distortion caused by scaling and cropping is effectively solved, and the recognition accuracy is improved. The accuracy of this method is 95.8%, which is 5.6% higher than that of other methods, and the F1-score is 93.39%, which is 4.66% higher than that of the original YOLOv5. Moreover, the network structure is simple and easy to train, with the average training time being 0.284 h, which can meet the requirements of detection accuracy and speed in actual production. This study provides an effective theoretical basis for the construction of an uncrewed agricultural machinery system. [ABSTRACT FROM AUTHOR]

Early recognition of fruit body diseases in edible fungi can effectively improve the quality and yield of edible fungi. This study proposes a method based on improved ShuffleNetV2 for edible fungi fruit body disease recognition. First, the ShuffleNetV2+SE model is constructed by deeply integrating the SE module with the ShuffleNetV2 network to make the network pay more attention to the target area and improve the model's disease classification performance. Second, the network model is optimized and improved. To simplify the convolution operation, the 1 × 1 convolution layer after the 3 × 3 depth convolution layer is removed, and the ShuffleNetV2-Lite+SE model is established. The experimental results indicate that the accuracy, precision, recall, and Macro-F1 value of the ShuffleNetV2-Lite+SE model on the test set are, respectively, 96.19%, 96.43%, 96.07%, and 96.25%, which are 4.85, 4.89, 3.86, and 5.37 percent higher than those before improvement. Meanwhile, the number of model parameters and the average iteration time are 1.6 MB and 41 s, which is 0.2 MB higher and 4 s lower than that before the improvement, respectively. Compared with the common lightweight convolutional neural networks MobileNetV2, MobileNetV3, DenseNet, and EfficientNet, the proposed model achieves higher recognition accuracy, and its number of model parameters is significantly reduced. In addition, the average iteration time is reduced by 37.88%, 31.67%, 33.87%, and 42.25%, respectively. The ShuffleNetV2-Lite+SE model proposed in this paper has a good balance among performance, number of parameters, and real-time performance. It is suitable for deploying on resource-limited devices such as mobile terminals and helps in realization of real-time and accurate recognition of fruit body diseases of edible fungi. [ABSTRACT FROM AUTHOR]

This paper proposes a method for recognizing the origin of Saposhnikovia divaricata using the IResNet model to achieve computer vision-based classification. Firstly, we created a small sample dataset and applied data augmentation techniques to enhance its diversity. After that, we introduced the hierarchical residual connection block in the early stage of the original model to expand the perceptual field of the neural network and enhance the extraction of scale features. Meanwhile, a depth-separable convolution operation was adopted in the later stage of the model to replace the conventional convolution operation and further reduce the time cost of the model. The experimental results demonstrate that the improved network model achieved a 5.03% improvement in accuracy compared to the original model while also significantly reducing the number of parameters required for the model. In our experiments, we compared the accuracy of the proposed model with several classical convolutional neural network models, including ResNet50, Resnest50, Res2net50, RepVggNet_B0, and ConvNext_T. The results showed that our proposed model achieved an accuracy of 93.72%, which outperformed ResNet50 (86.68%), Resnest50 (89.38%), Res2net50 (91.83%), RepVggNet_B0 (88.68%), and ConvNext_T (92.18%). Furthermore, our proposed model achieved the highest accuracy among the compared models, with a transmission frame rate of 158.9 fps and an inference time of only 6.29 ms. The research methodology employed in this study has demonstrated the ability to reduce potential errors caused by manual observation, effectively improving the recognition ability of Saposhnikovia divaricata based on existing data. Furthermore, the findings of this study provide valuable reference and support for future efforts to develop lightweight models in this area. [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]

Fruit quality is an important aspect in determining the consumer preference in the supply chain. Thermal imaging was used to determine different pineapple varieties according to the physicochemical changes of the fruit by means of the deep learning method. Deep learning has gained attention in fruit classification and recognition in unimodal processing. This paper proposes a multimodal data fusion framework for the determination of pineapple quality using deep learning methods based on the feature extraction acquired from thermal imaging. Feature extraction was selected from the thermal images that provided a correlation with the quality attributes of the fruit in developing the deep learning models. Three different types of deep learning architectures, including ResNet, VGG16, and InceptionV3, were built to develop the multimodal data fusion framework for the classification of pineapple varieties based on the concatenation of multiple features extracted by the robust networks. The multimodal data fusion coupled with powerful convolutional neural network architectures can remarkably distinguish different pineapple varieties. The proposed multimodal data fusion framework provides a reliable determination of fruit quality that can improve the recognition accuracy and the model performance up to 0.9687. The effectiveness of multimodal deep learning data fusion and thermal imaging has huge potential in monitoring the real-time determination of physicochemical changes of fruit. [ABSTRACT FROM AUTHOR]

The fruit quality and yield of sweet peppers can be effectively improved by accurately and efficiently controlling the growth conditions and taking timely corresponding measures to manage the planting process dynamically. The use of deep-learning-based image recognition technology to segment sweet pepper instances accurately is an important means of achieving the above goals. However, the accuracy of the existing instance segmentation algorithms is seriously affected by complex scenes such as changes in ambient light and shade, similarity between the pepper color and background, overlap, and leaf occlusion. Therefore, this paper proposes an instance segmentation algorithm that integrates the Swin Transformer attention mechanism into the backbone network of a Mask region-based convolutional neural network (Mask RCNN) to enhance the feature extraction ability of the algorithm. In addition, UNet3+ is used to improve the mask head and segmentation quality of the mask. The experimental results show that the proposed algorithm can effectively segment different categories of sweet peppers under conditions of extreme light, sweet pepper overlap, and leaf occlusion. The detection AP, AR, segmentation AP, and F1 score were 98.1%, 99.4%, 94.8%, and 98.8%, respectively. The average FPS value was 5, which can be satisfied with the requirement of dynamic monitoring of the growth status of sweet peppers. These findings provide important theoretical support for the intelligent management of greenhouse crops. [ABSTRACT FROM AUTHOR]

In order to select the light quality suitable for plant growth, a quantitative detection model of chlorophyll content in muskmelon leaves was established to monitor plant growth quickly and accurately. In the paper, muskmelon "Boyang 91" was used as the experimental material, and six different light proportion treatments were set up. Through measuring plant height, stem diameter, number of leaves, nodes, and other growth indicators, in addition to leaf chlorophyll content, the response difference of muskmelon to different light qualities was explored in a plant factory. The hyperspectral imaging technology was used to establish the prediction model for the chlorophyll content of muskmelon. The original spectrum was preprocessed and optimized by five pretreatments, and then the characteristic wavelengths were extracted by six methods. Partial least squares regression (PLSR), least squares support vector machine (LSSVM), and convolutional neural network (CNN) were established for optimal feature wavelength. The results showed that the plant height and stem diameter of the T3 treatment were higher than those of other treatments, and their values were 14.48 (cm) and 5.02 (mm), respectively. The chlorophyll content of the T3 treatment was the highest, and its value was 40.16 (mg/g), which was higher than that of other treatments. Through comprehensive analysis, the T3 treatment (light ratio: 6R/1B/2W, light quantum flux: 360 μmol/(m2·s), photoperiod: 12 h) was optimal. Meanwhile, the average spectral reflectance data of 216 leaf samples were extracted, and the S-G preprocessing method was selected to preprocess the original spectral data (Rc = 0.860, RMSEC = 1.806; Rcv = 0.790, RMSECV = 2.161). By comparing and analyzing the correlation coefficients and root mean square errors of six feature wavelength extraction methods, it was concluded that the variable combination population analysis (VCPA) method had the best model effect for feature wavelength extraction (RP = 0.824, RMSEP = 1.973). Ten characteristic wavelengths (396, 409, 457, 518, 532, 565, 687, 691, 701, and 705 nm) extracted by the VCPA method were used to establish the chlorophyll content prediction model, and the chlorophyll content prediction model of S-G-VCPA-CNN had the best performance (Rc = 0.9151, RMSEC = 1.445; Rp = 0.811, RMSEP = 2.055). The results of this study provide data support and a theoretical basis for screening the light ratio of other crops, and also present technical support for online monitoring of crop growth in plant factories. [ABSTRACT FROM AUTHOR]

Efficient identification of apple leaf diseases (ALDs) can reduce the use of pesticides and increase the quality of apple fruit, which is of significance to smart agriculture. However, existing research into identifying ALDs lacks models/methods that satisfy efficient identification in the wild environment, hindering the application of smart agriculture in the apple industry. Therefore, this paper explores an ACCURATE, LIGHTWEIGHT, and ROBUST convolutional neural network (CNN) called EfficientNet-MG, improving the conventional EfficientNet network by the multistage feature fusion (MSFF) method and gaussian error linear unit (GELU) activation function. The shallow and deep convolutional layers usually contain detailed and semantic information, respectively, but conventional EfficientNets do not fully utilize the different stage convolutional layers. Thus, MSFF was adopted to improve the semantic representation capacity of the last layer of features, and GELU was used to adapt to complicated tasks. Further, a comprehensive ALD dataset called AppleLeaf9 was constructed for the wild environment. The experimental results show that EfficientNet-MG achieves a higher accuracy (99.11%) and fewer parameters (8.42 M) than the five classical CNN models, thus proving that EfficientNet-MG achieves more competitive results on ALD identification. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate whether an automatic analysis with deep learning convolutional neural networks techniques offer the ability to efficiently identify olive groves with different intensification patterns by using very high-resolution aerial orthophotographs. First, a sub-image crop classification was carried out. To standardize the size and increase the number of samples of the data training (DT), the crop images were divided into mini-crops (sub-images) using segmentation techniques, which used a different threshold and stride size to consider the mini-crop as suitable for the analysis. The four scenarios evaluated discriminated the sub-images efficiently (accuracies higher than 0.8), obtaining the largest sub-images (H = 120, W = 120) for the highest average accuracy (0.957). The super-intensive olive plantings were the easiest to classify for most of the sub-image sizes. Nevertheless, although traditional olive groves were discriminated accurately, too, the most difficult task was to distinguish between the intensive plantings and the traditional ones. A second phase of the proposed system was to predict the crop at farm-level based on the most frequent class detected in the sub-images of each crop. The results obtained at farm level were slightly lower than at the sub-images level, reaching the highest accuracy (0.826) with an intermediate size image (H = 80, W = 80). Thus, the convolutional neural networks proposed made it possible to automate the classification and discriminate accurately among traditional, intensive, and super-intensive planting systems. [ABSTRACT FROM AUTHOR]

Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented. [ABSTRACT FROM AUTHOR]

The agricultural sector plays a key role in supplying quality food and makes the greatest contribution to growing economies and populations. Plant disease may cause significant losses in food production and eradicate diversity in species. Early diagnosis of plant diseases using accurate or automatic detection techniques can enhance the quality of food production and minimize economic losses. In recent years, deep learning has brought tremendous improvements in the recognition accuracy of image classification and object detection systems. Hence, in this paper, we utilized convolutional neural network (CNN)-based pre-trained models for efficient plant disease identification. We focused on fine tuning the hyperparameters of popular pre-trained models, such as DenseNet-121, ResNet-50, VGG-16, and Inception V4. The experiments were carried out using the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 classes. The performance of the model was evaluated through classification accuracy, sensitivity, specificity, and F1 score. A comparative analysis was also performed with similar state-of-the-art studies. The experiments proved that DenseNet-121 achieved 99.81% higher classification accuracy, which was superior to state-of-the-art models. [ABSTRACT FROM AUTHOR]

Site-specific weed control offers a great potential for herbicide savings in agricultural crops without causing yield losses and additional weed management costs in the following years. Therefore, precision weed management is an efficient tool to meet the EU targets for pesticide reduction. This review summarizes different commercial technologies and prototypes for precision patch spraying and spot spraying. All the presented technologies have in common that they consist of three essential parts. (1) Sensors and classifiers for weed/crop detection, (2) Decision algorithms to decide whether weed control is needed and to determine a suitable type and rate of herbicide. Usually, decision algorithms are installed on a controller and (3) a precise sprayer with boom section control or single nozzle control. One point that differs between some of the techniques is the way the decision algorithms classify. They are based on different approaches. Green vegetation can be differentiated from soil and crop residues based on spectral information in the visible and near-infrared wavebands ("Green on Brown"). Those sensors can be applied for real-time on/off control of single nozzles to control weeds before sowing after conservation tillage and in the inter-row area of crops. More sophisticated imaging algorithms are used to classify weeds in crops ("Green on Green"). This paper will focus on Convolutional Neural Networks (CNN) for plant species identification. Alternatively, the position of each crop can be recorded during sowing/planting and afterward herbicides can be targeted to single weeds or larger patches of weeds if the economic weed threshold is exceeded. With a standardized protocol of data communication between sensor, controller and sprayer, the user can combine different sensors with different sprayers. In this review, an ISOBUS communication protocol is presented for a spot sprayer. Precision chemical weed control can be realized with tractor-mounted sprayers and autonomous robots. Commercial systems for both classes will be introduced and their economic and environmental benefits and limitations will be highlighted. Farmers ask for robust systems with less need for maintenance and flexible application in different crops. [ABSTRACT FROM AUTHOR]

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset. [ABSTRACT FROM AUTHOR]

Phenotype analysis of leafy green vegetables in planting environment is the key technology of precision agriculture. In this paper, deep convolutional neural network is employed to conduct instance segmentation of leafy greens by weakly supervised learning based on box-level annotations and Excess Green (ExG) color similarity. Then, weeds are filtered based on area threshold, K-means clustering and time context constraint. Thirdly, leafy greens tracking is achieved by bipartite graph matching based on mask IoU measure. Under the framework of phenotype tracking, some time-context-dependent phenotype analysis tasks such as growth monitoring can be performed. Experiments show that the proposed method can achieve 0.95 F1-score and 76.3 sMOTSA (soft multi-object tracking and segmentation accuracy) by using weakly supervised annotation data. Compared with the fully supervised approach, the proposed method can effectively reduce the requirements for agricultural data annotation, which has more potential in practical applications. [ABSTRACT FROM AUTHOR]

Maize small leaf spot (Bipolaris maydis) is one of the most important diseases of maize. The severity of the disease cannot be accurately identified, the cost of pesticide application increases every year, and the agricultural ecological environment is polluted. Therefore, in order to solve this problem, this study proposes a novel deep learning network DISE-Net. We designed a dilated-inception module instead of the traditional inception module for strengthening the performance of multi-scale feature extraction, then embedded the attention module to learn the importance of interchannel relationships for input features. In addition, a dense connection strategy is used in model building to strengthen channel feature propagation. In this paper, we constructed a data set of maize small leaf spot, including 1268 images of four disease grades and healthy leaves. Comparative experiments show that DISE-Net with a test accuracy of 97.12% outperforms the classical VGG16 (91.11%), ResNet50 (89.77%), InceptionV3 (90.97%), MobileNetv1 (92.51%), MobileNetv2 (92.17%) and DenseNet121 (94.25%). In addition, Grad-Cam network visualization also shows that DISE-Net is able to pay more attention to the key areas in making the decision. The results showed that the DISE-Net was suitable for the classification of maize small leaf spot in the field. [ABSTRACT FROM AUTHOR]

Rapid and accurate grape leaf disease diagnosis is of great significance to its yield and quality of grape. In this paper, aiming at the identification of grape leaf diseases, a fast and accurate detection method based on fused deep features, extracted from a convolutional neural network (CNN), plus a support vector machine (SVM) is proposed. In the research, based on an open dataset, three types of state-of-the-art CNN networks, three kinds of deep feature fusion methods, seven species of deep feature layers, and a multi-class SVM classifier were studied. Firstly, images were resized to meet the input requirements of the CNN network; then, the deep features of the input images were extracted via the specific deep feature layer of the CNN network. Two kinds of deep features from different networks were then fused using different fusion methods to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused deep features. The experimental results on the open dataset show that the fused deep features with any kind of fusion method can obtain a better classification performance than using a single type of deep feature. The direct concatenation of the Fc1000 deep feature extracted from ResNet50 and ResNet101 can achieve the best classification result compared with the other two fusion methods, and its F1 score is 99.81%. Furthermore, the SVM classifier trained using the proposed method can achieve a classification performance comparable to that of using the CNN model directly, but the training time is less than 1 s, which has an advantage over spending tens of minutes training a CNN model. The experimental results indicate that the method proposed in this paper can achieve fast and accurate identification of grape leaf diseases and meet the needs of actual agricultural production. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we propose an original deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images from two different regions in Saudi Arabia, using two DJI drones, and we built a dataset of around 11,000 instances of palm trees. Then, we applied several recent convolutional neural network models (Faster R-CNN, YOLOv3, YOLOv4, and EfficientDet) to detect palms and other trees, and we conducted a complete comparative evaluation in terms of average precision and inference speed. YOLOv4 and EfficientDet-D5 yielded the best trade-off between accuracy and speed (up to 99% mean average precision and 7.4 FPS). Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to automatically detect the geographical location of detected palm trees. This geolocation technique was tested on two different types of drones (DJI Mavic Pro and Phantom 4 pro) and was assessed to provide an average geolocation accuracy that attains 1.6 m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, this innovative combination between deep learning object detection and geolocalization can be generalized to any other objects in UAV images. [ABSTRACT FROM AUTHOR]

This editorial introduces the Special Issue, entitled "Deep Learning (DL) Techniques for Agronomy Applications", of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) "Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks," by Chen et al.; (2) "Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques," by Alvarez et al.; and (3) "Development of a mushroom growth measurement system applying deep learning for image recognition," by Lu et al. One paper on DL-based time series data analysis techniques for agronomy applications is as follows: "LSTM neural network based forecasting model for wheat production in Pakistan," by Haider et al. One paper on behavior and strategy analysis for agronomy applications is as follows: "Research into the E-learning model of agriculture technology companies: analysis by deep learning," by Lin et al. [ABSTRACT FROM AUTHOR]

This paper deals with a soil moisture retrieval model design with airborne measurements for remote monitoring of soil moisture level in large crop fields. A small quadrotor unmanned aerial vehicle (UAV) is considered as a remote sensing platform for high spatial resolutions of airborne images and easy operations. A combination of multispectral and infrared (IR) sensors is applied to overcome the effects of canopies convering the field on the sensor measurements. Convolutional neural network (CNN) is utilized to take the measurement images directly as inputs for the soil moisture retrieval model without loss of information. The procedures to obtain an input image corresponding to a certain soil moisture level measurement point are addressed, and the overall structure of the proposed CNN-based model is suggested with descriptions. Training and testing of the proposed soil moisture retrieval model are conducted to verify and validate its performance and address the effects of input image sizes and errors on input images. The soil moisture level estimation performance decreases when the input image size increases as the ratio of the pixel corresponding to the point to estimate soil moisture level to the total number of pixels in the input image, whereas the input image size should be large enough to include this pixel under the errors in input images. The comparative study shows that the proposed CNN-based algorithm is advantageous on estimation performance by maintaining spatial information of pixels on the input images. [ABSTRACT FROM AUTHOR]

The segmentation and classification of leaves in plant images are a great challenge, especially when several leaves are overlapping in images with a complicated background. In this paper, the segmentation and classification of leaf images with a complicated background using deep learning are studied. First, more than 2500 leaf images with a complicated background are collected and artificially labeled with target pixels and background pixels. Two-thousand of them are fed into a Mask Region-based Convolutional Neural Network (Mask R-CNN) to train a model for leaf segmentation. Then, a training set that contains more than 1500 training images of 15 species is fed into a very deep convolutional network with 16 layers (VGG16) to train a model for leaf classification. The best hyperparameters for these methods are found by comparing a variety of parameter combinations. The results show that the average Misclassification Error ( M E ) of 80 test images using Mask R-CNN is 1.15%. The average accuracy value for the leaf classification of 150 test images using VGG16 is up to 91.5%. This indicates that these methods can be used to segment and classify the leaf image with a complicated background effectively. It could provide a reference for the phenotype analysis and automatic classification of plants. [ABSTRACT FROM AUTHOR]

Environmental stresses, such as drought and heat, can cause substantial yield loss in agriculture. As such, hybrid crops that are tolerant to drought and heat stress would produce more consistent yields compared to the hybrids that are not tolerant to these stresses. In the 2019 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the yield performances of 2452 corn hybrids planted in 1560 locations between 2008 and 2017 and asked participants to classify the corn hybrids as either tolerant or susceptible to drought stress, heat stress and combined drought and heat stress. However, no data was provided that classified any set of hybrids as tolerant or susceptible to any type of stress. In this paper, we present an unsupervised approach to solving this problem, which was recognized as one of the winners in the 2019 Syngenta Crop Challenge. Our results labeled 121 hybrids as drought tolerant, 193 as heat tolerant and 29 as tolerant to both stresses. [ABSTRACT FROM AUTHOR]

BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are associated with milk production, reproduction, and health of cows. However, in practice, obtaining BCS values is a time-consuming and subjective task performed visually by expert scorers. There have been several efforts to automate BCS of dairy cows by using image analysis and machine learning techniques. In a previous work, an automatic system to estimate BCS values was proposed, which is based on Convolutional Neural Networks (CNNs). In this paper we significantly extend the techniques exploited by that system via using transfer learning and ensemble modeling techniques to further improve BCS estimation accuracy. The improved system has achieved good estimations results in comparison with the base system. Overall accuracy of BCS estimations within 0.25 units of difference from true values has increased 4% (up to 82%), while overall accuracy within 0.50 units has increased 3% (up to 97%). [ABSTRACT FROM AUTHOR]

In Taiwan, mushrooms are an agricultural product with high nutritional value and economic benefit. However, global warming and climate change have affected plant quality. As a result, technological greenhouses are replacing traditional tin houses as locations for mushroom planting. These greenhouses feature several complex parameters. If we can reduce the complexity such greenhouses and improve the efficiency of their production management using intelligent schemes, technological greenhouses could become the expert assistants of farmers. In this paper, the main goal of the developed system is to measure the mushroom size and to count the amount of mushrooms. According to the results of each measurement, the growth rate of the mushrooms can be estimated. The proposed system also records the data of the mushrooms and broadcasts them to the mobile phone of the farmer. This improves the effectiveness of the production management. The proposed system is based on the convolutional neural network of deep learning, which is used to localize the mushrooms in the image. A positioning correction method is also proposed to modify the localization result. The experiments show that the proposed system has a good performance concerning the image measurement of mushrooms. [ABSTRACT FROM AUTHOR]