Artificial Intelligence in Agriculture最新文献

Although convolutional neural network (CNN) paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures, few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice diseases. Moreover, most CNN-based rice disease detection studies only considered a small number of diseases in their experiments. Both these shortcomings were addressed in this study. In this study, a rice disease classification comparison of six CNN-based deep-learning architectures (DenseNet121, Inceptionv3, MobileNetV2, resNext101, Resnet152V, and Seresnext101) was conducted using a database of nine of the most epidemic rice diseases in Bangladesh. In addition, we applied a transfer learning approach to DenseNet121, MobileNetV2, Resnet152V, Seresnext101, and an ensemble model called DEX (Densenet121, EfficientNetB7, and Xception) to compare the six individual CNN networks, transfer learning, and ensemble techniques. The results suggest that the ensemble framework provides the best accuracy of 98%, and transfer learning can increase the accuracy by 17% from the results obtained by Seresnext101 in detecting and localizing rice leaf diseases. The high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural systems. This research is significant for farmers in rice-growing countries, as like many other plant diseases, rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

Efficient irrigation management of urban landscapes is critical in arid/semi-arid environments and depends on the reliable estimation of reference evapotranspiration (ET_o). However, the available measured climatic data in urban areas are typically insufficient to use the standard Penman-Monteith for ET_o estimation. Therefore, smart landscape irrigation controllers often use temperature-based ET_o models for autonomous irrigation scheduling. This study focuses on developing deep learning temperature-based ET_o models and comparing their performance with widely used empirical temperature-based models, including FAO Blaney & Criddle (BC), and Hargreaves & Samani (HS). We also developed a simple free and easy-to-access tool called DeepET for ET_o estimation using the best-performing deep learning models developed in this study. Four artificial neural network (ANN) models were developed using raw weather data as inputs and the reconstructed signal obtained from the wavelet transform as inputs. In addition, long short-term memory (LSTM) recurrent neural network (NN) and one-dimensional convolution neural network (CNN) models were developed. A total of 101 active California Irrigation Management Information System (CIMIS) weather stations were selected for this study, with >725,000 data points expanding from 1985 to 2019. The performance of the models was evaluated against the standard CIMIS ET_o. When evaluated at the independent sites, the temperature-based DL (Deep Learning) models showed 15–20% lower mean absolute error values than the calibrated HS model. No improvement in the performance of the ANN models was observed using reconstructed signals obtained from the wavelet transform. Our study suggests that DL models offer a promising alternative for more accurate estimations of ET_o in urban areas using only temperature as input. The DeepET can be accessed from the Haghverdi Water Management Group website: http://www. ucrwater.com/software-and-tools.html.

Deep Learning (DL), a type of Machine Learning, has gained significant interest in many fields, including agriculture. This paper aims to shed light on deep learning techniques used in agriculture for abiotic and biotic stress detection in fruits and vegetables, their benefits, and the challenges faced by users. Scientific papers were collected from Web of Science, Scopus, Google Scholar, Springer, and Directory of Open Access Journals (DOAJ) using combinations of specific keywords such as:’Deep Learning’ OR’Artificial Intelligence’ in combination with fruit disease’, vegetable disease’, ‘fruit stress', OR ‘vegetable stress' following PRISMA guidelines. From the initial 818 papers identified using the keywords, 132 were reviewed after excluding books, reviews, and the irrelevant. The recovered scientific papers were from 2003 to 2022; 93 % addressed biotic stress on fruits and vegetables. The most common biotic stresses on species are fungal diseases (grey spots, brown spots, black spots, downy mildew, powdery mildew, and anthracnose). Few studies were interested in abiotic stresses (nutrient deficiency, water stress, light intensity, and heavy metal contamination). Deep Learning and Convolutional Neural Networks were the most used keywords, with GoogleNet (18.28%), ResNet50 (16.67%), and VGG16 (16.67%) as the most used architectures. Fifty-two percent of the data used to compile these models come from the fields, followed by data obtained online. Precision problems due to unbalanced classes and the small size of some databases were also analyzed. We provided the research gaps and some perspectives from the reviewed papers. Further research works are required for a deep understanding of the use of machine learning techniques in fruit and vegetable studies: collection of large datasets according to different scenarios on fruit and vegetable diseases, evaluation of the effect of climatic variability on the fruit and vegetable yield using AI methods and more abiotic stress studies.

Modern pig farming leaves much to be desired in terms of efficiency, as these systems rely mainly on electromechanical controls and can only categorize pigs according to their weight. This method is not only inefficient but also escalates labor expenses and heightens the threat of zoonotic diseases. Furthermore, confining pigs in large groups can exacerbate the spread of infections and complicate the monitoring and care of ill pigs. This research executed an experiment to construct a deep-learning sorting mechanism, leveraging a dataset infused with pivotal metrics and breeding imagery gathered over 24 months. This research integrated a Kalman filter-based algorithm to augment the precision of the dynamic sorting operation. This research experiment unveiled a pioneering machine vision sorting system powered by deep learning, adept at handling live imagery for multifaceted recognition objectives. The Individual recognition model based on Residual Neural Network (ResNet) monitors livestock weight for sustained data forecasting, whereas the Wasserstein Generative Adversarial Nets (WGAN) image enhancement algorithm bolsters recognition in distinct settings, fortifying the model's resilience. Notably, system can pinpoint livestock exhibiting signs of potential illness via irregular body appearances and isolate them for safety. Experimental outcomes validate the superiority of this proposed system over traditional counterparts. It not only minimizes manual interventions and data upkeep expenses but also heightens the accuracy of livestock identification and optimizes data usage. This findings reflect an 89% success rate in livestock ID recognition, a 32% surge in obscured image recognition, a 95% leap in livestock categorization accuracy, and a remarkable 98% success rate in discerning images of unwell pigs. In essence, this research augments identification efficiency, curtails operational expenses, and provides enhanced tools for disease monitoring.

This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types.

Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless features. These techniques are also being integrated into modern farming systems, such as aquaponics, to address the challenges hindering its commercialization and large-scale implementation. Aquaponics is a farming technology that combines a recirculating aquaculture system and soilless hydroponics agriculture, that promises to address food security issues. To complement the current research efforts, a methodology is proposed to automatically measure the morphological traits of crops such as width, length and area and estimate the effective plant spacing between grow channels. Plant spacing is one of the key design parameters that are dependent on crop type and its morphological traits and hence needs to be monitored to ensure high crop yield and quality which can be impacted due to foliage occlusion or overlapping as the crop grows. The proposed approach uses Mask-RCNN to estimate the size of the crops and a mathematical model to determine plant spacing for a self-adaptive aquaponics farm. For common little gem romaine lettuce, the growth is estimated within 2 cm of error for both length and width. The final model is deployed on a cloud-based application and integrated with an ontology model containing domain knowledge of the aquaponics system. The relevant knowledge about crop characteristics and optimal plant spacing is extracted from ontology and compared with results obtained from the final model to suggest further actions. The proposed application finds its significance as a decision support system that can pave the way for intelligent system monitoring and control.

For commercial broiler production, about 20,000–30,000 birds are raised in each confined house, which has caused growing public concerns on animal welfare. Currently, daily evaluation of broiler wellbeing and growth is conducted manually, which is labor-intensive and subjectively subject to human error. Therefore, there is a need for an automatic tool to detect and analyze the behaviors of chickens and predict their welfare status. In this study, we developed a YOLOv5-CBAM-broiler model and tested its performance for detecting broilers on litter floor. The proposed model consisted of two parts: (1) basic YOLOv5 model for bird or broiler feature extraction and object detection; and (2) the convolutional block attention module (CBAM) to improve the feature extraction capability of the network and the problem of missed detection of occluded targets and small targets. A complex dataset of broiler chicken images at different ages, multiple pens and scenes (fresh litter versus reused litter) was constructed to evaluate the effectiveness of the new model. In addition, the model was compared to the Faster R-CNN, SSD, YOLOv3, EfficientDet and YOLOv5 models. The results demonstrate that the precision, recall, F1 score and an [email protected] of the proposed method were 97.3%, 92.3%, 94.7%, and 96.5%, which were superior to the comparison models. In addition, comparing the detection effects in different scenes, the YOLOv5-CBAM model was still better than the comparison method. Overall, the proposed YOLOv5-CBAM-broiler model can achieve real-time accurate and fast target detection and provide technical support for the management and monitoring of birds in commercial broiler houses.

In agriculture, precise fertilization and effective nutrient management are critical. Machine learning (ML) has recently been increasingly used to develop decision support tools for modern agricultural systems, including nutrient management, to improve yields while reducing expenses and environmental impact. ML based systems require huge amounts of data from different platforms to handle non-linear tasks and build predictive models that can improve agricultural productivity. This study reviews machine learning based techniques for estimating fertilizer and nutrient status that have been developed in the last decade. A thorough investigation of detection and classification approaches was conducted, which served as the basis for a detailed assessment of the key challenges that remain to be addressed. The research findings suggest that rapid improvements in machine learning and sensor technology can provide cost-effective and thorough nutrient assessment and decision-making solutions. Future research directions are also recommended to improve the practical application of this technology.

The cactus is a plant that grows in many rural areas, widely used as a hedge, and has multiple benefits through the manufacture of various cosmetics and other products. However, this crop has been suffering for some time from the attack of the carmine scale Dactylopius opuntia (Hemiptera: Dactylopiidae). The infestation can spread rapidly if not treated in the early stage. Current solutions consist of regular field checks by the naked eyes carried out by experts. The major difficulty is the lack of experts to check all fields, especially in remote areas. In addition, this requires time and resources. Hence the need for a system that can categorize the health level of cacti remotely. To date, deep learning models used to categorize plant diseases from images have not addressed the mealy bug infestation of cacti because computer vision has not sufficiently addressed this disease. Since there is no public dataset and smartphones are commonly used as tools to take pictures, it might then be conceivable for farmers to use them to categorize the infection level of their crops. In this work, we developed a system called CactiVIT that instantly determines the health status of cacti using the Visual image Transformer (ViT) model. We also provided a new image dataset of cochineal infested cacti.¹ Finally, we developed a mobile application that delivers the classification results directly to farmers about the infestation in their fields by showing the probabilities related to each class. This study compares the existing models on the new dataset and presents the results obtained. The VIT-B-16 model reveals an approved performance in the literature and in our experiments, in which it achieved 88.73% overall accuracy with an average of +2.61% compared to other convolutional neural network (CNN) models that we evaluated under similar conditions.

In the field of precision agriculture, diagnosing rice diseases from images remains challenging due to high error rates, multiple influencing factors, and unstable conditions. While machine learning and convolutional neural networks have shown promising results in identifying rice diseases, they were limited in their ability to explain the relationships among disease features. In this study, we proposed an improved rice disease classification method that combines a convolutional neural network (CNN) with a bidirectional gated recurrent unit (BiGRU). Specifically, we introduced a residual mechanism into the Inception module, expanded the module's depth, and integrated an improved Convolutional Block Attention Module (CBAM). We trained and tested the improved CNN and BiGRU, concatenated the outputs of the CNN and BiGRU modules, and passed them to the classification layer for recognition. Our experiments demonstrate that this approach achieves an accuracy of 98.21% in identifying four types of rice diseases, providing a reliable method for rice disease recognition research.