Artificial Intelligence in Agriculture最新文献

{"title":"Vision Intelligence for Smart Sheep Farming: Applying Ensemble Learning to Detect Sheep Breeds","authors":"Galib Muhammad Shahriar Himel ,&nbsp;Md. Masudul Islam ,&nbsp;Mijanur Rahaman","doi":"10.1016/j.aiia.2023.11.002","DOIUrl":"https://doi.org/10.1016/j.aiia.2023.11.002","url":null,"abstract":"<div><p>The ability to automatically recognize sheep breeds holds significant value for the sheep industry. Sheep farmers often require breed identification to assess the commercial worth of their flocks. However, many farmers specifically the novice one encounter difficulties in accurately identifying sheep breeds without experts in the field. Therefore, there is a need for autonomous approaches that can effectively and precisely replicate the breed identification skills of a sheep breed expert while functioning within a farm environment, thus providing considerable benefits the industry-specific to the novice farmers in the industry. To achieve this objective, we suggest utilizing a model based on convolutional neural networks (CNNs) which can rapidly and efficiently identify the type of sheep based on their facial features. This approach offers a cost-effective solution. To conduct our experiment, we utilized a dataset consisting of 1680 facial images which represented four distinct sheep breeds. This paper proposes an ensemble method that combines Xception, VGG16, InceptionV3, InceptionResNetV2, and DenseNet121 models. During the transfer learning using this pre-trained model, we applied several optimizers and loss functions and chose the best combinations out of them. This classification model has the potential to aid sheep farmers in precisely and efficiently distinguishing between various breeds, enabling more precise assessments of sector-specific classification for different businesses.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"11 ","pages":"Pages 1-12"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S258972172300048X/pdfft?md5=5303eef40412bbb4acced911b2385da5&pid=1-s2.0-S258972172300048X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138582174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes","authors":"P. Isaac Ritharson ,&nbsp;Kumudha Raimond ,&nbsp;X. Anitha Mary ,&nbsp;Jennifer Eunice Robert ,&nbsp;Andrew J","doi":"10.1016/j.aiia.2023.11.001","DOIUrl":"https://doi.org/10.1016/j.aiia.2023.11.001","url":null,"abstract":"<div><p>Rice stands as a crucial staple food globally, with its enduring sustainability hinging on the prompt detection of rice leaf diseases. Hence, efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical testing. In the recent past, the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment, which proves to be time-consuming and inefficient. This study offers a remedy by harnessing Deep Learning (DL) and transfer learning techniques to accurately identify and classify rice leaf diseases. A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets, categorized into 9 classes irrespective of the extent of disease spread across the leaves. These classes encompass diverse states including healthy leaves, mild and severe blight, mild and severe tungro, mild and severe blast, as well as mild and severe brown spot. Following meticulous manual labelling and dataset segmentation, which was validated by horticulture experts, data augmentation strategies were implemented to amplify the number of images. The datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks models. Their performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16, Xception, ResNet50, DenseNet121, Inception ResnetV2, and Inception V3. The effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images, yielding an exceptional accuracy of 99.94%, surpassing the benchmarks set by existing state-of-the-art models. Further, the layer wise feature extraction is also visualized as the interpretable AI.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"11 ","pages":"Pages 34-49"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721723000430/pdfft?md5=8298c1c42100a96a98aecb1442163521&pid=1-s2.0-S2589721723000430-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138656374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cumulative unsupervised multi-domain adaptation for Holstein cattle re-identification","authors":"Fabian Dubourvieux ,&nbsp;Guillaume Lapouge ,&nbsp;Angélique Loesch ,&nbsp;Bertrand Luvison ,&nbsp;Romaric Audigier","doi":"10.1016/j.aiia.2023.10.002","DOIUrl":"https://doi.org/10.1016/j.aiia.2023.10.002","url":null,"abstract":"<div><p>In dairy farming, ensuring the health of each cow and minimizing economic losses requires individual monitoring, achieved through cow <em>Re</em>-Identification (Re-ID). Computer vision-based Re-ID relies on visually distinguishing features, such as the distinctive coat patterns of breeds like Holstein.</p><p>However, annotating every cow in each farm is cost-prohibitive. Our objective is to develop <em>Re</em>-ID methods applicable to both labeled and unlabeled farms, accommodating new individuals and diverse environments. Unsupervised Domain Adaptation (UDA) techniques bridge this gap, transferring knowledge from labeled source domains to unlabeled target domains, but have only been mainly designed for pedestrian and vehicle <em>Re</em>-ID applications.</p><p>Our work introduces Cumulative Unsupervised Multi-Domain Adaptation (CUMDA) to address challenges of limited identity diversity and diverse farm appearances. CUMDA accumulates knowledge from all domains, enhancing specialization in known domains and improving generalization to unseen domains. Our contributions include a CUMDA method adapting to multiple unlabeled target domains while preserving source domain performance, along with extensive cross-dataset experiments on three cattle <em>Re</em>-ID datasets. These experiments demonstrate significant enhancements in source preservation, target domain specialization, and generalization to unseen domains.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"10 ","pages":"Pages 46-60"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721723000429/pdfft?md5=415adc99dee89219367d287b9bd79295&pid=1-s2.0-S2589721723000429-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91987268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harvest optimization for sustainable agriculture: The case of tea harvest scheduling","authors":"Bedirhan Sarımehmet,&nbsp;Mehmet Pınarbaşı,&nbsp;Hacı Mehmet Alakaş,&nbsp;Tamer Eren","doi":"10.1016/j.aiia.2023.10.001","DOIUrl":"https://doi.org/10.1016/j.aiia.2023.10.001","url":null,"abstract":"<div><p>To ensure sustainability in agriculture, many optimization problems need to be solved. An important one of them is harvest scheduling problem. In this study, the harvest scheduling problem for the tea is discussed. The tea harvest problem includes the creating a harvest schedule by considering the farmers' quotas under the purchase location and factory capacity. Tea harvesting is carried out in cooperation with the farmer - factory. Factory management is interested in using its resources. So, the factory capacity, purchase location capacities and number of expeditions should be considered during the harvesting process. When the farmer's side is examined, it is seen that the real professions of farmers are different. On harvest days, farmers often cannot attend to their primary professions. Considering the harvest day preferences of farmers in creating the harvest schedule are of great importance for sustainability in agriculture. Two different mathematical models are proposed to solve this problem. The first model minimizes the number of weekly expeditions of factory vehicles within the factor and purchase location capacity restrictions. The second model minimizes the number of expeditions and aims to comply with the preferences of the farmers as much as possible. A sample application was performed in a region with 12 purchase locations, 988 farmers, and 3392 decares of tea fields. The results show that the compliance rate of farmers to harvesting preferences could be increased from 52% to 97%, and this situation did not affect the number of expeditions of the factory. This result shows that considering the farmers' preferences on the harvest day will have no negative impact on the factory. On the contrary, it was concluded that this situation increases sustainability and encouragement in agriculture. Furthermore, the results show that models are effective for solving the problem.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"10 ","pages":"Pages 35-45"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50186814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based spectral and spatial analysis of hyper- and multi-spectral leaf images for Dutch elm disease detection and resistance screening","authors":"Xing Wei ,&nbsp;Jinnuo Zhang ,&nbsp;Anna O. Conrad ,&nbsp;Charles E. Flower ,&nbsp;Cornelia C. Pinchot ,&nbsp;Nancy Hayes-Plazolles ,&nbsp;Ziling Chen ,&nbsp;Zhihang Song ,&nbsp;Songlin Fei ,&nbsp;Jian Jin","doi":"10.1016/j.aiia.2023.09.003","DOIUrl":"https://doi.org/10.1016/j.aiia.2023.09.003","url":null,"abstract":"<div><p>Diseases caused by invasive pathogens are an increasing threat to forest health, and early and accurate disease detection is essential for timely and precision forest management. The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees. In this study, Dutch elm disease (DED; caused by <em>Ophiostoma novo-ulmi,</em>) and American elm (<em>Ulmus americana</em>) was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper- and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection. Hyper- and multi-spectral images were collected from leaves of American elm genotypes with varied disease susceptibilities after mock-inoculation and inoculation with <em>O. novo-ulmi</em> under greenhouse conditions. Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes. Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED. Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees. In addition, spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes. Though further studies are needed to assess applications in other pathosystems, hyper- and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"10 ","pages":"Pages 26-34"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50186813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning for weed–plant discrimination in agriculture 5.0: An in-depth review","authors":"Filbert H. Juwono ,&nbsp;W.K. Wong ,&nbsp;Seema Verma ,&nbsp;Neha Shekhawat ,&nbsp;Basil Andy Lease ,&nbsp;Catur Apriono","doi":"10.1016/j.aiia.2023.09.002","DOIUrl":"https://doi.org/10.1016/j.aiia.2023.09.002","url":null,"abstract":"<div><p>Agriculture 5.0 is an emerging concept where sensors, big data, Internet-of-Things (IoT), robots, and Artificial Intelligence (AI) are used for agricultural purposes. Different from Agriculture 4.0, robots and AI become the focus of the implementation in Agriculture 5.0. One of the applications of Agriculture 5.0 is weed management where robots are used to discriminate weeds from the crops or plants so that proper action can be performed to remove the weeds. This paper discusses an in-depth review of Machine Learning (ML) techniques used for discriminating weeds from crops or plants. We specifically present a detailed explanation of five steps required in using ML algorithms to distinguish between weeds and plants.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"10 ","pages":"Pages 13-25"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50186812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crop diagnostic system: A robust disease detection and management system for leafy green crops grown in an aquaponics facility","authors":"R. Abbasi ,&nbsp;P. Martinez ,&nbsp;R. Ahmad","doi":"10.1016/j.aiia.2023.09.001","DOIUrl":"https://doi.org/10.1016/j.aiia.2023.09.001","url":null,"abstract":"<div><p>Crops grown on aquaponics farms are susceptible to various diseases or biotic stresses during their growth cycle, just like traditional agriculture. The early detection of diseases is crucial to witnessing the efficiency and progress of the aquaponics system. Aquaponics combines recirculating aquaculture and soilless hydroponics methods and promises to ensure food security, reduce water scarcity, and eliminate carbon footprint. For the large-scale implementation of this farming technique, a unified system is needed that can detect crop diseases and support researchers and farmers in identifying potential causes and treatments at early stages. This study proposes an automatic crop diagnostic system for detecting biotic stresses and managing diseases in four leafy green crops, lettuce, basil, spinach, and parsley, grown in an aquaponics facility. First, a dataset comprising 2640 images is constructed. Then, a disease detection system is developed that works in three phases. The first phase is a crop classification system that identifies the type of crop. The second phase is a disease identification system that determines the crop's health status. The final phase is a disease detection system that localizes and detects the diseased and healthy spots in leaves and categorizes the disease. The proposed approach has shown promising results with accuracy in each of the three phases, reaching 95.83%, 94.13%, and 82.13%, respectively. The final disease detection system is then integrated with an ontology model through a cloud-based application. This ontology model contains domain knowledge related to crop pathology, particularly causes and treatments of different diseases of the studied leafy green crops, which can be automatically extracted upon disease detection allowing agricultural practitioners to take precautionary measures. The proposed application finds its significance as a decision support system that can automate aquaponics facility health monitoring and assist agricultural practitioners in decision-making processes regarding crop and disease management.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"10 ","pages":"Pages 1-12"},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50186811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of CNN-based deep learning architectures for rice diseases classification","authors":"Md Taimur Ahad ,&nbsp;Yan Li ,&nbsp;Bo Song ,&nbsp;Touhid Bhuiyan","doi":"10.1016/j.aiia.2023.07.001","DOIUrl":"10.1016/j.aiia.2023.07.001","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"9 ","pages":"Pages 22-35"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45329372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of temperature-based deep learning models to estimate reference evapotranspiration","authors":"Amninder Singh,&nbsp;Amir Haghverdi","doi":"10.1016/j.aiia.2023.08.003","DOIUrl":"10.1016/j.aiia.2023.08.003","url":null,"abstract":"<div><p>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 &amp; Criddle (BC), and Hargreaves &amp; 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 &gt;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: <span>http://www</span><svg><path></path></svg>. <span>ucrwater.com/software-and-tools.html</span><svg><path></path></svg>.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"9 ","pages":"Pages 61-75"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48943818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning methods for biotic and abiotic stresses detection and classification in fruits and vegetables: State of the art and perspectives","authors":"Sèton Calmette Ariane Houetohossou ,&nbsp;Vinasetan Ratheil Houndji ,&nbsp;Castro Gbêmêmali Hounmenou ,&nbsp;Rachidatou Sikirou ,&nbsp;Romain Lucas Glele Kakaï","doi":"10.1016/j.aiia.2023.08.001","DOIUrl":"10.1016/j.aiia.2023.08.001","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"9 ","pages":"Pages 46-60"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46218612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}