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Detección de enfermedades en el sector agrícola utilizando Inteligencia Artificial
... Si bien es cierto, esto genera muchas dificultades para los agricultores, ya que repercute en su trabajo, también se debe tener en cuenta que no todos poseen el conocimiento necesario, ni la capacidad para poder afrontar este tipo de desastres. En consecuencia, gracias a la inteligencia artificial, será capaz de diagnosticar, prevenir y tratar a este tipo de problemas para que cualquier persona, y más aún, los agricultores se vean beneficiados al tomar algunas acciones inmediatas que los ayude a combatir estas plagas y enfermedades (Roldán, Roshan, & Sánchez, 2019). ...
... De los algoritmos SVM se puede concluir que es otra buena alternativa para la resolución de estos casos, aunque en un principio, no fueron pensados para resolver este tipo de problemas, con el paso del tiempo se han ido adaptando y presentando resultados muy buenos. Con utilizar RBF y SOM parece bastante buena, comparados con KNN, ANN, Bayes, su enfoque de obtener las características por s u textura y color, hace que se obtenga mejores resultados, pues la elección de esta última es la que mejor se desenvuelve para estos casos (Roldán, Roshan, & Sánchez, 2019). ...
El presente artículo, detalla sobre la posibilidad de detectar enfermedades fúngicas en cultivos de arroz, utilizando técnicas de inteligencia artificial. Los autores de la información recopilada hacen propuestas de técnicas de obtención de características de las hojas o frutos de las plantas, así como también el uso de algoritmos clasificadores o de agrupación, todo esto con el fin de determinar si una hoja, presenta signos de alguna enfermedad. Al haber diversos tipos de enfermedades y diversas variedades de plantas, los autores hacen propuestas para utilizar el algoritmo que ellos consideran el que obtendrá mejores resultados. Al final se concluyó que, sí es posible detectar enfermedades trayendo consigo un beneficio directo para el agricultor que la implemente, ya que un diagnóstico oportuno, daría una respuesta a la enfermedad y por lo tanto reducción del riesgo en pérdidas económicas.
... Del análisis realizado se coincide con (Begue et al., 2017;Cervantes et al., 2017;Jye et al., 2017) en que las ANN y MLP corresponde al aprendizaje automático, en el cual el proceso de extracción de características se hace a través de enfoques tradicionales como Scale Invariant Feature Transform (SIFT), filtros GLCM, histogramas, técnicas basadas en áreas (ABT), algoritmos basados en colores y formas para modelar características de color y textura. Otra precisión en aprendizaje automático es que los datos deben de estar bien ordenados, etiquetados y estructurados (Roldán et al., 2019). La eliminación de ruido en las imágenes se hace con éxito, pero con una dificultad para reducir la sobrecarga computacional hasta llegar a un buen rendimiento de eliminación de ruido (S. ...
El rendimiento de las matemáticas complejas se abordó desde el uso de
tecnologías del ocio como recurso disruptivo en el aula virtual. El objetivo permitió
modificar la demanda cognitiva en matemática, mediante el uso de videojuegos
como gamificador de sesiones de aprendizaje. Participaron 250 escolares como
parte de una muestra latinoamericana. Luego de seis meses de desarrollo de
la investigación con el modelo experimental, concluimos en la mejora de las
habilidades en los sujetos para abordar la demanda de bajo nivel y la demanda
de tipo no conectiva luego de participar en clases gamificadas con modelo D-O
[Dinámica-Operativa]. Los eventos produjeron mejores posibilidades de producir
operaciones matemáticas basadas en el razonamiento, provocando mayor reflexión
en el grupo experimental. Las evidencias necesitan profundizarse en el nivel de
demanda conectiva (alto nivel) con otras muestras latinoamericanas.
Crop diseases are a key danger for food security, but their speedy identification still difficult in many portions of the world because of the lack of the essential infrastructure. The mixture of increasing worldwide smartphone dispersion and current advances in computer vision made conceivable by deep learning has cemented the way for smartphone-assisted disease identification. Using a public dataset of 9000 images of infected and healthy Tomato leaves collected under controlled conditions, we trained a deep convolutional neural network to identify 5 diseases. The trained model achieved an accuracy of 99.84% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.
Botany is the scientific examination of plants characters. Plant disease can be caused either by living organisms having wide range as harmful or benign ones which grow specifically on the horticultural plant leaves or deficiency of minerals e.g. iron, phosphate, nitrogen, etc. Modern agriculture requires smart and feasible techniques to replace the human intelligence with machine intelligence. Whereas a human identifies the disease infected leaves by his eye, the machine should also be capable of vision-based disease identification. The objective of this paper is to practically verify the applicability of a new computer-vision method for discrimination between the healthy and disease infected strawberry leaves which does not require neural network or time consuming trainings. The proposed method was tested under outdoor lighting condition using a regular DLSR camera without any particular lens. Since the type and infection degree of disease is approximated a human brain a fuzzy decision maker classifies the leaves over the images captured on-site having the same properties of human vision. Optimizing the fuzzy parameters for a typical horticultural field at a mid-day summer Mediterranean day in produced 96% accuracy for segmented iron deficiency and 93% accuracy for segmented using a typical human instant classification approximation as the benchmark holding higher accuracy than a human eye identifier. The fuzzy-base classifier provides approximate result for decision making on the leaf status as if it is healthy or not.
Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.
Agricultural productivity is something on which Economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases .It also covers survey on different diseases classification techniques that can be used for plant leaf disease detection. Image segmentation, which is an important aspect for disease detection in plant leaf disease, is done by using genetic algorithm.
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. When testing the model on a set of images collected from trusted online sources - i.e. taken under conditions different from the images used for training - the model still achieves an accuracy of 31.4%. While this accuracy is much higher than the one based on random selection (2.6%), a more diverse set of training data is needed to improve the general accuracy. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path towards smartphone-assisted crop disease diagnosis on a massive global scale.
The gap between the current capabilities of image-based methods for automatic plant disease identification and the real-world needs is still wide. Although advances have been made on the subject, most methods are still not robust enough to deal with a wide variety of diseases and plant species. This paper proposes a method for disease identification, based on colour transformations, colour histograms and a pairwise-based classification system. Its performance was tested using a large database containing images of symptoms belonging to 82 different biotic and abiotic stresses, affecting the leaves of 12 different plant species. The wide variety of images used in the tests made it possible to carry out an in-depth investigation about the main advantages and limitations of the proposed algorithm. A comparison with other algorithms is also presented, and some possible solutions for the main challenges that still prevent this kind of tool to be adopted in practice.
Crops are being affected by uneven climatic conditions leading to decreased agricultural yield. This affects global agricultural economy. Moreover, condition becomes even worst when the crops are infected by any disease. Also, increasing population burdens farmers to increase yield. This is where modern agricultural techniques and systems are needed to detect and prevent the crops from being effected by different diseases. In this paper, we propose a web based tool that helps farmers for identifying fruit disease by uploading fruit image to the system. The system has an already trained dataset of images for the pomegranate fruit. Input image given by the user undergoes several processing steps to detect the severity of disease by comparing with the trained dataset images. First the image is resized and then its features are extracted on parameters such as color, morphology, and CCV and clustering is done by using k-means algorithm. Next, SVM is used for classification to classify the image as infected or non-infected. An intent search technique is also provided which is very useful to find the user intension. Out of three features extracted we got best results using morphology. Experimental evaluation of the proposed approach is effective and 82% accurate to identify pomegranate disease.
Future food production is highly vulnerable to both climate change and air pollution with implications for global food security(1-4). Climate change adaptation and ozone regulation have been identified as important strategies to safeguard food production(5,6), but little is known about how climate and ozone pollution interact to affect agriculture, nor the relative effectiveness of these two strategies for different crops and regions. Here we present an integrated analysis of the individual and combined effects of 2000-2050 climate change and ozone trends on the production of four major crops (wheat, rice, maize and soybean) worldwide based on historical observations and model projections, specifically accounting for ozone-temperature co-variation. The projections exclude the effect of rising CO2, which has complex and potentially offsetting impacts on global food supply(7-10). We show that warming reduces global crop production by >10% by 2050 with a potential to substantially worsen global malnutrition in all scenarios considered. Ozone trends either exacerbate or offset a substantial fraction of climate impacts depending on the scenario, suggesting the importance of air quality management in agricultural planning. Furthermore, we find that depending on region some crops are primarily sensitive to either ozone (for example, wheat) or heat (for example, maize) alone, providing a measure of relative benefits of climate adaptation versus ozone regulation for food security in different regions.
Across the tropics, smallholder farmers already face numerous risks to agricultural production. Climate change is expected to disproportionately affect smallholder farmers and make their livelihoods even more precarious; however, there is limited information on their overall vulnerability and adaptation needs. We conducted surveys of 600 households in Madagascar to characterize the vulnerability of smallholder farmers, identify how farmers cope with risks and explore what strategies are needed to help them adapt to climate change. Malagasy farmers are particularly vulnerable to any shocks to their agricultural system owing to their high dependence on agriculture for their livelihoods, chronic food insecurity, physical isolation and lack of access to formal safety nets. Farmers are frequently exposed to pest and disease outbreaks and extreme weather events (particularly cyclones), which cause significant crop and income losses and exacerbate food insecurity. Although farmers use a variety of risk-coping strategies, these are insufficient to prevent them from remaining food insecure. Few farmers have adjusted their farming strategies in response to climate change, owing to limited resources and capacity. Urgent technical, financial and institutional support is needed to improve the agricultural production and food security of Malagasy farmers and make their livelihoods resilient to climate change.
This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research.
Deep learning is quickly becoming the standard technique for image classification. The main problem facing the automatic identification of plant diseases using this strategy is the lack of image databases capable of representing the wide variety of conditions and symptom characteristics found in practice. Data augmentation techniques decrease the impact of this problem, but those cannot reproduce most of the practical diversity. This paper explores the use of individual lesions and spots for the task, rather than considering the entire leaf. Since each region has its own characteristics, the variability of the data is increased without the need for additional images. This also allows the identification of multiple diseases affecting the same leaf. On the other hand, suitable symptom segmentation still needs to be done manually, preventing full automation. The accuracies obtained using this approach were, in average, 12% higher than those achieved using the original images. Additionally, no crop had accuracies below 75%, even when as many as 10 diseases were considered. Although the database does not cover the entire range of practical possibilities, these results indicate that, as long as enough data is available, deep learning techniques are effective for plant disease detection and recognition.
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called dropout that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry
Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system) using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM) and artificial neural network (ANN) classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops.
To reach the Millennium Development Goal of halving hunger by 2015, the Millennium Project's Task Force on Hunger recommends
seven mutually reinforcing actions: political action; national policy reforms; increased agricultural productivity for food-insecure
farmers; improved nutrition for the chronically hungry; productive safety nets for the acutely hungry; improved rural incomes
and markets; and restored natural resources essential for food security. The Task Force concludes that "it can be done"-the
Hunger MDG can be attained. In this
Policy Forum, the task force chairmen explain why achieving that goal will require unprecedented levels of effort, but it is well within
the reach of our technical and financial capabilities.
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