Technologies, methods, and approaches on detection system of plant pests and diseases

This research aims to identify the technology, methods, approaches applied in developing plant pest and disease detection systems. For this purpose, it mainly reviews systematically related research on identification, monitoring, detection, and control techniques of plant pests and diseases using a computer or mobile technology. Evidence from the literature shows previous both academia and practitioners have used various technologies, methods and approaches for developing detection system of plant pests and diseases. Some technologies have been applied for the detection system, such as web-based, mobile-based, and internet of things (IoT). Furthermore, the dominant approaches are expert system and deep learning. While backward chaining, forward chaining, fuzzy model, genetic algorithm (GA), K-means clustering, Bayesian networks and incremental learning, Naïve Bayes and Certainty Factors, Convolutional Neural Network, and Decision Tree are the most frequently methods applied in the previous researches. The review also indicated that no single technology or technique is best for developing accurate pest/disease detection system. Instead, the combination of technologies, methods, and approaches resulted in different performance and accuracies. A possible explanation for this is because the systems are used for detecting, controlling and monitoring various plants, such as corn, onion, wheat, rice, mango, flower, and others that are different. This research contributes by providing a reference for technologies, methods, and approaches to the detection system for plant pests and diseases. Also, it adds a way of literature review. This research has implications for researchers as a reference for researching in the computer system, especially for the detection of plant pest and disease research. Hence, this research also extends the body of knowledge of the intelligence system, deep learning, and computer science. For practice, the method references can be used for developing technology for detecting plant pest and disease

Digital image processing techniques for detecting, quantifying and classifying plant diseases.

Abstract. 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

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time

Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey

Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. This paper presents a literature review on ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and prediction of diseases and pests, with an emphasis on tomato crops. This survey aims to contribute to the development of smart farming and precision agriculture by promoting the development of techniques that will allow farmers to decrease the use of pesticides and chemicals while preserving and improving their crop quality and production.info:eu-repo/semantics/publishedVersio

IMPLEMENTASI METODE FUZZY C-MEANS DALAM PENGELOMPOKKAN HASIL PANEN PADI DI PROVINSI BALI

Di Indonesia, peranan padi sebagai salah satu komoditas pertanian tidak dapat diabaikan dan menjadi sangat penting. Salah satu cara yang efektif untuk mencapai peningkatan hasil pertanian yang optimal adalah menggunakan metode pengelompokkan. Terdapat beberapa metode pengelompokkan dalam klaster yang dapat digunakan, seperti k-means, hirarki, fuzzy c-means, DBSCAN, dan metode lainnya. Metode Fuzzy C-Means (FCM) menjadi solusi yang menarik untuk mengelompokkan hasil panen padi dengan lebih efektif. Penelitian ini bertujuan untuk mengimplementasikan metode Fuzzy C-Means (FCM) dalam pengelompokkan hasil panen padi di Provinsi Bali. Data yang digunakan adalah data sekunder yang diperoleh dari Kabupaten/Kota di Bali, termasuk variabel yang relevan seperti produktivitas, luas lahan, produksi padi, dan tinggi dataran. Hasilnya menunjukkan tiga kluster utama: kluster pertama terdiri dari Kabupaten Badung, Gianyar, dan Buleleng; kluster kedua terdiri dari Kabupaten Tabanan; dan kluster ketiga terdiri dari Kabupaten Jembrana, Klungkung, Bangli, Karangasem, dan Kota Denpasar. Untuk pengukuran kebaikan kluster digunakan nilai validasi, hasil validasi untuk nilai FSI sebesar 0,758, nilai PEI menghasilkan nilai 0,221, PCI dan MPCI, diperoleh nilai 0,888 dan 0,832

Uma ferramenta semiautomática para medição da área de lesões de Mancha Alvo em folhas de café.

Este documento apresenta uma ferramenta semiautomática que requer apenas algumas poucas interações com o usuário para medir lesões associadas com a doença mancha-alvo em folhas de café.bitstream/item/144642/1/Doc140.pd

TIC na segurança fitossanitária das cadeias produtivas.

Com a intensificação da indústria agropecuária, têm crescido os desafios e as preocupações relacionadas à segurança sanitária dos alimentos produzidos. A circulação de volumes cada vez maiores desse tipo de mercadoria exige que as medidas necessárias para garantir sua segurança sanitária sejam implementadas de maneira rápida, eficiente e barata. O controle manual tradicionalmente utilizado muitas vezes não é capaz de atender a esses requisitos. Como resultado, tecnologias de informação e comunicação têm sido cada vez mais utilizadas para: 1) Aumentar o grau de automação e, consequentemente, a velocidade dos processos de controle fitossanitário. 2) Identificar problemas sanitários tão cedo quanto possível, minimizando possíveis prejuízos econômicos, ambientais e sociais. 3) Identificar, a partir de variáveis ambientais e históricas, áreas potencialmente sujeitas a problemas sanitários, antes mesmo destes se manifestarem. Este capítulo trata especificamente dos dois últimos itens. Na Seção 2, são mostradas iniciativas voltadas ao diagnóstico de doenças em plantas, explorando tecnologias como processamento digital de imagens e sistemas especialistas. A Seção 3, por sua vez, apresenta iniciativas voltadas à construção de modelos de previsão e sistemas de alerta de doenças de culturas agrícolas

Processo automático para determinação de grau de infestação de mancha bacteriana em plantações de tomate industrial

Monografia (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Curso de Graduação em Engenharia de Controle e Automação, 2014.A avaliação do estado de contaminação de uma planta é uma etapa fundamental à automação na agricultura. A dosagem de pesticidas e fertilizantes, por exemplo, depende diretamente dessa análise. Tanto maior sua precisão, menores o desperdício de recursos e os efeitos colaterais, como a contaminação do solo. No entanto, por ser tarefa complexa e dependente de conhecimentos específicos, é geralmente cara e demorada, com uso justificável apenas em grandes escalas. Este trabalho apresenta um processo automático de avaliação do grau de saúde de uma planta de tomate industrial por meio de uma imagem digital do espécime em campo. Utilizando um agregado de técnicas de processamento de imagens, o sistema é capaz de fornecer análises que se mostraram equiparáveis às de especialistas, mas de maneira mais consistente e rápida. O processo, consolidado em um software, visa diminuir os custos operacionais da análise e ampliar a possibilidade do uso de metodologias que dela diretamente dependam, como a dosagem, em plantações de menor escala. Em aplicações que já adotam tais técnicas, a maior velocidade e precisão propiciam maior sofisticação dos métodos utilizados, com o consequente aumento de produtividade e eficiência.A plant’s contamination evaluation is a key step to automation in agriculture. The dosage of pesticides and fertilizers, for example, depends directly on this analysis. The greater its accuracy, the lower waste of resources and side effects, such as soil contamination. However, because it is complex and dependent on specific knowledge, it is generally expensive and time consuming, with use justified only in large scales. This work presents an automatic procedure for evaluating the health degree of an industrial tomato plant by means of a digital image of the specimen on-field. Using an array of image processing techniques, the system is able to provide analysis that is comparable to those of experts, but more consistently and faster. The process, consolidated in a software, aims to reduce the operating costs of the analysis and fostering the use of methodologies that directly depend on it, such as dosage, in small-scale plantations. For applications that already adopt such techniques, the speed and accuracy allow for greater sophistication of the methods used, resulting in increased productivity and efficiency

Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales

Reconciling continuous soil variation and crop yield : a study of some implications of within-field variability for site-specific crop management

Within-field variability in cropping system attributes is often obvious but difficult to accurately and efficiently quantity. The magnitude of the variation also changes with attribute, location and time. Importantly, variability at this scale of the soil/crop system may give rise to economic, environmental and societal problems on cropping enterprises under traditional 'uniform' management. In general, the problems arise from a decision to use 'mean-of-field' information to guide the amelioration of an area which may result in zones being under-or over-treated. Gathering data on, and extracting useful management information from, within-field variability is the goal of Precision Agriculture