IoT-Enabled Smart Robotic System for Greenhouse Management using Deep Learning Model with STS Approach

A significant component of any country's Gross Domestic Product is made up of farming and agriculture. Utilizing IoT in agriculture and farming methods is essential as the global population is projected to reach around 9.6 billion by 2050. To meet such high demand, an improvisation and optimization of the current farming technologies is the need of the hour. Numerous researchers developed different application specific system for agriculture but less attention was paid towards critical aspects such as intelligence, modularity and human centric design. There is lacuna in existing developed system in the utilization of advanced technologies to their full potential. The agricultural sector wants autonomous systems that are smarter and more effective. Therefore, this research paper introduced the smart solution as an intelligent modular autonomous system with human-centric design approach for agricultural application. The developed system able to detect plant disease with more than 96% accuracy with the help of novel deep learning model designed with sharpening to Smoothening approach. The disease detection and classification results has been verified through confusion matrix method of evaluation. An intelligent robotic system has been developed to detect plant diseases using novel deep learning model and perform multiple functions like greenhouse monitoring, pesticide sprinkling etc. The robotic system has control over internet through web control system so that farmer can monitor greenhouse and control robot activity from remote place. This smart farming solution able to make farmer life simpler and perform difficult task like plant disease detection and pesticide sprinkling easily

Apple scab detection using CNN and Transfer Learning

Received: January 11th, 2021 ; Accepted: April 10th, 2021 ; Published: April 22nd, 2021 ; Correspondence: [email protected] goal of smart and precise horticulture is to increase yield and product quality by simultaneous reduction of pesticide application, thereby promoting the improvement of food security. The scope of this research is apple scab detection in the early stage of development using mobile phones and artificial intelligence based on convolutional neural network (CNN) applications. The research considers data acquisition and CNN training. Two datasets were collected - with images of scab infected fruits and leaves of an apple tree. However, data acquisition is a time-consuming process and scab appearance has a probability factor. Therefore, transfer learning is an appropriate training methodology. The goal of this research was to select the most suitable dataset for transfer learning for the apple scab detection domain and to evaluate the transfer learning impact comparing it with learning from scratch. The statistical analysis confirmed the positive effect of transfer learning on CNN performance with significance level 0.05

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

Un enfoque para la detección de enfermedades de las plantas utilizando técnicas de aprendizaje profundo

Agriculture is the backbone of Indian economy. Conventional farming systems are no longer being followed by our generation, due to lack of knowledge and expertise. Advancement of technologies pave a path that make a transition from traditional farming methods to smart agriculture by automating the processes involved. Challenges faced by today’s agriculture are depletion of soil nutrients and diseases caused by pests which lead to low productivity, irrigation problems, soil erosion, shortage of storage facilities, availability of quality seeds, lack of transportation, poor marketing etc. Among all these challenges in agriculture, prediction of diseases remains a major issue to be addressed. Identifying diseases based on visual inspection is the traditional way of farming which needs knowledge and experience to handle. Automating the process of detecting and identifying through visual inspection (cognitive) is the motivation behind this work. This is made possible with the availability of images of the plant or parts of plants, since most diseases are reflected on the leaves. A deep learning network architecture named Plant Disease Detection Network PDDNet-cv and a transfer learning approach of identifying diseases in plants were proposed. Our proposed system is compared with VGG19, ResNet50, InceptionResNetV2, the state-of-the-art methods reported in [9, 13, 5] and the results show that our method is significantly performing better than the existing systems. Our proposed PDDNet-cv has achieved average classification accuracy of 99.09% in detecting different classes of diseases. The proposed not so deep architecture is performing well compared to other deep learning architectures in terms of performance and computational time.La agricultura es la columna vertebral de la economía india. Los sistemas agrícolas convencionales ya no están siendo seguidos por nuestra generación, debido a la falta de conocimiento y experiencia. El avance de las tecnologías allana un camino que hace una transición de los métodos agrícolas tradicionales a la agricultura inteligente mediante la automatización de los procesos involucrados. Los desafíos que enfrenta la agricultura actual son el agotamiento de los nutrientes del suelo, las enfermedades causadas por plagas que conducen a una baja productividad, los problemas de riego, la erosión del suelo, la escasez de instalaciones de almacenamiento, la disponibilidad de semillas de calidad, la falta de transporte, la mala comercialización, etc. Entre todos estos desafíos en la agricultura, la predicción de enfermedades sigue siendo un tema importante que debe abordarse. La identificación de enfermedades basadas en la inspección visual es la forma tradicional de cultivo que necesita el conocimiento y la experiencia para manejarlas que obtiene un buen rendimiento. Automatizar el proceso de detección e identificación a través de la inspección visual (cognitiva) es la motivación detrás de este trabajo. Esto es posible gracias a la disponibilidad de imágenes de la planta o partes de plantas, ya que la mayoría de las enfermedades se reflejan en las hojas. Se propuso una arquitectura de red de aprendizaje profundo llamada Red de Detección de Enfermedades de las plantas por sus siglas en inglés (Plant Disease Detection Network PDDNet-cv) y un enfoque de aprendizaje por transferencia para identificar enfermedades en las plantas. Nuestro sistema propuesto se compara con VGG19, ResNet50, InceptionResNetV2, los métodos de vanguardia reportados en [9, 13, 5] y los resultados muestran que nuestro método está funcionando significativamente mejor que los sistemas existentes. Nuestra propuesta PDDNet-cv ha logrado una precisión de clasificación promedio del 99,09% en la detección de diferentes clases de enfermedades. La arquitectura no tan profunda propuesta, está funcionando bien en comparación con otras arquitecturas de aprendizaje profundo en términos de rendimiento y tiempo computacional

Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery

Early detection of diseases in growing olive trees is essential for reducing costs and increasing productivity in this crucial economic activity. The quality and quantity of olive oil depend on the health of the fruit, making accurate and timely information on olive tree diseases critical to monitor growth and anticipate fruit output. The use of unmanned aerial vehicles (UAVs) and deep learning (DL) has made it possible to quickly monitor olive diseases over a large area indeed of limited sampling methods. Moreover, the limited number of research studies on olive disease detection has motivated us to enrich the literature with this work by introducing new disease classes and classification methods for this tree. In this study, we present a UAV system using convolutional neuronal network (CNN) and transfer learning (TL). We constructed an olive disease dataset of 14K images, processed and trained it with various CNN in addition to the proposed MobileNet-TL for improved classification and generalization. The simulation results confirm that this model allows for efficient diseases classification, with a precision accuracy achieving 99% in validation. In summary, TL has a positive impact on MobileNet architecture by improving its performance and reducing the training time for new tasks

Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for Early Detection and Mapping of Red Palm Weevil

The Red Palm Weevil (RPW) is a highly destructive insect causing economic losses and impacting palm tree farming worldwide. This paper proposes an innovative approach for sustainable palm tree farming by utilizing advanced technologies for the early detection and management of RPW. Our approach combines computer vision, deep learning (DL), the Internet of Things (IoT), and geospatial data to detect and classify RPW-infested palm trees effectively. The main phases include; (1) DL classification using sound data from IoT devices, (2) palm tree detection using YOLOv8 on UAV images, and (3) RPW mapping using geospatial data. Our custom DL model achieves 100% precision and recall in detecting and localizing infested palm trees. Integrating geospatial data enables the creation of a comprehensive RPW distribution map for efficient monitoring and targeted management strategies. This technology-driven approach benefits agricultural authorities, farmers, and researchers in managing RPW infestations and safeguarding palm tree plantations' productivity

Artificial Intelligence: The Future of Sustainable Agriculture? A Research Agenda

Global warming and the increasing food demand are problems of the current generation and require a change towards sustainable agriculture. In recent years, research in the field of artificial intelligence has made considerable progress. Thus, the use of artificial intelligence in agriculture can be a promising solution to ensure sufficient food supply on a global scale. To investigate the state-of-the-art in the use of artificial intelligence-based systems in agriculture, we provide a structured literature review. We show that research has been done in the field of irrigation and plant growth. In this regard, camera systems often provide images as training/input data for artificial intelligence-based systems. Finally, we provide a research agenda to pave the way for further research on the use of artificial intelligence in sustainable agriculture