Use of images of leaves and fruits of apple trees for automatic identification of symptoms of diseases and nutritional disorders.

Rapid diagnosis ofsymptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The resultsshowed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way. Keywords Apple, Apple Disorders, Artificial Intelligence, Automatic Disease Identification, Classifications, Convolutional Neural Networks, Disorders, Machine Learnin

Classification of apple tree disorders using Convolutional Neural Networks.

Abstract?This paper studies the use of Convolutional Neural Networks to automatically detect and classify diseases, nutritional deficiencies and damage by herbicides on apple trees from images of their leaves. This task is fundamental to guarantee a high quality of the resulting yields and is currently largely performed by experts in the field, which can severely limit scale and add to costs. By using a novel data set containing labeled examples consisting of 2539 images from 6 known disorders, we show that trained Convolutional Neural Networks are able to match or outperform experts in this task, achieving a 97.3% accuracy on a hold-out set

Master in advanced techniques for research and development in food and agriculture

[SPA] El Máster de Técnicas Avanzadas en Investigación y Desarrollo Agrario y Alimentario, tiene como principal objetivo la formación de investigadores en el ámbito del desarrollo agrario y alimentario, pues todos los indicadores actuales muestran que es necesario un aumento de la masa crítica de investigadores en éste área en la UE; a pesar de que la agricultura, desde la fase de producción hasta la de procesado y fabricación tiene un campo muy amplio en el que se puede investigar e innovar, existen determinadas herramientas horizontales de trabajo que permiten formar alumnos con unas capacidades técnicas excelentes y de aplicación a ámbitos tan variados como el medio ambiente, genética y mejora animal y vegetal, procesado de alimentos o control de las plagas. Estos antecedentes nos han llevado a formular un programa de Máster que tiene cuatro módulos definidos, uno de cursos metodológicos y tres de cursos fundamentales. [ENG] The Master in Advanced Techniques in Agricultural and Food Research and Development intends to introduce university students into the research in such field. The programme is based on research carried out by the Higher Technical School of Agriculture Engineer on the one hand, and on the other hand on the technological development of the Spanish agricultural and food sector. This Master programme has as its main goal the qualifying of researchers in the area of agricultural and food development, since all the present indicators point at the need for an increase in the critical mass of researchers in this area in the EU; despite the fact that agriculture, from production to processing and manufacturing, is a wide field in which to research and innovate. There exist some horizontal tools that allow us to train students with excellent technical skills, and that can be applied to a wide range of areas such as environment, genetics, animal and vegetal improvement, food processing or pest control. On these grounds we have been led to draw up a Master programme that has four clearly defined modules: one with methodology courses and three with basic courses. The students who follow the proposed Master programme should become university experts in research and development in the agricultural and food field.[ENG] The Master in Advanced Techniques in Agricultural and Food Research and Development intends to introduce university students into the research in such field. The programme is based on research carried out by the Higher Technical School of Agriculture Engineer on the one hand, and on the other hand on the technological development of the Spanish agricultural and food sector. This Master programme has as its main goal the qualifying of researchers in the area of agricultural and food development, since all the present indicators point at the need for an increase in the critical mass of researchers in this area in the EU; despite the fact that agriculture, from production to processing and manufacturing, is a wide field in which to research and innovate. There exist some horizontal tools that allow us to train students with excellent technical skills, and that can be applied to a wide range of areas such as environment, genetics, animal and vegetal improvement, food processing or pest control. On these grounds we have been led to draw up a Master programme that has four clearly defined modules: one with methodology courses and three with basic courses. The students who follow the proposed Master programme should become university experts in research and development in the agricultural and food field

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop

Feature Papers in Horticulturae

Several of the 17 papers in this volume represent diverse strategies for improving sustainability in crop production systems. The maintenance of soil quality and the reclamation of marginal soils, improving tolerance to saline irrigation water, biodegradable alternatives to black plastic mulch, use of natural plant extracts against bacterial disease, and development of cultivars resistant to herbivorous arthropods address urgent priorities in sustainable systems. Two papers examine the driving forces and effects of adopting innovative agricultural technologies in food value chains in underdeveloped regions of the world, and identification of new Asian vegetable crop species for European environments and markets. Three papers reported on managing fruit set and ripening in important fruit crop species like orange, apple, and plum. Postharvest techniques to reduce disease and maintain fruit nutraceutical content were reported in separate papers. Classification techniques, conservation and utilization of unique plant species, and in vitro propagation techniques of species with potential horticultural value were described in four papers

Postharvest Technologies of Fresh Citrus Fruit: Advances and Recent Developments for the Loss Reduction during Handling and Storage

Citrus spp. are spread mainly in the Mediterranean basin and represent the largest fruit source for human consumption. Postharvest losses, mainly due to diseases and metabolic disorders of fruits, can cause severe wastage, reaching 30 to 50% of the total production. Preserving quality and extending shelf life are essential objectives for postharvest technological innovation, determined by the proper handling, treatment, storage and transport of harvested produce. Moreover, the application of novel sustainable strategies is critical for the reduction of synthetic fungicide residues on fruit surfaces and the impact on the environment caused by waste disposal of fungicides. In this article, the current knowledge about the safest and more sustainable strategies, as well as advanced postharvest handling and storage technologies, will be critically reviewed

Recent Innovations in Post-harvest Preservation and Protection of Agricultural Products

The global food supply chain relies on engineered systems, operational practices, and logistics to preserve, protect, process, and deliver agricultural crops along complex supply lines from farmers in low-, middle-, and high-income countries to markets around the world. Food and nutrition security is compromised by post-harvest losses (and food waste) that have been estimated to be as high as 20% in durable and 40% in perishable crops. Preserving crops using technologies and practices such as timely harvesting, evaporative cooling, cold and frozen storage, drying, and dehydrating, and protecting crops using technologies and practices such as damage-less handling, controlled and modified atmosphere storage, non-chemical heat and gas treatment, plant-derived protective films for individual fruits and vegetables, and improved packaging containers are critical to preserving nutrients, improving livelihoods, and realizing an efficient food system. This Special Issue aims to cover recent progress and innovations in science, technology, engineering, operational practices, and logistics related to post-harvest preservation and protection of durable and perishable agricultural crops. It seeks contributions that improve effectiveness, efficiency, reliability and sustainability in post-harvest handling of crops from field to end use that preserve product quality and result in foods and feeds which are nutritious and safe for human and animal consumption

Effect of canopy position and non-detructive determination of rind biochemical properties of citrus fruit during postharvest non-chilling cold storage.

Doctor of Philosophy in Horticultural Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.No abstract provided.This thesis is a compilation of manuscripts where each individual chapter is an independent article/manuscript introduced disjointedly