Special Issue "Sensors and Remote Sensing in Precision Horticulture"

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 1588

Special Issue Editors

Geosystems Research Institute, Mississippi State University, Starkville, MS 39762, USA
Interests: signal processing; learning problems in wireless; sensor networks using optimization; probability

Special Issue Information

Dear Colleagues,

Precision horticulture is a data-driven management method that collects site- or plant-specific information of fruits and vegetables in order to (1) make in-growth decisions to improve production and (2) postharvest process management. Precision horticulture is particularly advantageous to the farmer due to the high value of their products and the high quantities of crop inputs required to produce horticultural crops. Clearly any cost reduction significantly boosts producer profits and effective utilization of crop inputs may lessen the environmental impact of horticultural crop production.

In horticulture, analysis of the product's quality is more crucial than in any other crop. Typically, the field size is less than that of agricultural output. Even single plants may be handled individually in accordance with the spatial or temporal pattern, as the planting density is reduced. Precision horticulture implementation relies primarily on sensors and systems that can collect weather, soil, and plant-specific data at a reasonable cost. Optical sensors are the most prevalent, and many approaches have demonstrated the promise for effective, quick, non-invasive in-situ disease diagnosis and yield estimate. The most common applications are biotic and abiotic stress detection at asymptomatic or early stages, canopy size and density, yield estimation, and crop quality, among other data.

Dr. Alessandro Matese
Dr. Santhana Krishnan Boopalan
Guest Editors

Manuscript Submission Information

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  • precision horticulture
  • yield monitor
  • quality monitor
  • agricultural decision support systems (AgriDSS)
  • remote sensing applications
  • proximal sensors
  • artificial intelligence (AI) and machine learning (ML) methodologies
  • internet of Things (IoT)
  • variable-rate input applications
  • automated machinery and agricultural robots

Published Papers (1 paper)

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Automation of Crop Disease Detection through Conventional Machine Learning and Deep Transfer Learning Approaches
Agriculture 2023, 13(2), 352; https://doi.org/10.3390/agriculture13020352 - 31 Jan 2023
Cited by 1 | Viewed by 1129
With the rapid population growth, increasing agricultural productivity is an extreme requirement to meet demands. Early identification of crop diseases is essential to prevent yield loss. Nevertheless, it is a tedious task to manually monitor leaf diseases, as it demands in-depth knowledge of [...] Read more.
With the rapid population growth, increasing agricultural productivity is an extreme requirement to meet demands. Early identification of crop diseases is essential to prevent yield loss. Nevertheless, it is a tedious task to manually monitor leaf diseases, as it demands in-depth knowledge of plant pathogens as well as a lot of work, and excessive processing time. For these purposes, various methods based on image processing, deep learning, and machine learning are developed and examined by researchers for crop leaf disease identification and often have obtained significant results. Motivated by this existing work, we conducted an extensive comparative study between traditional machine learning (SVM, LDA, KNN, CART, RF, and NB) and deep transfer learning (VGG16, VGG19, InceptionV3, ResNet50, and CNN) models in terms of precision, accuracy, f1-score, and recall on a dataset taken from the PlantVillage Dataset composed of diseased and healthy crop leaves for binary classification. Moreover, we applied several activation functions and deep learning optimizers to further enhance these CNN architectures’ performance. The classification accuracy (CA) of leaf diseases that we obtained by experimentation is quite impressive for all models. Our findings reveal that NB gives the least CA at 60.09%, while the InceptionV3 model yields the best CA, reaching an accuracy of 98.01%. Full article
(This article belongs to the Special Issue Sensors and Remote Sensing in Precision Horticulture)
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