Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricu...Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.展开更多
In order to improve the data transmission reliability of mobile ad hoc network, a routing scheme called integrated forward error correction multipath routing protocol was proposed, which integrates the techniques of p...In order to improve the data transmission reliability of mobile ad hoc network, a routing scheme called integrated forward error correction multipath routing protocol was proposed, which integrates the techniques of packet fragmenting and forward error correction encoding into multipath routing. The scheme works as follows: adding a certain redundancy into the original packets; fragmenting the resulting packets into exclusive blocks of the same size; encoding with the forward error correction technique, and then sending them to the destination node. When the receiving end receives a certain amount of information blocks, the original information will be recovered even with partial loss. The performance of the scheme was evaluated using OPNET modeler. The experimental results show that with the method the average transmission delay is decreased by 20% and the transmission reliability is increased by 30%.展开更多
基金This research was funded under EPSRC DTA studentship which is awarded to A.Z.for his PhD.Research Council(DTG EP/N509668/1 Eng).
文摘Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.
基金Projects(2003CB314802) supported by the State Key Fundamental Research and Development Programof China project(90104001) supported by the National Natural Science Foundation of China
文摘In order to improve the data transmission reliability of mobile ad hoc network, a routing scheme called integrated forward error correction multipath routing protocol was proposed, which integrates the techniques of packet fragmenting and forward error correction encoding into multipath routing. The scheme works as follows: adding a certain redundancy into the original packets; fragmenting the resulting packets into exclusive blocks of the same size; encoding with the forward error correction technique, and then sending them to the destination node. When the receiving end receives a certain amount of information blocks, the original information will be recovered even with partial loss. The performance of the scheme was evaluated using OPNET modeler. The experimental results show that with the method the average transmission delay is decreased by 20% and the transmission reliability is increased by 30%.
